FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals (Sustainable Development Goals Series) [1st ed. 2023] 3031377753, 3031377761, 9783031377754, 9783031377761

Table of contents :
Preface
Acknowledgements
Contents
List of Figures
Part I Introduction and Background
1 FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals
Introduction
The General Outline of the Book
Chapter Summary
References
2 Exploring the Evolution of Artificial Intelligence and the Fourth Industrial Revolution an Overview
Introduction
The First Industrial Revolution
The Second Industrial Revolution
The Third Industrial Revolution
The Fourth Industrial Revolution
Technologies Driving the Fourth Industrial Revolution
Artificial Intelligence
A Brief History and Definition of AI
Deep Learning and Machine Learning
Deep Learning
Machine Learning (ML)
Chapter Summary
References
3 Financial Technology (FinTech) an Introduction
Introduction
Historical Evolution of Payment System
Barter Trade
Coins
Paper Money and Banknotes
Bills of Exchange and Cheques
Cards
Digital Payments
Real-Time Gross Settlement (RTGS)
What Is FinTech?
Elements of FinTech
Building Block Technologies for FinTech
General FinTech Domains
Digital Payments/Electronic Payment
Digital Capital Raising
Digital Lending
Digital Savings
Wealth Management and Investment
Insurance
Digital Currency—Cryptocurrencies and Central Bank Digital Currency (CBDC)
Digital Asset and Consensus Services
Open Application Programming Interfaces (API)
Distributed Ledger Technology (DLT)
QR Codes
Biometric Payments
History of FinTech
FinTech 1.0 (1886–1967)
FinTech 2.0 (1967–2008)
FinTech 3.0 (2008–2014)
FinTech 3.5 (2014–2017)
The Focus of FinTech 3.5 Is on Internationalization
FinTech 4.0 (2018–today)
FinTech Today
Chapter Summary
References
4 A Historical Perspective on Sustainable Development and the Sustainable Development Goals
Introduction
History Behind Sustainable Development
The Emergence of the Concept
Sustainable Development Goals
Nations and Selected SDGs Targets
Reduce Child Mortality
Universal and Equitable Access to Modern Energy Services—Electricity Access
Access to Clean Fuels for Cooking and Heating
Improved Water Access in 2020
Access to Sanitation
Chapter Summary
References
Part II Advancing the Sustainable Development Goals (SDGS) with FinTech and Artificial Intelligence
5 FinTech and Artificial Intelligence in Addressing Poverty, Towards Sustainable Development
Introduction
Definition of Important Terms
Poverty
FinTech
Artificial Intelligence (AI)
Poverty in the World
FinTech and Poverty Alleviation
FinTech and Access to Credit by the Poor and Vulnerable
Improvement in the Availability of Information
Social Networks Expansion
Promotion of Entrepreneurial Activities
AI Reducing Poverty and Boosting Shared Prosperity: Identifying the Development Opportunities of AI
AI Productivity and Poverty Reduction
AI and Access to Essential Services by the Poor and Vulnerable
AI Identifying and Targeting Poverty
AI Improving Access to Education
AI and Digital Financial Inclusion and Agriculture
Chapter Summary
References
6 The Role of FinTech and AI in Agriculture, Towards Eradicating Hunger and Ensuring Food Security
Introduction
FinTech and Artificial Intelligence on Food Security, Important Literature
FinTech
Artificial Intelligence
Food Security
FinTech and Food Security and Hunger
Financial Inclusion and Food Security
FinTech and Efficiency in Agricultural Supply Chains
FinTech and Insurance for Smallholder Farmers
Food Distribution and Transparency in Food Assistance Programmes
AI Food Security and Hunger
Crop Forecasting
Crop Yield Prediction
Food Safety and Traceability
Food Waste Reduction
Artificial Intelligence and Satellite Imagery
Real-Time Data for Better Agricultural Decisions
Precision Agriculture
Chapter Summary
References
7 Financial Technology, Artificial Intelligence, and the Health Sector, Lessons We Are Learning on Good Health and Well-Being
Introduction
FinTech and Healthcare
FinTech and Healthcare Literature
FinTech in the Healthcare Sector
Artificial Intelligence in the Health Sector
The Role of AI and Machine Learning in Overcoming Health Challenges
Accelerating Complications of COVID-19 Research and Treatment
AI, Forecasting, and Customer Communications Scaling
Identification, Control, and Monitoring
AI and How to Understand Spread, therapies, and cures for Diseases
Fourth Industrial and Sustainable Development Goals and AI Lessons
Conclusion and Policy Recommendation
References
8 Financial Technology, Digital Transformation, and Quality Education in the Fourth Industrial Revolution
Introduction
The Fourth Industrial Revolution and Education
Industry 4.0, Education and Digital Transformation
Industry 4.0 in Education
Digital Transformation in Education
Key Areas of Digital Transformation in Education
The Main Focuses of Digital Transformation in Education Are Shown in Fig. 8.3
Financial Technology, Digital Transformation, and Quality Education in the Fourth Industrial Revolution
FinTech and Student Loan-Based Ed FinTech
FinTech and Education Management
FinTech and Online Learning
Personalized Learning
Career Advancement
Chapter Summary
References
9 Artificial Intelligence and Machine Learning in Making Transport, Safer, Cleaner, More Reliable, and Efficient in Emerging Markets
Introduction
AI Models in the Transport Sector
Artificial Immune System
Fuzzy Logic Model
Ant Colony Optimizer
Bee Colony Optimization
A Window of Opportunity for the Use of Artificial Intelligence in Transportation
The Application of Artificial Intelligence and Machine Learning in Making Transport, Safer, Cleaner, More Reliable, and Efficient in Emerging Markets
Utilizing AI for Planning, Designing, and Controlling the Structures of Transportation Networks
The Detection of Incidents
System for the Management of Hazards and Emergencies
AI and Autonomous Vehicles
Smart Parking Management
Limitations Risks and Challenges of AI and ML in Making Transport Safe and Reliable
Summary
References
10 FinTech and Climate-Related Challenges in the Fourth Industrial Revolution
Introduction
The Fourth Industrial Revolution
Financial Inclusion
Financial Technology (FinTech)
What Is Climate Change?
Empirical Literature on the Role of Financial Inclusion
The Role of FinTech for Financial Inclusion in Addressing Climate-Related Challenges in the Fourth Industrial Revolution
Financial Services as a Strategy for Resilience Building
Channels Through Which Financial Inclusion Can Help to Address Climate-Related Challenges
The Poor’s Ability to Save Can Help Them Overcome the Problems Posed by Climate Change
Climate Change and Credit Access
Insurance as a Tool for Dealing with Climate Change Issues
Financial Services as a Means of Increasing Clean Accessibility and Adoption of Clean Technology
Conclusion/Recommendations
References
11 Artificial Intelligence and Machine Learning in the Power Sector
Introduction
Machine Learning (ML)
AI Towards a Smart Power Sector
The General Access to Electricity
The Role of AI and ML for Energy Production and Consumption
Predictive Maintenance of Turbines and Optimize Energy Consumption
Management of the Grid and the Ability to Accurately Predict Energy Prices
AI and ML to Correctly Determine Energy Demand and Energy Efficiency in Homes
Recommendations for Emerging Markets to Maximize AI and ML in Energy
Looking to the Future
Conclusion and Recommendations
References
12 Block Chain for Digital Financial Inclusion Towards Reduced Inequalities
Introduction
What Is Block Chain Technology?
Digital Financial Inclusion
Industry 4.0
Sustainable Development
Empirical Literature Review
Results and Discussion
Strategies for Broadening Access to Financial Services Using Blockchain
Utilize the Potential of Blockchain Technology in Financial Transactions
Blockchain Technology as a Tool for Boosting Financial Savings
The Application of Blockchain Technology to the Provision of Credit
Utilization of Block Chain Technology in the Process of Providing Insurance
Blockchain Technology, Digital Financial Inclusion, Towards Sustainable Development
Conclusion and Recommendation
References
13 The Power of Smart Technologies for Global Partnership for Development
Introduction
Sustainable Development Goals
Sustainable Development Goal 17
Technology and Partnership for Goals
Inclusive by Design Online
Smart Technologies and Their Contribution Towards Global Partnership for Development
The Fourth Industrial Revolution
Artificial Intelligence (AI)
Machine Learning
Internet of Things
Block Chain Technology
Augmented Reality (AR)
Advanced Robotics
Chapter Summary
References
14 FinTech and Financial Inclusion: Application of AI to the Problem of Financial Exclusion What Are the Challenges
Introduction
Innovation in Artificial Intelligence for the Financial Sector
Applications of Artificial Intelligence in the Financial Market, Including Case Studies
The Implications That Artificial Intelligence Will Have on Financial Inclusion
A Discussion of the Difficulties of Implementing Artificial Intelligence (AI) Solutions for Excluding People’s Financial Inclusion
Data Protection and Online Attacks as Obstacles on the Path of Consumer Protection
Problems Arise from the Lessening of Competition and the Irresponsible Deployment of Artificial Intelligence
The Problem of Providing Fuel for the Digital Divide, Exclusion, and Displacements
Conclusion and Policy Recommendation
References
15 FinTech for Sustainable Development in Emerging Markets with Case Studies
Introduction
FinTech Literature in General
Financial Technology Scope in Emerging Markets
The FinTech Disruption
Use of Alternative Data
Peer-to-Peer Transactions
Emergence of Non-traditional Players
Technology Companies
Mobile Network Operators
Cash Networks
E-Retailers
FinTech in India
Paytm Case Study
FinTech in China
Lufax FinTech in China
The Benefits of FinTech for Sustainable Development in Emerging Markets
Increased Access to Financial Services and Financial Inclusion
Enhanced Financial Security
Increased Efficiency and Reduction in Costs
Boosting the Economy
Chapter Summary
References
16 Artificial Intelligence and Machine Learning for Sustainable Development Case Studies in Emerging Markets
Introduction
General Investment in Artificial Intelligence
AI Sectorial Investment Analysis
Artificial Intelligence in Energy
Artificial Intelligence in Healthcare
Artificial Intelligence in Education
Artificial Intelligence in Manufacturing
AI in Financial Services
Artificial Intelligence in Transport
The Role of Artificial Intelligence in Agriculture
Case Studies
Anti-Theft Technology in Brazil
FarmDrive
Branch Mobile Application
Aadhar Housing Finance Ltd
Aavas Financiers Ltd.
Chapter Summary
References
17 Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning
Introduction
A Brief History of OpenAI and ChatGTP
ChatGPT the Meaning
Empirical Literature Review
Use of ChatGPT for Education: Challenges
Use of ChatGPT for Education: Opportunities
Responsible and Ethical Use of ChatGPT in Education
Respect for Privacy
Fairness and Non-Discrimination
ChatGpt is not a Substitute for Human Teachers
ChatGPT is not Capable of Comprehending the Surrounding Context
Responsible AI, It’s Important to Educate Students About AI and Its Limitations
Transparency in the Use of ChatGPT
Accuracy of Information
Conclusion and Policy Recommendations
References
Part III Conclusion and Policy Recommendations
18 Conclusion on FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals
Introduction
The General Outline of the Book
Key Recommendations for FinTech and Artificial Intelligence for Sustainable Development
Summary of the Policy Recommendations for FinTech and Artificial Intelligence for Sustainable Development
Conclusion
References
Index

Citation preview

Sustainable Development Goals Series

Connecting the Goals

FinTech and Artificial Intelligence for Sustainable Development The Role of Smart Technologies in Achieving Development Goals David Mhlanga

Sustainable Development Goals Series

The Sustainable Development Goals Series is Springer Nature’s inaugural cross-imprint book series that addresses and supports the United Nations’ seventeen Sustainable Development Goals. The series fosters comprehensive research focused on these global targets and endeavours to address some of society’s greatest grand challenges. The SDGs are inherently multidisciplinary, and they bring people working across different fields together and working towards a common goal. In this spirit, the Sustainable Development Goals series is the first at Springer Nature to publish books under both the Springer and Palgrave Macmillan imprints, bringing the strengths of our imprints together. The Sustainable Development Goals Series is organized into eighteen subseries: one subseries based around each of the seventeen respective Sustainable Development Goals, and an eighteenth subseries, “Connecting the Goals,” which serves as a home for volumes addressing multiple goals or studying the SDGs as a whole. Each subseries is guided by an expert Subseries Advisor with years or decades of experience studying and addressing core components of their respective Goal. The SDG Series has a remit as broad as the SDGs themselves, and contributions are welcome from scientists, academics, policymakers, and researchers working in fields related to any of the seventeen goals. If you are interested in contributing a monograph or curated volume to the series, please contact the Publishers: Zachary Romano [Springer; zachary. [email protected]] and Rachael Ballard [Palgrave Macmillan; rachael. [email protected]].

David Mhlanga

FinTech and Artificial Intelligence for Sustainable Development The Role of Smart Technologies in Achieving Development Goals

David Mhlanga College of Business and Economics University of Johannesburg Johannesburg, South Africa

ISSN 2523-3084 ISSN 2523-3092 (electronic) Sustainable Development Goals Series ISBN 978-3-031-37775-4 ISBN 978-3-031-37776-1 (eBook) https://doi.org/10.1007/978-3-031-37776-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Color wheel and icons: From https://www.un.org/sustainabledevelopment/, Copyright © 2020 United Nations. Used with the permission of the United Nations. The content of this publication has not been approved by the United Nations and does not reflect the views of the United Nations or its officials or Member States. This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: Barbara Boensch/Alamy Stock Photo This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

I would like to dedicate this book to my mother and father.

Preface

The world is currently confronted with tremendous challenges that cast doubt on the viability of our planet and the welfare of the people who live on it. In 2015, the United Nations General Assembly adopted the Sustainable Development Goals (SDGs) as a universal call to action to end poverty, protect the planet, and ensure that all people enjoy peace and prosperity by the year 2030. These goals were established as part of the 2030 Agenda for Sustainable Development. But even though we have made some headway towards accomplishing these objectives, there is still a significant amount of work to be done, and the clock is ticking. Concurrently, we are witnesses to a technological revolution that is transforming the way we live, work, and communicate with one another. This revolution is affecting all aspects of our lives. The emergence of innovative technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain, which are altering the way we think about finance, commerce, and governance, are hallmarks of the Fourth Industrial Revolution. The potential of financial technology (FinTech) and Artificial Intelligence (AI) to contribute to sustainable development is investigated in this book. The book focuses on the intersection of these two trends. We can recognize patterns and trends that can help inform policy decisions and contribute to the achievement of Sustainable Development Goals (SDGs) by leveraging the power of big data and machine

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PREFACE

learning. The book examines the various ways in which FinTech and AI can be used to support economic growth, improve access to finance, and foster innovation. The ultimate objective of this work is to encourage decision-makers, business owners, and academics to recognize the potential of artificial intelligence and financial technology to contribute to sustainable development. We can accelerate progress towards the Sustainable Development Goals (SDGs) and create a world that is more prosperous and equitable for all of us if we work together and utilize the power of technology. Johannesburg, South Africa

David Mhlanga

Acknowledgements

Without the generous support of many different people and organizations, the publication of this work would not have been feasible. I would like to take this opportunity to extend my appreciation to everyone who has helped me out and encouraged me while working on this endeavour. I would like to begin by extending my gratitude to the United Nations for their role in laying the groundwork and serving as a source of motivation for this book by way of their Sustainable Development Goals. Throughout the entire process of publication, my publisher and the editorial team provided invaluable direction, support, and tolerance. I cannot express how grateful I am for all their hard work. One more time, I would like to offer my sincere appreciation to my family and friends for the love, support, and understanding they have shown me throughout this difficult time. Your support has been extremely helpful in allowing us to continue our interest in environmentally responsible building and design. To conclude, we would like to express our gratitude to the readers of this work for their attention and participation. It is my sincere wish that reading this book will motivate and educate you in your quest to advance sustainable development through the application of advances in artificial intelligence and financial technology.

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Contents

Part I Introduction and Background 1

2

FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals Introduction The General Outline of the Book Chapter Summary References

3 3 5 11 12

Exploring the Evolution of Artificial Intelligence and the Fourth Industrial Revolution an Overview Introduction The First Industrial Revolution The Second Industrial Revolution The Third Industrial Revolution The Fourth Industrial Revolution Technologies Driving the Fourth Industrial Revolution Artificial Intelligence A Brief History and Definition of AI Deep Learning and Machine Learning Deep Learning Chapter Summary References

15 15 16 19 20 23 25 26 27 32 32 37 37

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CONTENTS

3

Financial Technology (FinTech) an Introduction Introduction Historical Evolution of Payment System Barter Trade Coins Paper Money and Banknotes Bills of Exchange and Cheques Cards Digital Payments Real-Time Gross Settlement (RTGS) What Is FinTech? Elements of FinTech Building Block Technologies for FinTech General FinTech Domains Digital Payments/Electronic Payment Digital Capital Raising Digital Lending Digital Savings Wealth Management and Investment Insurance Digital Currency—Cryptocurrencies and Central Bank Digital Currency (CBDC) Digital Asset and Consensus Services Open Application Programming Interfaces (API) Distributed Ledger Technology (DLT) QR Codes Biometric Payments History of FinTech FinTech 1.0 (1886–1967) FinTech 2.0 (1967–2008) FinTech 3.0 (2008–2014) FinTech 3.5 (2014–2017) FinTech 4.0 (2018–today) FinTech Today Chapter Summary References

41 41 43 44 44 44 45 45 45 46 46 47 47 47 48 49 51 52 52 54 55 55 56 56 56 57 57 58 59 61 61 62 63 63 64

CONTENTS

4

A Historical Perspective on Sustainable Development and the Sustainable Development Goals Introduction History Behind Sustainable Development The Emergence of the Concept Sustainable Development Goals Nations and Selected SDGs Targets Reduce Child Mortality Universal and Equitable Access to Modern Energy Services—Electricity Access Access to Clean Fuels for Cooking and Heating Improved Water Access in 2020 Access to Sanitation Chapter Summary References

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67 67 69 71 76 79 79 80 81 82 83 84 85

Part II Advancing the Sustainable Development Goals (SDGS) with FinTech and Artificial Intelligence 5

FinTech and Artificial Intelligence in Addressing Poverty, Towards Sustainable Development Introduction Definition of Important Terms Poverty FinTech Artificial Intelligence (AI) Poverty in the World FinTech and Poverty Alleviation FinTech and Access to Credit by the Poor and Vulnerable Improvement in the Availability of Information Social Networks Expansion Promotion of Entrepreneurial Activities AI Reducing Poverty and Boosting Shared Prosperity: Identifying the Development Opportunities of AI AI Productivity and Poverty Reduction AI and Access to Essential Services by the Poor and Vulnerable AI Identifying and Targeting Poverty

89 89 92 92 94 95 95 97 99 99 101 101 102 104 105 107

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CONTENTS

AI Improving Access to Education AI and Digital Financial Inclusion and Agriculture Chapter Summary References 6

7

The Role of FinTech and AI in Agriculture, Towards Eradicating Hunger and Ensuring Food Security Introduction FinTech and Artificial Intelligence on Food Security, Important Literature FinTech Artificial Intelligence Food Security FinTech and Food Security and Hunger Financial Inclusion and Food Security FinTech and Efficiency in Agricultural Supply Chains FinTech and Insurance for Smallholder Farmers Food Distribution and Transparency in Food Assistance Programmes AI Food Security and Hunger Crop Forecasting Crop Yield Prediction Food Safety and Traceability Food Waste Reduction Artificial Intelligence and Satellite Imagery Real-Time Data for Better Agricultural Decisions Precision Agriculture Chapter Summary References Financial Technology, Artificial Intelligence, and the Health Sector, Lessons We Are Learning on Good Health and Well-Being Introduction FinTech and Healthcare FinTech and Healthcare Literature FinTech in the Healthcare Sector Artificial Intelligence in the Health Sector The Role of AI and Machine Learning in Overcoming Health Challenges

109 111 114 115 119 119 121 121 121 122 124 124 125 126 127 128 130 131 132 134 136 137 137 140 141

145 145 147 149 151 153 156

CONTENTS

Accelerating Complications of COVID-19 Research and Treatment AI, Forecasting, and Customer Communications Scaling Identification, Control, and Monitoring AI and How to Understand Spread, therapies, and cures for Diseases Fourth Industrial and Sustainable Development Goals and AI Lessons Conclusion and Policy Recommendation References 8

9

Financial Technology, Digital Transformation, and Quality Education in the Fourth Industrial Revolution Introduction The Fourth Industrial Revolution and Education Industry 4.0, Education and Digital Transformation Industry 4.0 in Education Digital Transformation in Education Key Areas of Digital Transformation in Education The Main Focuses of Digital Transformation in Education Are Shown in Fig. 8.3 Financial Technology, Digital Transformation, and Quality Education in the Fourth Industrial Revolution FinTech and Student Loan-Based Ed FinTech FinTech and Education Management FinTech and Online Learning Personalized Learning Career Advancement Chapter Summary References Artificial Intelligence and Machine Learning in Making Transport, Safer, Cleaner, More Reliable, and Efficient in Emerging Markets Introduction AI Models in the Transport Sector Artificial Immune System Fuzzy Logic Model Ant Colony Optimizer

xv

157 159 160 161 162 165 167

171 171 174 176 177 181 182 183 184 184 186 187 188 189 189 190

193 193 195 196 196 197

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CONTENTS

Bee Colony Optimization A Window of Opportunity for the Use of Artificial Intelligence in Transportation The Application of Artificial Intelligence and Machine Learning in Making Transport, Safer, Cleaner, More Reliable, and Efficient in Emerging Markets Utilizing AI for Planning, Designing, and Controlling the Structures of Transportation Networks The Detection of Incidents System for the Management of Hazards and Emergencies AI and Autonomous Vehicles Smart Parking Management Limitations Risks and Challenges of AI and ML in Making Transport Safe and Reliable Summary References 10

FinTech and Climate-Related Challenges in the Fourth Industrial Revolution Introduction The Fourth Industrial Revolution Financial Inclusion Financial Technology (FinTech) What Is Climate Change? Empirical Literature on the Role of Financial Inclusion The Role of FinTech for Financial Inclusion in Addressing Climate-Related Challenges in the Fourth Industrial Revolution Financial Services as a Strategy for Resilience Building Channels Through Which Financial Inclusion Can Help to Address Climate-Related Challenges The Poor’s Ability to Save Can Help Them Overcome the Problems Posed by Climate Change Climate Change and Credit Access Insurance as a Tool for Dealing with Climate Change Issues Financial Services as a Means of Increasing Clean Accessibility and Adoption of Clean Technology Conclusion/Recommendations References

197 197

200 200 202 203 205 206 208 210 210 213 213 217 218 219 220 221

226 226 227 228 229 231 232 234 234

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11

12

Artificial Intelligence and Machine Learning in the Power Sector Introduction Machine Learning (ML) AI Towards a Smart Power Sector The General Access to Electricity The Role of AI and ML for Energy Production and Consumption Predictive Maintenance of Turbines and Optimize Energy Consumption Management of the Grid and the Ability to Accurately Predict Energy Prices AI and ML to Correctly Determine Energy Demand and Energy Efficiency in Homes Recommendations for Emerging Markets to Maximize AI and ML in Energy Looking to the Future Conclusion and Recommendations References Block Chain for Digital Financial Inclusion Towards Reduced Inequalities Introduction What Is Block Chain Technology? Digital Financial Inclusion Industry 4.0 Sustainable Development Empirical Literature Review Results and Discussion Strategies for Broadening Access to Financial Services Using Blockchain Utilize the Potential of Blockchain Technology in Financial Transactions Blockchain Technology as a Tool for Boosting Financial Savings The Application of Blockchain Technology to the Provision of Credit Utilization of Block Chain Technology in the Process of Providing Insurance

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241 241 244 246 248 250 250 253 254 256 257 259 260 263 263 266 271 272 272 273 277 278 279 281 282 284

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Blockchain Technology, Digital Financial Inclusion, Towards Sustainable Development Conclusion and Recommendation References 13

14

The Power of Smart Technologies for Global Partnership for Development Introduction Sustainable Development Goals Sustainable Development Goal 17 Technology and Partnership for Goals Inclusive by Design Online Smart Technologies and Their Contribution Towards Global Partnership for Development The Fourth Industrial Revolution Artificial Intelligence (AI) Machine Learning Internet of Things Block Chain Technology Augmented Reality (AR) Advanced Robotics Chapter Summary References FinTech and Financial Inclusion: Application of AI to the Problem of Financial Exclusion What Are the Challenges Introduction Innovation in Artificial Intelligence for the Financial Sector Applications of Artificial Intelligence in the Financial Market, Including Case Studies The Implications That Artificial Intelligence Will Have on Financial Inclusion A Discussion of the Difficulties of Implementing Artificial Intelligence (AI) Solutions for Excluding People’s Financial Inclusion Data Protection and Online Attacks as Obstacles on the Path of Consumer Protection Problems Arise from the Lessening of Competition and the Irresponsible Deployment of Artificial Intelligence

286 287 288 291 291 292 293 295 295 300 300 302 303 305 307 308 310 312 313

317 317 319 320 322

325 325 327

CONTENTS

The Problem of Providing Fuel for the Digital Divide, Exclusion, and Displacements Conclusion and Policy Recommendation References 15

16

FinTech for Sustainable Development in Emerging Markets with Case Studies Introduction FinTech Literature in General Financial Technology Scope in Emerging Markets The FinTech Disruption Use of Alternative Data Peer-to-Peer Transactions Emergence of Non-traditional Players Technology Companies Mobile Network Operators Cash Networks E-Retailers FinTech in India Paytm Case Study FinTech in China Lufax FinTech in China The Benefits of FinTech for Sustainable Development in Emerging Markets Increased Access to Financial Services and Financial Inclusion Enhanced Financial Security Increased Efficiency and Reduction in Costs Boosting the Economy Chapter Summary References Artificial Intelligence and Machine Learning for Sustainable Development Case Studies in Emerging Markets Introduction General Investment in Artificial Intelligence AI Sectorial Investment Analysis Artificial Intelligence in Energy Artificial Intelligence in Healthcare

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330 331 332 337 337 340 345 346 347 348 348 349 349 350 350 350 351 353 355 356 356 357 358 359 360 360

365 365 367 369 369 370

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Artificial Intelligence in Education Artificial Intelligence in Manufacturing AI in Financial Services Artificial Intelligence in Transport The Role of Artificial Intelligence in Agriculture Case Studies Anti-Theft Technology in Brazil FarmDrive Branch Mobile Application Aadhar Housing Finance Ltd Aavas Financiers Ltd. Chapter Summary References 17

Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning Introduction A Brief History of OpenAI and ChatGTP ChatGPT the Meaning Empirical Literature Review Use of ChatGPT for Education: Challenges Use of ChatGPT for Education: Opportunities Responsible and Ethical Use of ChatGPT in Education Respect for Privacy Fairness and Non-Discrimination ChatGpt is not a Substitute for Human Teachers ChatGPT is not Capable of Comprehending the Surrounding Context Responsible AI, It’s Important to Educate Students About AI and Its Limitations Transparency in the Use of ChatGPT Accuracy of Information Conclusion and Policy Recommendations References

372 373 374 375 376 377 377 379 380 380 381 383 383 387 387 389 390 391 396 396 397 397 399 400 401 402 403 405 405 406

Part III Conclusion and Policy Recommendations 18

Conclusion on FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals

413

CONTENTS

Introduction The General Outline of the Book Key Recommendations for FinTech and Artificial Intelligence for Sustainable Development Summary of the Policy Recommendations for FinTech and Artificial Intelligence for Sustainable Development Conclusion References Index

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413 416 418 421 422 423 425

List of Figures

Fig. 2.1 Fig. 2.2 Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

2.3 2.4 2.5 3.1 3.2 3.3 3.4 3.5 3.6 4.1

Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 5.1 Fig. 5.2 Fig. 6.1

Evolution of the industrial revolutions Technologies driving the fourth industrial revolution (Source Author’s analysis) Potential definitions of AI (Source Author’s analysis) Machine learning, deep learning, and artificial intelligence Categories of machine learning Historical evolution of the payment system Elements of FinTech Segments and elements Building block technologies for, FinTech General FinTech domains FinTech evolution Has the country already reached the SDGs target on child mortality in 2020 (Source Our World in Data) Has the country already reached the SDG target on electricity access in 2020 Has the country already reached the SDG target on clean cooking fuels in 2020? Has the country already reached the SDG target of improved water access in 2020? Has the country already reached the SDG target for access to sanitation in 2020? FinTech and poverty alleviation Channels in which AI addresses poverty Artificial Intelligence, FinTech, and Food Security

17 26 32 33 36 43 48 49 50 51 58 80 81 82 83 84 98 104 123

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LIST OF FIGURES

Fig. 6.2 Fig. 6.3 Fig. 6.4 Fig. 7.1 Fig. 7.2

Fig. 7.3 Fig. 7.4

Fig. 7.5 Fig. Fig. Fig. Fig. Fig.

8.1 8.2 8.3 9.1 9.2

Fig. 10.1 Fig. 10.2

Fig. 11.1 Fig. 11.2 Fig. 11.3 Fig. 12.1 Fig. 12.2 Fig. 12.3 Fig. Fig. Fig. Fig.

15.1 15.2 17.1 18.1

FinTech and Food Security and Hunger Ways FinTech can be used to increase transparency in food distribution AI Food Security and Hunger Health financing solutions Traditional drug development process (Source Author’s Analysis Important Information was taken from Mhlanga [2022a]) Targets of sustainable development goal 3 (Source Author’s analysis) The roles of AI The role of AI in healthcare that can assist in the attainment of SDG3 and its Targets in the post-Covid World Ethical and regulatory aspects of the application of AI in healthcare Education 4.0 (Source Author’s analysis) Project learning outcomes (Source Author’s analysis) Key areas of digital transformation in education Artificial neural networks Opportunities for the use of artificial intelligence in Transportation Technologies driving change in the Fourth Industrial Revolution (Source Author’s Analysis) Channels through which financial inclusion can help to address climate-related challenges (Source Author’s Analysis) Global access to electricity (Source Our World in Data [2020]) AI and ML for energy production Proposals for AI and ML to be effective for emerging markets The fundamental components of a blockchain Types of blockchain networks Strategies for broadening access to financial services using blockchain FinTech explosion Non-traditional players in the financial sector Responsible and ethical use of ChatGPT in education Key recommendations for FinTech and artificial intelligence for sustainable development

124 129 129 148

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PART I

Introduction and Background

CHAPTER 1

FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals

Introduction Growing awareness of the need for sustainable development that strikes a balance between economic, social, and environmental issues has been shown in recent years. The Sustainable Development Goals (SDGs), a series of 17 interrelated objectives with the triple objectives of eradicating poverty, fostering economic growth, and preserving the environment, was endorsed by the United Nations in 2015. Though progress has been gradual in accomplishing these objectives, more vigorous action is required. The World Economic Forum reports that our attempts to achieve the United Nations Global Goals for sustainable development by the year 2030 are lagging. Even while several of the Goals have made advancements since 2015 in a variety of areas, the global response has not been nearly as ambitious as it should have been (PWC, 2020; World Economic Forum, 2020, 2021). The globe is not on track to eradicate poverty by 2030, 785 million people still lack access to basic drinking water, and economic and inclusive growth targets for developing nations are not being realized, according to the most recent Sustainable Development Progress Report. Moreover, industrialization in many nations is not advancing quickly enough to meet the 2030 Agenda’s goals, especially

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1_1

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in technological fields (World Economic Forum, 2020). The research also showed that human attempts to stop climate change and preserve biodiversity have failed. This is because the global material footprint is expanding faster than both the people and the economy combined. Performance varies not just among goals but also inside each goal. When it comes to the proportion of women who have access to the Internet, OECD countries are well on their way to fulfilling Goal 5 on gender equality; but the same nations still lag far behind when it comes to the gender gap in unpaid employment. The ongoing crisis between Russia and Ukraine is the second thing that is posing a substantial barrier (Ben Hassen & El Bilali, 2022, Rawtani et al., 2022). The Fourth Industrial Revolution’s technologies, such as artificial intelligence, blockchain, and the Internet of Things, among others, are, on the other hand, quickly attracting public attention and making it feasible to change entire networks and systems, particularly in the financial industry (Agbehadji et al., 2021; Mhlanga, 2020, 2021, 2022). Governments, markets, businesses, and society have all undergone a great deal of upheaval recently. These fundamental changes are boosting the rate of market expansion while simultaneously expanding its overall size (Mhlanga, 2022, 2023). They have an impact on almost every industry and threaten both tried-and-true business models as well as completely creative ones made possible by 4IR. These technologies provide a wide range of opportunities, but there is a chance that they could strain global resources and our civilization even more. Because of this, we must ensure that these technologies are fully utilized possible to realize their potential to revolutionize our world, transform people’s lives, and open new doors to prosperity, thereby hastening the process of environmentally sustainable development on a global scale. Using emerging technology to deepen and widen the scope of the current action is one of the best ways to accelerate progress towards Global Goals. The 169 targets that help achieve the Goals may be strengthened by technological advancement, and more than half of those targets may be significantly impacted by the Fourth Industrial Revolution’s technologies. Maybe even more astounding is the ability of big data platforms and artificial intelligence (AI) to aid in the accomplishment of each Global Goal. The potential of FinTech and AI in attaining the SDGs is examined in the book “FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development

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Goals.” The book gives a thorough introduction to sustainable development, the SDGs, and the 4IR, and it describes possible options for achieving the SDGs. The book investigates the potential of smart technologies in achieving SDGs, including no poverty, zero hunger, good health and well-being, quality education, affordable and clean energy, reduced inequalities, sustainable cities and communities, climate action, and partnerships for the goals. For instance, the book looks at how FinTech could increase financial inclusion and finance accessibility, while platforms powered by AI and big data could help track and report on progress made towards the SDGs. The book also suggests novel approaches to the problems associated with sustainable development, such as the application of blockchain technology to increase accountability and transparency in the execution of SDG-related projects. The book also examines potential hazards and difficulties related to the adoption of smart technologies, such as the possibility of data privacy violations or the risk of escalating inequality. This book’s overall goal is to motivate governments, development organizations, and other stakeholders to use FinTech and AI’s transformative power to enhance sustainable development. The book aims to support worldwide efforts to achieve the SDGs by 2030 by offering insights, suggestions, and methods. The goal of the book is to assess how the Fourth Industrial Revolution’s technologies, more notably FinTech and AI, can contribute to the achievement of the Sustainable Development Goals using this brief introduction as a foundation.

The General Outline of the Book The eighteen chapters of the book FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals have three main themes. The background and introduction provide background information for the book’s key ideas. The second theme is concerned with how different Fourth Industrial Revolution technologies might help achieve the chosen Sustainable Development Goals. The last theme will talk about the tactics different stakeholders might use to achieve sustainable development goals in the context of the Fourth Industrial Revolution. FinTech and artificial intelligence are the main topics of this chapter’s “Smart Technologies for Sustainable Development: The Role of Smart Technologies in Achieving Development Objectives.” As demonstrated in this chapter, despite some of the UN’s Sustainable Development Goals making progress since 2015,

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the global response has not been nearly as forceful as it could have been to meet its 2030 deadline. The most recent Sustainable Development Progress Report estimates that 785 million people do not now have access to basic drinking water and that the economic and inclusive growth goals for developing nations are not being realized. Contrarily, the technologies of the Fourth Industrial Revolution, such as the Internet of Things, blockchain, and artificial intelligence, are quickly gaining popularity and creating new opportunities for network and system modification, particularly in the field of financial technology, also known as FinTech. One of the best ways to quicken progress towards Global Goals is to use emerging technology to broaden and deepen the existing course of action, especially by enhancing access to finance. What is perhaps even more amazing is how big data platforms and artificial intelligence (AI) can help achieve each Global Goal. This chapter also highlights the book’s objective. Chapter 2 details the Fourth Industrial Revolution and the development of artificial intelligence. The notion of the Fourth Industrial Revolution, commonly known as Industry 4.0, is dissected in this chapter by looking at its many components. The technology that serves as the foundation for the analysis of the book, artificial intelligence, receives particular attention. Industry 4.0 is characterized by advancements in artificial intelligence, robots, the Internet of Things, internet services, autonomous cars, 3D printing, nanotechnology, materials science, energy storage, and quantum computing. These advancements are articulated. The chapter is completed with a thorough explanation of artificial intelligence, which is defined as a collection of technologies working together to give robots the ability to sense, understand, act, and learn at a level of intellect comparable to humans. The introduction of FinTech, or financial technology, was the main goal of Chapter 3. FinTech involves digitizing traditional financial services provided by banks, credit unions, investment banks, credit card firms, and other enterprises in the financial industry, as demonstrated in Chapter three. As stated in Chapter 3, FinTech technology is quickly replacing the established financial system. It is disruptive because it creates a variety of new financial firms, each with its self-sustaining ecosystem. FinTech is recognized as a stimulus for long-term economic growth and development, and Chapter 3 underlines that it is a novel sector with distinctive traits that separate it apart from the existing financial industry. Because of this, there is a lot of excitement about the future of FinTech,

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and investors from all over the world have made significant investments in this industry. The historical context for sustainable development and the sustainable development goals are presented in Chapter 4, which came in fourth. Development that serves the demands of the present generation without endangering the capacity of future generations to meet their own needs is described as sustainable development in Chapter 4. Once more, Chapter 4 makes clear that the idea of sustainable development is based on two main tenets: the importance of meeting the basic needs of the underprivileged and the understanding that the environment’s capacity to meet present and future needs is constrained by current social structures and technological advancements. Chapter 5’s goal is to examine how financial technology and artificial intelligence (AI) help to achieve the Sustainable Development Goals (SDGs), with an emphasis on those goals specifically lowering poverty. Chapter 5 demonstrates that because poverty is one of the most important global issues, reducing it is essential for promoting economic growth and expanding possibilities for people in developing economies. Chapter 6 looks at the critical roles that FinTech and AI are playing in the agriculture sector to ensure food security. The chapter focuses on how artificial intelligence (AI) and financial technology are essential for securing food security and eradicating hunger. The agricultural industry has been significantly impacted by several significant changes brought about by the ongoing advancements in computer science and finance technology. Since just a little amount of additional land would be available for farming in 2050, when the world’s population is expected to increase from 7.5 billion to 9.7 billion, it was demonstrated that agriculture is an essential activity in many nations. The chapter demonstrates how FinTech businesses can leverage technology to offer effective and easily accessible financial services, addressing some of the underlying causes of food insecurity. On the other side, the chapter demonstrates how AI is significantly influencing precision agriculture, which enhances harvest quality and accuracy. Detecting plant illnesses, eliminating pests, and enhancing agricultural nutrition are all made possible by AI technology. Weeds can also be discovered and targeted with the aid of AI sensors, which then determine the best herbicide to use in that area. Chapter 7 examined financial technology, artificial intelligence, and the health industry, looking for lessons on well-being and good health. The use of financial

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technology (FinTech) and artificial intelligence (AI) in the medical profession will have a significant impact on our ability to maintain or improve our health and quality of life, as demonstrated in Chapter 7. In Chapter 7, the intersection of financial technology, AI, and healthcare is examined, along with the lessons that may be learned from such a situation. This chapter discusses how using FinTech and AI might help the healthcare sector overcome challenges like expanding access to care, bringing down prices, and enhancing patient outcomes. Also, it explores any hazards that can be associated with using these technologies, such as issues with user data security and privacy. The chapter offers suggestions for the best ways to use these technologies to improve one’s health and well-being and highlights the most important lessons discovered via the application of FinTech and AI in the healthcare industry. In conclusion, this chapter provides important insights into how FinTech and AI have the potential to disrupt the health business as well as the relevance of using these technologies ethically to ensure favourable health results for everyone. Chapter 8 also discusses the Fourth Industrial Revolution’s impact on quality education, digital transformation, and financial technology. Considering the Fourth Industrial Revolution, this chapter explores the relationship between financial technology (FinTech) and high-quality instruction. The Fourth Industrial Revolution has been shown to have disrupted conventional teaching and learning techniques, with FinTech emerging as a key driver of this transformation. FinTech has been shown to have the ability to increase access to high-quality education, improve educational outcomes, and increase financial literacy abilities. The chapter did highlight some of the difficulties that come with implementing FinTech in education, such as the need for a sufficient technological foundation, digital literacy, and financial resources. This chapter analyses the opportunities and challenges presented by FinTech in education, paying particular attention to how to guarantee that all students can profit from these innovations. It does this through a thorough review of the existing literature and case studies. The chapter also covers how FinTech will affect education in the future and what abilities students will need to succeed in the twenty-first century. This chapter concludes by arguing that FinTech has the potential to transform education and that policymakers, academics, and financial institutions must collaborate to realize this potential and address the obstacles to guarantee that all students have access to high-quality education in the digital age. Chapter 9 also discusses the use of artificial intelligence and machine

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learning to improve the reliability, efficiency, and safety of transportation in emerging markets. The chapter discusses how machine learning (ML) and artificial intelligence (AI) applications have attracted a lot of attention in the transportation industry recently, with an emphasis on enhancing sustainability, efficiency, and safety. The goal of this chapter is to examine how AI and ML could improve transportation in developing nations by making it safer, cleaner, more dependable, and more effective. Developing economies suffer several transportation-related difficulties, such as insufficient infrastructure, high accident rates, ineffective transportation systems, and environmental issues. This chapter looks at how AI and ML can provide intelligent solutions to these problems, such as better transportation planning, traffic flow optimization, and increased system stability and safety. The utilization of real-time traffic analysis, predictive maintenance, and driver monitoring systems are just a few examples of the case studies this paper covers of effective AI and ML deployments in emerging markets. The chapter’s discussion of the advantages and drawbacks of applying AI and ML to transportation in developing economies, as well as potential future research topics, come to an end. The Fourth Industrial Revolution’s challenges linked to climate change and FinTech are examined in Chapter 10. This chapter’s goal is to examine how financial technology (FinTech) might help mitigate the risks and difficulties related to climate change in the context of the Fourth Industrial Revolution. According to the chapter, there is a growing demand for financial resources from both individuals and enterprises, which has prompted the creation of several financial instruments to meet this need, including microloans, insurance, and cash transfers. A rising number of development partners are beginning to promote the use of these tools to address climate-related concerns and natural disaster risks. In the chapter, it is demonstrated that the growing number of risks and challenges related to climate change has increased the urgency of assessing the effectiveness of financial instruments in addressing those challenges. Again, the chapter made clear that the idea of financial inclusion, which aims to give access to financial services to underserved and marginalized populations, has gained significance in the context of climate change as these populations are frequently the most vulnerable to its effects. In Chapter 11, the impact of AI and machine learning on the power sector is examined. In the chapter, it is emphasized that there are a growing number of problems that the global energy sector must deal with, including those brought on by rising

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consumption, rising efficiency, shifting patterns of supply and demand, and a lack of analytics necessary for effective management. In countries that are emerging as market leaders, these challenges are felt more keenly. Again, it was demonstrated that the prevalence of informal connections to the electricity grid results in a significant amount of power that is neither measured nor billed, causing losses as well as an increase in CO2 emissions because consumers lack incentives to use energy sensibly when they don’t have to pay for it. Because of the abundance of informal links, efficiency issues are particularly difficult. The chapter also revealed that smart grids, smart metres, and other Internet of Things devices have already begun to connect in developed nations’ electrical industries. This is due to the implementation of artificial intelligence and other related technologies. Also, it is noted that machine learning and artificial intelligence have the potential to help increase the use of renewable energy sources and improve power management, efficiency, and transparency. Chapter 12 examines the significance of blockchain for reducing inequality through digital financial inclusion. This chapter’s goals are to examine how blockchain technology has helped formerly marginalized people participate in traditional financial systems and to offer commentary on the most crucial lessons that sustainable development has taught us and the advantages that it has created. The chapter emphasized how the use of blockchain technology holds out a great deal of promise for both the institutionalization of money transfers and the expansion of access to financial services. The chapter also mentioned how blockchain technology has shown promise for integrating previously underserved people and communities into established financial systems, in addition to enhancing financial services. This has important ramifications for sustainable development because financial inclusion is essential to attaining economic growth and eradicating poverty. One of the most crucial lessons that sustainable development has imparted to us is the significance of addressing inequality and encouraging social and economic inclusion. By enabling safe and open financial transactions for all users, regardless of their location, background, or socioeconomic status, blockchain technology can play a crucial role in helping to realize this objective. In Chapter 13, the influence of smart technologies on international cooperation for development is examined. In this chapter, we’ll examine how technology might be able to aid in the creation of beneficial alliances for the sake of growth. Multi-stakeholder

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global cooperation for development is the means of advancing development. For the benefit of people, the earth, prosperity, and peace, partnership for development enables cooperation for development. To ensure that the objectives of sustainable development are met, the global partnership for development can bring together a wide range of stakeholders, including representatives from governments, bilateral and multilateral organizations, civil society, the private sector, and representatives from parliamentary and labour unions. Chapter 14 examines the difficulties in applying artificial intelligence (AI) methods to the issue of financial exclusion. This chapter’s goal was to provide a thorough analysis of the challenges associated with putting AI-based solutions into practice, particularly for those who are currently shut out of the financial system. Additionally, it is demonstrated that using artificial intelligence (AI) to increase financial inclusion using digital technologies is thought to be connected to numerous challenges in addition to the many advantages it offers. Although there are many benefits, using AI to include previously excluded families is not without its challenges. These challenges include issues with data protection, consumer protection, and cyberattacks. A decrease in competition, the reckless application of artificial intelligence, and the possibility of fostering the digital divide, exclusion, and displacements are additional worries. Chapter 15 presented case studies of how the inclusion of digital financial services is helping to address development issues in emerging economies. Case studies in emerging markets for AI and machine learning for sustainable development are covered in Chapter 16. In addition to presenting a comprehensive examination of the acceptable and ethical utilization of ChatGPT in educational settings geared towards lifelong learning, the purpose of Chapter 17 is to promote additional research and debate on this significant topic. Chapter 18 presented real-world case studies of how artificial intelligence and machine learning are assisting in addressing changes in emerging economies around the world. The book’s conclusion was provided in Chapter 18.

Chapter Summary The chapter sought to give a general outline of the book “FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals”. Since the adoption of the United Nations’ Sustainable Development Goals (SDGs) in 2015,

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progress towards achieving these goals has been slow, and the global response has not been as aggressive as it could have been. According to the latest Sustainable Development Progress Report, over 785 million people worldwide still lack access to basic drinking water, and the economic and inclusive growth targets set for developing countries have not been met. However, the emergence of the Fourth Industrial Revolution (4IR) technologies, such as the Internet of Things, blockchain, and artificial intelligence, has opened new opportunities for network and system modification, particularly in the financial technology (FinTech) industry. Leveraging these emerging technologies to deepen and widen the scope of current actions, particularly in improving access to finance, could be one of the most effective ways to accelerate progress towards achieving the SDGs. Furthermore, big data platforms and artificial intelligence have the potential to aid in achieving each of the SDGs. With this in mind, a book with 18 chapters has been written to examine how financial technology and 4IR technologies particularly FinTech and Artificial Intelligence among others can contribute to achieving the SDGs, specifically SDG1 (No Poverty), SDG2 (Zero Hunger), SDG3 (Good Health and Well-being), SDG4 (Quality Education), SDG7 (Affordable and Clean Energy), SDG10 (Reduced Inequalities), SDG11 (Sustainable Cities and Communities), SDG13 (Climate Action), and SDG17 (Partnerships for the Goals) The book not only provides background information on sustainable development, sustainable development goals, and the Fourth Industrial Revolution but also suggests strategies that can assist in achieving the SDGs. By exploring the potential of FinTech and 4IR technologies, the book aims to inspire innovative solutions to the challenges of sustainable development and encourage more aggressive action towards achieving the SDGs by 2030.

References Agbehadji, I. E., Awuzie, B. O., & Ngowi, A. B. (2021). COVID-19 pandemic waves: 4IR technology utilisation in the multi-sector economy. Sustainability, 13(18), 10168. Ben Hassen, T., & El Bilali, H. (2022). Impacts of the Russia-Ukraine war on global food security: Towards more sustainable and resilient food systems. Foods, 11(15), 2301.

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Mhlanga, D. (2020). Industry 4.0 in finance: The impact of artificial intelligence (AI) on digital financial inclusion. International Journal of Financial Studies, 8(3), 45. Mhlanga, D. (2021). Artificial intelligence in Industry 4.0, and its impact on poverty, Innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability, 13(11), 5788. Mhlanga, D. (2022). The role of artificial intelligence and machine learning amid the COVID-19 pandemic: What lessons are we learning on 4IR and the sustainable development goals. International Journal of Environmental Research and Public Health, 19(3), 1879. Mhlanga, D. (2023). Artificial intelligence and machine learning for energy consumption and production in emerging markets: A review. Energies, 16(2), 745. PWC. (2020). https://www.pwc.com/gx/en/services/sustainability/publicati ons/accelerating-sustainable-development.html Rawtani, D., Gupta, G., Khatri, N., Rao, P. K., & Hussain, C. M. (2022). Environmental damages due to war in Ukraine: A perspective. Science of the Total Environment, 850, 157932. World Economic Forum. (2020). Unlocking technology for the global goals. https://www3.weforum.org/docs/Unlocking_Technology_for_the_Global_ Goals.pdf World Economic Forum. (2021). Harnessing technology for the global goals: A framework for government action. https://www3.weforum.org/docs/WEF_ Harnessing_Technology_for_the_Global_Goals_2021.pdf

CHAPTER 2

Exploring the Evolution of Artificial Intelligence and the Fourth Industrial Revolution an Overview

Introduction Klaus Schwab, the founder, and executive chairman of the World Economic Forum is credited with coining the term “the fourth industrial revolution,” which refers to a world in which individuals move among digital domains and offline reality using attached technology that allows and manage their lives (Xu et al., 2018). The integration and connectedness of emerging technology domains, such as nanotechnology, biotechnology, new materials, and many others (Lavopa & Delera, 2021, Mhlanga, 2022a), such as 3D printing, human–machine interfaces, and artificial intelligence, are characteristics of the Fourth Industrial Revolution (4IR), which is also known as the “Industrial Revolution 4.0.” The Fourth Industrial Revolution is already altering the global industrial landscape by integrating new technologies into industrial production processes. This has given rise to the concept of Industry 4.0, also known as the Smart Factory, which is a factory that learns as it operates, continually adapting and optimizing its processes following the information it receives (Lavopa & Delera, 2021). It is commonly held that the rate and extent of the changes brought about by the 4IR cannot be ignored and that these changes will bring about shifts in power, shifts in wealth, and shifts in knowledge. This belief is shared by most people. Lavopa and Delera (2021) claimed that

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1_2

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the only way to ensure that gains in knowledge and technology reach all people and are beneficial to all of them is to be educated about the changes that are occurring and the rate at which they are occurring. What truly differentiates the technologies of Industry 4.0 from other similar developments is the novel way in which hardware, software, and interoperability are being retooled and integrated to achieve ever-more ambitious goals. Other distinguishing characteristics include the compilation and assessment of massive amounts of data, the smooth interplay among intelligent devices, and the obscuring of the physical and virtual dimensions of production (Lavopa & Delera, 2021). One of the possibilities includes sensors that might identify activities or process durations or environmental factors such as temperature, so enabling independent self-correction following discoveries made possible by “big data” and artificial intelligence (AI). These data points could also be fed via the system in real time to alter and optimize the subsequent stages of the manufacturing process, hence reducing downtime and freeing up resources (Lavopa & Delera, 2021, Mhlanga, 2021, 2022b). To truly grasp what is meant by the term “Fourth Industrial Revolution,” one must first have a solid foundational knowledge of the preceding three revolutions in the manufacturing sector: the first, the second, and the third. Figure 2.1 is a summary of the researchers’ consensus regarding the development of the Industrial Revolutions (Mhlanga, 2022b; Schwab, 2017). The evolution of the Industrial Revolutions is shown in Fig. 2.1 from the First Industrial Revolution, the Second Industrial Revolution, the Third Industrial Revolution and now the Fourth Industrial Revolution.

The First Industrial Revolution According to Mohajan (2019a), the First Industrial Revolution, often known as simply the Industrial Revolution, was a revolution that began in England in approximately 1760 and lasted until sometime between 1820 and 1840. Mohajan states that this revolution is also known as the Industrial Revolution. It marks one of the most significant inflexion moments in the course of human history. During this period, technologies that had previously relied on human and animal labour began to be replaced by machines. These technologies included steam power, spinning jenny, coke smelting, puddling, rolling processes for producing iron, etc. (Mhlanga, 2022b). The First Industrial Revolution is being revitalized because it contributed to the expansion of the global economy, a rise

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• 1760 • Steam, water, mechanical production equipment The First Industrial Revolution

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The Second Industrial Revolution • 1860 • Division of Labor, Electricity, Mass Production

• Electronics, Information Technology, Automated Production

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The Tird Industrial Revolution(1969)

Fig. 2.1 Evolution of the industrial revolutions

in the production and consumption of common people, and an improvement in the system of transportation communication via canals, roads, and trains. Also, banking, and other financial systems developed, making it easier to run industries and business firms smoothly. Child and infant mortality rates declined, but fertility rates soared, resulting in substantial shifts in population growth (Mhlanga, 2021). On the other hand, the number of children and women forced to labour in hazardous and unsanitary conditions has increased, and factory workers now have to put in sixteen hours of work each day just to keep their families from going hungry (Mohajan, 2019a). A large chasm opened up between the rich and the poor as a direct result of the First Industrial Revolution. This article tries to describe the myriad of repercussions that the Industrial Revolution had. At the tail end of the eighteenth century and the beginning of the nineteenth century (1760–1840), there were enormous socioeconomic changes in England that are collectively known as the Industrial Revolution. These changes began in England, which is the first country where industrially related productions got their start. The Industrial Revolution (IR) was the move from a technology that relied on human and animal labour to one that relied on machinery, new chemicals production and iron production methods, greater efficiency of waterpower, increased use

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of steam power, and the invention of machine tools. The iron and textile industries were particularly significant contributors to the initial phase of the Industrial Revolution. An English blacksmith named Thomas Newcomen is credited with inventing the steam engine in the year 1712. This is one of the most significant advancements in technological history. According to Sinclair (1907), the invention that was made by Thomas Newcomen in the course of history is the “atmospheric engine.” This engine required coal as its fuel to provide the necessary motive force, which was then applied to the task of pumping water out of the shafts of coal mines. James Watt, a Scottish mechanical engineer who lived from 1736 until 1819 and worked in a laboratory at the University of Glasgow in England, made improvements to the Newcomen steam engine in 1776. These improvements made it possible to harness massive amounts of coal-powered energy efficiently and cost-effectively (Allen, 2017; Mohajan, 2019a). This innovation ushered in the beginning of the early modern industrial age around the globe, ushering in revolutionary changes across a variety of economic subfields and industries, including textiles, mining, steam-powered railroads and ocean freighters, steam-powered steel production, and more (Mhlanga, 2022b). A result, this resulted in the huge expansion of cities, industries, and many types of infrastructure. According to Crafts (2011), the First Industrial Revolution brought about significant changes to our way of life as well as the economy, shifting it from one based on agriculture and handicrafts to one dominated by industry and the production of machines. During the second phase of the industrial revolution, the availability of oil and electricity encouraged mass production. Production became more mechanized with the use of information technology throughout the Third Industrial Revolution. Although each of the industrial revolutions is frequently viewed as a distinct event, they can be best understood when viewed collectively as a series of events that built upon the innovations introduced during the preceding revolution and led to more sophisticated forms of production (Crafts, 2011; Mohajan, 2019a). Significant shifts in sociocultural norms and values were brought about by the First Industrial Revolution, which was driven primarily by economic and technological developments. It appeared to worsen the workers’ precarious financial situations in the beginning phases of its implementation. They were forced to rely on expensive means of production that very few people could afford to acquire to maintain their work and way of life. A lack of job stability existed, and workers were regularly

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displaced from their positions due to advances in technology and a vast labour pool (Crafts, 2011; Mhlanga, 2022b; Mohajan, 2019a). Due to the absence of labour rights and regulations, workers were forced to put in long hours for meagre pay, endure deplorable living conditions, and face exploitation and abuse on the job. But even as new issues surfaced, new concepts emerged intending to resolve those issues (Crafts, 2011; Mhlanga, 2022b). These concepts inspired technologies and regulatory changes that not only made people’s lives easier in terms of material comforts but also made it possible for them to produce more, travel more quickly, and communicate more quickly.

The Second Industrial Revolution According to Mohajan (2019b), the years 1860 to 1914 are part of the Second Industrial Revolution because many innovative technologies were developed during this period. Some of these technologies include electricity, the internal combustion engine, chemical plants, alloys, petroleum and other chemical products, electrical communications technology (telegraph, telephone, and radio), and water supply with indoor plumbing. Inventions and improvements that occurred during the second phase of the industrial revolution were science-based and focused on areas such as iron and steel production, railroads, electricity, and chemical production. The transition from a rural to an urban society was brought about by the American Industrial Revolution, which is considered the Second Industrial Revolution. Massive innovations are linked with the Second Industrial Revolution. Some of these innovations include the internal combustion engine, electricity, chemical plants, petroleum and other compounds, alloys, electromagnetic communications technology, and indoor plumbing with running water (Mohajan, 2019b). Many people’s business practices and day-to-day lifestyles have been altered because of technological advancements in the areas of transportation and communications. These advancements, along with innovations in the fields of medicine and medical instrumentation, have also contributed to a significant improvement in the state of public health (Mhlanga, 2022b). Between the years 1820 and 1920, approximately 33 million people, the majority of whom were labourers, immigrated to the United States in search of greater economic opportunity, and as a result, cities became overcrowded. The global political, economic, and social systems have undergone very rapid and widespread change (Mohajan, 2019b).

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Labour dissatisfaction has been caused by a variety of factors, including but not limited to low salaries, dangerous working conditions, long working hours, child labour, wage discrimination, etc. In addition to this, a strike by train workers protesting job losses and salary cuts has taken place in several places around the United States (Mhlanga, 2022b; Mokyr & Strotz, 1998). In terms of the materials used in production, modern industry began to make use of various natural and synthetic resources that had not been used in the past. These resources included lighter metals, rare piles of earth, new alloys, and synthetic products such as plastics, in addition to new energy sources. The combination of these factors, along with advances in machinery, tools, and computer technology, resulted in the birth of the automatic factory. Although many sectors of industry were nearly entirely mechanized in the early to middle of the nineteenth century, the second half of the twentieth century was the first time that automatic operation, as opposed to the assembly line, acquired substantial significance. Alterations were also made to the ownership structure of the means of production. The acquisition of ordinary shares by individuals and by organizations such as insurance providers led to a wider distribution of ownership during the Industrial Revolution, which occurred from the early to the middle of the nineteenth century. This occurred because of the oligarchical control of the means of production that existed during this period. In the first half of the twentieth century, numerous European nations socialized fundamental aspects of their economies. Instead of the laissez-faire concepts that influenced the socioeconomic and thought of the traditional Industrial Revolution, which occurred during that period, governments typically moved into the economic and social realm to meet the requirements of their more complicated industrial societies during that period. This change in political theory occurred during that period (Mhlanga, 2022b; Mohajan, 2019b).

The Third Industrial Revolution One of the first things that kicked off the Third Industrial Revolution was the creation of the Advanced Research Projects Agency Network (ARPANET) in 1969. ARPANET was an early packet-switching network and the first network to apply the TCP/IP protocol suite. This was one of the first things that got the ball rolling on the Third Industrial Revolution (Mhlanga, 2022b; Roberts, 2015). The Third Industrial Revolution

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is being driven primarily by technological advancements in manufacturing, distribution, and energy factors. This is consistent with previous revolutions, which were also driven primarily by technological advances. The development of the Internet and the information age that followed was sparked by the Third Industrial Revolution. The Third Industrial Revolution is taking place on a global scale, but it is also taking place on a local scale, which is where the name “glocal” comes from. It radically altered how we organize and run cities and regions, as well as how we work, produce, and entertain ourselves. In addition to this, it resulted in the globalization of production as well as the repatriation of jobs. The development of the Internet, improved methods of communication, and metadata were the driving forces behind the Third Industrial Revolution, which resulted in a move away from the reliance on labour as the primary component of production. Since the early 1980s, both the global labour share and the ratio of labour to capital have experienced major declines. The Third Industrial Revolution has been propelled forward by developments in facets of industry such as the manufacturing sector, the energy sector, and the distribution sector. Both the local and the global fronts participated in the Third Industrial Revolution. The local front is what gave rise to the term “glocal,” and the global front is what gave rise to “globalization.” The Third Industrial Revolution, similar to the revolutions that came before it, brought about significant shifts in how people worked, how they produced goods, and even how they passed their leisure time. In a nutshell, the widespread use of automation and digital technology have been the defining characteristics of the Third Industrial Revolution. As a result of the development of the Internet, there was a rise in the utilization of electronic devices and computers during the Third Industrial Revolution. The introduction of nuclear power marked the beginning of a new era in the energy industry. The development of electronics made it possible to automate many of the processes used in manufacturing, and the spread of technological advances in communication led to globalization, which made it possible for businesses to produce goods and services at a lower cost in other countries. The following is a synopsis of some of the technological advances that occurred during the Third Industrial Revolution. In 1969, the advanced research projects agency network of the United States Department of Defense was responsible for the development of a great deal

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of the protocol software that is presently utilized for internet communication (APARNET). Waseda University in Japan, which was one of the universities in Japan at the time, was responsible for the creation of the world’s first full-scale intelligent humanoid robot in the year 1972 through the WABOT-1 project. The year 1973 marked the beginning of Bob Metcalfe’s work at the Xerox Palo Alto Research Center, also known as PARC, in California on the development of the ethernet. The description of the operation of the ethernet network system may be found in a document that Bob Metcalfe penned. The ethernet was a network that could connect advanced computer workstations to high-speed laser printers. This gave the workstations the ability to communicate data to both one another and to the printers. The first Internet service provider (ISP) came into existence in the year 1974. In the same year, a commercial version of APARNET, known as Telenet, was made available for use. This version was also introduced. In 1983, the standardization of Ethernet was completed, which was also the year that the domain name system was founded. This was the system that created the extensions.com,.org,.gov, and.net, and it was used to provide names to websites. William Gibson is credited with coining the word “cyberspace” during the Third Industrial Revolution, which is also regarded as an important innovation. In 1986, programmable logic controllers were connected to personal computers, and in 1989, Tim Berners-Lee invented the HTML language and made the World Wide Web accessible to the public. Both developments occurred simultaneously. The development of the first Internet of Things device, which was accomplished by John Romkey through the creation of a toaster that could be turned on and off over the Internet, was the other significant and outstanding accomplishment of the Third Industrial Revolution. This was accomplished by creating a toaster that could be controlled over the Internet. Tim Berners-Lee was the one who developed the very first web page in 1991, which was also the year that the Internet carried its first audio and video files. Other noteworthy occurrences that occurred during the Third Industrial Revolution included the Internet beginning to be used for commercial purposes with the launch of Amazon, eBay, and Craigslist in 1995, among other concerns (Janicke & Jacob, 2013; Mhlanga, 2022b).

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The Fourth Industrial Revolution The term “Fourth Industrial Revolution” refers to an age that is perceived to be defined by digitization, digital transformation, the Industrial Internet of Things (IoT), personally linked devices, artificial intelligence (AI) technology, automation, and data analytics (Mhlanga, 2022b). There is a direct challenge on the lines between the physical, digital, and biological domains in the period that is being dubbed the Fourth Industrial Revolution. This age is also described as a time. There is concern that the industries of the globe will be uprooted because of the Fourth Industrial Revolution since manufacturers are adopting a range of technologies such as the cloud, big data analytics, and the Internet of Things. The Fourth Industrial Revolution is characterized by rapid technical advancement, which promotes transformations that apply to all sectors of the economy as well as all aspects of society. The Fourth Industrial Revolution is bringing about alterations in the way that value in the economy, politics, and society is created, exchanged, and dispersed (Schwab, 2017). According to Schwab (2017), the Fourth Industrial Revolution is on the cusp of a technological revolution that is changing the way people live, the way people work, and the way people relate to one another. The scale, scope, and complexity of the Fourth Industrial Revolution are unparalleled to anything ever experienced by humankind. This makes the Fourth Industrial Revolution a one-of-a-kind event. One of the points that Schwab (2017) brought up was that it is not yet apparent how the Fourth Industrial Revolution will play out, but that the way humanity should adapt to it must be integrated and comprehensive, with all stakeholders considered. This reaction ought to include participation from the public sector, the commercial sector, and civil society. Because of the factors discussed in this article, the Fourth Industrial Revolution can be considered an entirely new revolution in comparison to the Third Industrial Revolution. One of the reasons for this is because the speed, scale, and influence that the Fourth Industrial Revolution will have on systems will be unlike anything that has ever been seen before. The other problem is that the rate at which the breakthroughs are happening has no historical precedent, and the Fourth Industrial Revolution is happening at an exponential pace rather than a linear pace. Additionally, the Fourth Industrial Revolution is causing disruptions in all industries in every nation. It is also considered that the Fourth Industrial Revolution is bringing about enormous prospects for countries, individuals, and even businesses themselves.

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Artificial intelligence, the Internet of Things, quantum computing, and blockchains are examples of emerging technologies that hold the promise of improved system optimization. People’s daily routines and identities are being transformed because of the Fourth Industrial Revolution, particularly as a result of its effects on people’s perceptions of their privacy, their patterns of consumption, the amount of time they spend working and relaxing, and how carriers are created. On the other hand, businesses are experiencing significant disruption because of the Fourth Industrial Revolution. On the supply side, existing industry value chains are being disrupted by the introduction of new technologies that come forward with new ways of serving the existing demands. On the supply side, there has been an increase in transparency, consumer engagement, and new patterns of consumer behaviour, all of which have been spearheaded by access to mobile devices. As a result, businesses are being forced to change how they design their products, as well as how they market, sell, and distribute their goods and services. In conclusion, the Fourth Industrial Revolution has four primary effects on enterprises, and these effects may be broken down into the following categories: customer expectations, product enhancement, collaborative innovation, and organizational forms. Citizens are now ready to embark on extensive interactions with the government to voice their ideas because of the convergence of the physical, digital, and biological worlds, as well as the advent of new technologies and platforms. It even made it possible for citizens to avoid the oversight of governmental authorities by working together and coordinating their actions. On the other side, governments are continuously obtaining technological tools to expand their control over their citizens using widespread surveillance systems and the capability to have control over the digital infrastructure. The Fourth Industrial Revolution will increase the amount of pressure that is placed on governments to bring about changes in their conventional techniques towards engaging the public and developing policies. By enabling the redistribution and decentralization of authority, technology poses a challenge to the primary function of the government, which is the formulation of public policy. The capacity of governments to adjust will be a critical factor in determining whether they will continue to exist. The possibilities that arise when billions of people are connected through mobile devices, each with tremendous processing power, storage capacity, and access to information, are almost limitless. And these possibilities will be expanded as new

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technological advances are made in disciplines such as artificial intelligence, robots, the Internet of Things, autonomous cars, 3D printing, nanotechnology, biotechnology, materials science, energy storage, and quantum computing. Already, artificial intelligence can be found in a wide variety of applications, including self-driving cars and drones, virtual assistants, and software that can translate or invest. Recent years have seen remarkable advancements in artificial intelligence (AI), driven by exponential increases in computing power as well as the availability of vast amounts of data. Examples of these advancements include software that is used to discover new drugs and algorithms that are used to predict our cultural interests. Meanwhile, technologies for the manufacturing of digital objects are constantly interacting with the biological environment. Computational design, additive manufacturing, materials engineering, and synthetic biology are being combined by engineers, designers, and architects to pioneer a symbiosis between microorganisms, our bodies, the things we consume, and even the buildings we inhabit.

Technologies Driving the Fourth Industrial Revolution The Fourth Industrial Revolution is being propelled by an abundance of different technologies, some of which include artificial intelligence, big data, blockchain, cloud computing, autonomous vehicles, quantum computing, robots and cobots, and the Internet of Things, to name just a few. Figure 2.1 provides a concise explanation of these different technologies. The technologies that are driving the Fourth Industrial Revolution have been illustrated in Fig. 2.2. These technologies include artificial intelligence, big data, blockchain, cloud computing, autonomous vehicles, quantum computing, robots and cobots, and the Internet of Things. These technologies are currently being used throughout multiple sectors of the economy, one of which is the medical industry. In the field of mental healthcare, applications of artificial intelligence and machine learning are being developed to create solutions for the prediction, detection, and treatment of mental health problems. To reiterate, artificial intelligence is being applied to digital treatments such as web and smartphone applications to improve the user experience and maximize the effectiveness of personal mental healthcare. All these things contribute to the fact that AI and machine learning are currently being employed in

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Artificial Inteligence and Machine Learning Quantum Computing Big Dta Blockchain Cloud Computing Robots and Cobots Internet of Things

Fig. 2.2 Technologies driving the fourth industrial revolution (Source Author’s analysis)

the field of healthcare. This fits in well with the goal of the current study, which is to investigate the role that artificial intelligence and machine learning play in addressing the COVID-19 Pandemic and to understand the lessons that we are learning about the Fourth Industrial Revolution and the sustainable development goals. To be more explicit, goal three focuses on maintaining good health and well-being. In the following section, you will find a quick explanation of artificial intelligence (AI), which will be followed by an explanation of the function that AI plays, as well as the lessons that we are learning about the Fourth Industrial Revolution and the aims for sustainable development.

Artificial Intelligence According to Bajwa et al. (2021), the term “artificial intelligence” (AI) refers to the science and engineering of creating intelligent machines using algorithms or a set of rules that the machine must adhere to mimic human cognitive processes such as learning and problem-solving. AI systems have the potential to predict difficulties or to deal with issues

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as they come up. As a result, AI systems act in a manner that is deliberate, intelligent, and adaptive (Bajwa et al., 2021). The ability of artificial intelligence to learn and recognize patterns and relationships from large multidimensional and multimodal datasets is one of its greatest strengths. For instance, AI technologies could translate a patient’s overall health file into a single data point that represents a likely diagnosis. This would be a tremendous time saver for medical professionals. In addition, AI systems are dynamic and self-sufficient; they are capable of learning and adapting as new data is made accessible to them. The term “artificial intelligence” (AI) does not refer to a single, all-encompassing piece of technology; rather, it encompasses several distinct subfields, including “machine learning” and “deep learning,” all of which can, on their own or in conjunction with one another, impart.

A Brief History and Definition of AI In 1955, John McCarthy had the idea that all the significant features of intelligence and learning might be simulated by a machine. This was the beginning of the field of study that is now known as artificial intelligence (AI) (Mhlanga, 2020; Wisskirchen et al., 2017). On the other hand, Haenlein et al. (2019) stated that “although it is challenging to identify the origins of AI, nevertheless, it is feasible to trace back to the 1940s, particularly 1942, when the American Science Fiction writer Isaac Asimov authored his short story Run around.” In other words, it is possible to trace the origins of AI back to the 1940s. A story about a robot that was constructed by engineers is at the centre of the plot of Run. In 1956, Marvin Minsky and John McCarthy, both of whom were computer scientists at Stanford, are credited with being the first people to formally create the term “artificial intelligence.” The study of intelligent behaviour in the context of problem-solving and the development of intelligent computer systems are examples of what is meant when people talk about artificial intelligence. To put it another way, artificial intelligence (AI) refers to the activities carried out by computers that would require intelligence if conducted by people (Wisskirchen et al., 2017). According to Wisskirchen et al., (2017), there are two categories of artificial intelligence: weak AI and strong AI. With a low level of artificial intelligence, “the computer is only an instrument for exploring cognitive processes” and “the computer simulates intelligence.” On the other hand, strong artificial intelligence implies “the processes where computers are

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intellectual, self-learning processes"(Mhlanga, 2023). Using the appropriate software and programming, computers can comprehend and can modify their actions following their historical actions and their acquired knowledge (Wisskirchen et al., 2017). A powerful artificial intelligence would automatically network with other machines, which would result in a significant scaling impact (Buchanan, 2005). Deep learning, robotization, dematerialization, the gig economy, and autonomous driving cars are among the most prominent examples of economic applications of artificial intelligence (Wisskirchen et al., 2017). According to Benko and Sik Lányi (2009), even though AI has been studied for decades, it is still one of the most elusive issues in computer science. This is because the subject is both expansive and ambiguous, which makes it difficult to pin down. Some people believe that artificial intelligence encompasses everything from machines that can reason to search algorithms used to play board games. It is implied that artificial intelligence has applications in virtually every way that people make use of computers in society. AI is employed in more covert ways, such as evaluating purchase histories and influencing marketing decisions (Benko & Sik Lányi, 2009). These are both examples of AI applications. According to Buchanan (2005), the public’s perception of intelligent computers has always included robots. However, early attempts in robotics had more to do with mechanical engineering than with intelligent control. However, because of the potential of artificial intelligence, robots are becoming more powerful vehicles for the testing of ideas regarding intelligent behaviour all around the world. However, some people believe that artificial intelligence is not limited to the study of robots. In addition to this, it is about using computers as tools for research to gain an understanding of the nature of intelligent thought and behaviour (Buchanan, 2005). Artificial intelligence (AI) refers to a vast area of computer science that focuses on the development of systems that can simulate intelligent behaviour or act intelligently on their own. To put it another way, artificial intelligence (AI) is a constellation of several technologies that collaborate to help robots sense, comprehend, act, and learn at a level of intellect that is comparable to that of humans. There are two different types of artificial intelligence: narrow AI, sometimes known as weak AI, and general AI, also known as strong AI. Narrow AI refers to the type of AI that is used in our day-to-day life. This type of AI is responsible for performing a single task or a mix of tasks that are related to one another, such as digital assistants and weather apps. These technologies and algorithms are powerful; nonetheless, the competitive

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landscape is limited, which lends itself to the words being limited and weak. Even AI that is only partially developed or focused has the potential to bring about significant transformations, especially if it is deployed in the right way. This potential can affect how we work and live on a global scale. In a sense, the primary focus of weak AI is on improving operational efficiencies in a variety of different environments. On the other hand, general artificial intelligence, also known as strong artificial intelligence, is a type of artificial intelligence in which sentient machines imitate human intelligence by thinking strategically, abstractly, and creatively and having the ability to handle a wide variety of complex tasks. AI is still considered an extension of human capabilities rather than a substitute for those capabilities at this point; the full realization of general AI has not yet been achieved. Because of this, it is critical to have productive collaboration between humans and machines. Artificial intelligence can take the form of statistical learning, in which machines imitate human speech and listen to communicate through language; this subfield of voice recognition is another name for this area of study. Because statistical analysis underpins a significant portion of voice recognition, this method is frequently referred to as statistical learning. There is also the area of study known as natural language processing, which entails programming computers to read and write in human languages. The study of making computers capable of seeing and processing information is known as computer vision. The symbolic method of information processing is one of the categories that is included under the umbrella of the study of computer vision. The other subfield of AI is pattern recognition, in which computers can figure out patterns such as collections of things that are like one another. The topic of machine learning involves an increase in the amount of data as well as the dimensionality of data. The ability of machines to comprehend their surroundings and freely navigate their surroundings falls under the purview of robotics. When it comes to the human brain, the human brain is comprised of a network of neurons that are responsible for the process of learning new things. Therefore, for computers to achieve cognitive skills, which are referred to as the field of neural networks, they use the same structure and function. Deep learning is a subfield of machine learning that refers to situations in which the networks being used to learn something difficult are themselves both complex and deep. Deep learning, or the process of using computers to learn in a manner analogous to that of the

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human brain, can be implemented in several different ways in machines. A convolutional neural network is one in which the network can scan images in both horizontal and vertical directions. Objects in a scene are often recognized using a convolution neural network. This is where computer vision comes into play, and how artificial intelligence accomplishes the task of object recognition. Recurrent neural networks are those that can remember a limited amount of the past and are therefore referred to by that name. Neural networks can have this ability. The symbolic-based and the data-based modes of operation are both possible in neural networks. Before the computer can learn, it must first be given a large amount of data from the database, which is also known as machine learning. On the other hand, symbolic learning uses symbols to stand in for certain things and ideas and enables developers to describe explicitly the connections that exist between those symbols and the things and ideas they stand for. All of these different methods of machine learning can either be used for categorization or prediction, both of which are useful applications. According to Dick (2019), the historical background of artificial intelligence is not simply a documentation of machines trying to imitate or try replacing human intellect; rather, it is also a record of how our conception of intelligence has developed with time. This is because the history of artificial intelligence is more than just a record of machines attempting to imitate or replace human intellect. Therefore, contrary to what the mainstream narrative would have us believe, artificial intelligence (AI) is not an invention; rather, it is entrenched in far more comprehensive histories of what makes up intelligence and what makes up artificial intelligence. Artificial intelligence (AI) is the science and engineering that underpins the construction of intelligent machines, most notably intelligent computer programmes, according to John McCarthy’s definition of the term. It is related to the same work of utilizing computers to comprehend human intellect, but artificial intelligence does not have to restrict itself to ways that can be observed medically. Instead, it is connected to the challenge of employing computers to understand human intelligence (Sutton, 2020). Intelligence, on the other hand, might be defined as the capacity to learn and use efficient techniques for problem-solving and goal attainment, taking into consideration the particulars of the situation in a world that is fundamentally uncertain and continuously moving. However, despite its versatility, accuracy, and dependability, a pre-programmed manufacturing robot lacks intelligence. However, the discourse around

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artificial intelligence was characterized by Alan Turing’s seminal work, Computing Machinery, and Intelligence, which was published in 1950. This paper was the beginning of the conversation. This occurred several decades before the introduction of this idea. In this piece of writing, Turing, who is widely recognized as the “father of computer science,” asks the question, “Can machines think?” (Can computers think?). Following that, he suggests a test that would subsequently become widely known as the Turing Test in the future. During this test, a human interrogator will attempt to distinguish between a written response produced by a machine, and one produced by a human (Dick, 2019; McCarthy, 2007; Mhlanga, 2023). Even though this test has been the subject of a great deal of scrutiny ever since it was published, it is still considered to be an important part of the history of artificial intelligence and an ongoing concept within the field of philosophy because it makes use of ideas that revolve around linguistics (Mhlanga, 2023). Following that, Stuart Russell and Peter Norvig went on to publish a book that would later become one of the most influential textbooks in the field of artificial intelligence research. That book was titled Artificial Intelligence: A Modern Approach, and it was written by Stuart Russell and Peter Norvig. In it, they discuss four distinct goals or definitions of artificial intelligence, each of which categorizes computer systems in terms of logic and reasoning as opposed to action. Possible Definitions of Artificial Intelligence are shown in Fig. 2.3. Figure 2.3 illustrates the many ways in which the term “artificial intelligence” might be interpreted. Alan Turing’s notion of artificial intelligence would have included computer programmes that behave in a manner that is comparable to that of humans. To put its definition in the simplest terms possible, artificial intelligence (AI) is a field of study that, when combined with huge datasets and computer science, enables problems to be solved. In addition to this, it integrates the subfields of machine learning and deep learning, both of which are frequently referred to when discussing artificial intelligence. In addition to this, it incorporates the subfields of machine learning and deep learning. These subfields of artificial intelligence are constituted of algorithms that work towards the construction of expert systems that, based on the information to which they have access, can produce forecasts or classifications (Mhlanga, 2023).

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Human Approach

Ideal Approach

• Simulations of human thought in machines • AI systems that mimic human behavior

• Systems that are capable of rational thought • Systems that behave in a reasonable manner

Fig. 2.3 Potential definitions of AI (Source Author’s analysis)

Deep Learning and Machine Learning Although the phrases “deep learning” and “machine learning” are sometimes used interchangeably, there are significant differences between the two that need to be clarified. Machine learning is a subfield of artificial intelligence, while deep learning is a subfield of machine learning. Deep learning is a subfield of machine learning (AI). AI includes not just machine learning but also deep learning as one of its subfields. Deep Learning Deep learning is a subset of machine learning that entails training artificial neural networks with multiple layers to perform complex tasks. Some examples of these tasks include image and speech recognition, natural language processing, and decision-making. Deep learning is a subset of machine learning. One way to train a deep learning model to detect photographs of cats and dogs, for instance, is to provide the model with a large dataset of annotated images. The model then learns to recognize patterns in the images that allow it to differentiate between the two classes of images. Another illustration of this may be seen in the field of speech recognition, where deep learning models can be taught to

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identify and transcribe spoken words. These models are educated using massive datasets that contain audio recordings and the transcripts that correspond to those recordings. This enables the models to learn the connections that exist between sound waves and written words. Natural language processing tasks including language translation, sentiment analysis, and chatbots are examples of applications for deep learning. For instance, a deep learning model may be educated to translate text from one language to another by learning the associations between the words in each language. This can be accomplished by gathering information about the connections between words in both languages. The connections between these three ideas are depicted in Fig. 2.4, which can be found here (Artificial Intelligence, Machine Learning and Deep Learning).

Fig. 2.4 Machine learning, deep learning, and artificial intelligence

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Figure 2.4 illustrates the links that exist between artificial intelligence, machine learning, and deep learning. Deep learning relies heavily on the use of neural networks as its primary building pieces. An example of a deep learning algorithm is a neural network that has more than three layers and would contain both the inputs and the outputs (Goodfellow et al., 2016). The concept of “deep learning” refers to the presence of these many nested layers (LeCun et al., 2015). Deep learning has the potential to automate a sizeable chunk of the process that entails the collection of features. As a consequence of this, part of the necessary manual interaction with humans can be reduced or eliminated, and larger data sets can be employed. In contrast to machine learning, which may train its algorithm with the help of labelled datasets, deep learning does not always require an existing dataset to be labelled to be successful. One of the names that are used to refer to machine learning is supervised learning. It can ingest unstructured data in its raw form, which includes text and images, and it can automatically establish the hierarchy of qualities that differentiate various forms of data from one another. This feature allows it to process unstructured data faster than traditional methods. In contrast to machine learning, it does not necessitate the participation of people in the data processing. As a direct result of this, we have more intriguing opportunities to expand machine learning. Machine Learning (ML) Machine learning is a subfield of artificial intelligence that involves using algorithms to enable computer systems to automatically learn from data and improve their performance on a given task without being explicitly programmed. For instance, a machine learning model can be taught to determine, based on historical behaviour, such as purchase history and usage trends, if a consumer is likely to cancel their membership to a service. The service provider can then take proactive steps to retain these high-risk clients by using the model to identify those who are most likely to leave. Using a sizable dataset of labelled photos, a model can be trained to recognize various objects in photographs as another example of machine learning. The model can classify new, previously undiscovered photos by learning to recognize patterns and features in the images that correspond to each object. In natural language processing tasks like sentiment analysis, text categorization, and speech recognition, machine learning is also employed. A machine learning model, for instance, can

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be trained to recognize speech and type it into text or to categorize text documents according to their content. “Supervised learning,” “Unsupervised learning,” and “Reinforcement learning” are the three main subfields that fall under the umbrella of “machine learning.” In the context of machine learning, supervised learning refers to the process of developing algorithms for prediction or classification in the presence of labelled data. These algorithms require that inputs (predictors) be mapped to some output (response) (Chen et al., 2020; Mhlanga, 2021, 2022b). When the output is categorical, the problem that needs to be solved is classification, but when the output is continuous, the problem that needs to be solved is prediction. Methods such as linear and nonlinear regression, random forests, neural networks, and decision trees are all examples of algorithms that can be used for supervised learning. The formation of patterns and trends in data that have not been labelled is an aspect of unsupervised learning. Machine learning (ML) refers to the study of algorithms that allow computer programmes to automatically improve through experience and it may be categorized as “supervised,” “unsupervised,” and “reinforcement learning” (RL) as outlined in Fig. 2.2. There is also ongoing research in various subfields including “semi-supervised,” “selfsupervised,” and “multi-instance” ML (Bajwa et al., 2021; Mhlanga, 2023). There are many subfields within the field of machine learning, as can be seen in Fig. 2.5. Utilizing labelled data or annotated information is at the heart of supervised learning. One application of this technique is finding tumours in new photographs by comparing them to tagged X-ray images of previous tumours. The term “unsupervised learning” refers to the process of attempting to glean information from data in the absence of labels; for instance, classifying groups of patients who exhibit similar symptoms to determine a common cause. Through a process known as reinforcement learning, computational agents can learn either through trial and error or by watching an expert demonstrate their knowledge. The learning algorithm comes up with a plan to earn the most rewards possible. Reinforcement learning has been the driving force behind many of the significant advances made in artificial intelligence in recent years. Deep learning refers to a category of algorithms that acquires knowledge by making use of a sizable and interconnected network of many-tiered processes and presenting these processors with a substantial quantity of illustrative data. Deep learning has emerged as the method of choice in

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artificial intelligence (AI) today, and it is driving breakthroughs in fields such as image and speech recognition as mentioned before. The argument made in this book is that AI platforms can employ machine learning to track and report on progress made towards Sustainable Development Goals (SDGs). The book argues that AI platforms can employ machine learning techniques to monitor, track, and analyse progress made towards achieving the Sustainable Development Goals (SDGs). By using advanced algorithms, these AI platforms can process large amounts of data and identify trends and patterns that can be used to generate insights into how well countries, organizations, and individuals are progressing towards the SDGs. These AI platforms can gather data from various sources such as social media, news outlets, and government databases, and analyse it using machine learning algorithms to identify relevant information, such as progress on

Deep Learning

Reinforcement Learning

Machine Learning

Unsupervised Learning

Fig. 2.5 Categories of machine learning

Supervised Learning

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poverty reduction, gender equality, and climate change. They can also use natural language processing to analyse text and identify sentiment, which can help determine public attitudes towards sustainable development. By tracking progress towards the SDGs, these AI platforms can help organizations and policymakers make data-driven decisions that are informed by real-time data. This can lead to more effective and targeted interventions, such as policies that incentivize sustainable practices and investments in renewable energy. Ultimately, the use of AI platforms to monitor and report on progress towards the SDGs has the potential to accelerate progress towards achieving a sustainable future for all.

Chapter Summary This chapter thoroughly examined the concept of the Fourth Industrial Revolution and its close relationship with artificial intelligence. The Fourth Industrial Revolution represents a convergence of digital, biological, and physical technology, and is a distinct and unprecedented revolution on a scale never seen before. This revolutionary period has led to massive changes in all areas, with billions of people linked together through mobile devices that have unparalleled processing power, data storage capacity, and access to information. Industry 4.0 is characterized by cutting-edge advancements in numerous areas, including artificial intelligence, robotics, the Internet of Things, autonomous vehicles, 3D printing, nanotechnology, materials science, energy storage, and quantum computing. The chapter’s final section delved into a detailed description of artificial intelligence.

References Allen, R. C. (2017). The industrial revolution: A very short introduction (Vol. 509). Oxford University Press. Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188. Benko, A., & Sik Lányi, C. (2009). History of artificial intelligence. In Encyclopedia of information science and technology (2nd ed., pp. 1759–1762). IGI Global. Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. AI Magazine, 26(4), 53–53.

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Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020). A critical review of machine learning of energy materials. Advanced Energy Materials, 10(8), 1903242. Crafts, N. (2011). Explaining the first industrial revolution: Two views. European Review of Economic History, 15(1), 153–168. Dick, S. (2019). Artificial intelligence. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.92fe150c Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. Haenlein, M., & Kaplan, A. (2019). Guest editorial to the special issue—A brief history of AI: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. Janicke, M., & Jacob, K. (2013). A third industrial revolution. In Long-term governance for social-ecological change (pp. 47–71). Routledge. Lavopa, A., & Delera, M. (2021). What is the Fourth Industrial Revolution? https://iap.unido.org/articles/what-fourth-industrial-revolution LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. McCarthy, J. (2007). What is artificial intelligence? http://jmc.stanford.edu/art icles/whatisai/whatisai.pdf. Accessed May 20, 2022. Mhlanga, D. (2020). Industry 4.0 in finance: The impact of artificial intelligence (AI) on digital financial inclusion. International Journal of Financial Studies, 8(3), 45. Mhlanga, D. (2021). Artificial intelligence in Industry 4.0, and its impact on poverty, innovation, infrastructure development, and sustainable development goals: Lessons from emerging economies? Sustainability, 13(11), 5788. Mhlanga, D. (2022a). The role of artificial intelligence and machine learning amid the COVID-19 pandemic: What lessons are we learning on 4IR and the sustainable development goals. International Journal of Environmental Research and Public Health, 19(3), 1879. Mhlanga, D. (2022b). The Fourth Industrial Revolution: An introduction to its main elements. In Digital financial inclusion (pp. 17–38). Palgrave Macmillan. Mhlanga, D. (2023). Artificial intelligence and machine learning for energy consumption and production in emerging markets: A review. Energies, 16(2), 745. Mohajan, H. (2019a). The first industrial revolution: Creation of a new global human era. Munich Personal RePEc Archive. https://mpra.ub.uni-mue nchen.de/96644/ Mohajan, H. (2019b). The second industrial revolution has brought modern social and economic developments. Munich Personal RePEc Archive. https://mpra. ub.uni-muenchen.de/98209/

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Mokyr, J., & Strotz, R. H. (1998). The second industrial revolution, 1870–1914. Storia dell’economia Mondiale, 21945(1). Roberts, B. H. (2015). The third industrial revolution: Implications for planning cities and regions. https://www.researchgate.net/publication/278671121_ The_Third_Industrial_Revolution_Implications_for_Planning_Cities_and_Reg ions. Accessed October 19, 2022. Schwab, K. (2017). The Fourth Industrial Revolution. Currency. Sinclair, A. (1907). Development of the locomotive engine: A history of the growth of the locomotive from its most elementary form, showing the gradual steps made toward the developed engine, with biographical sketches of the eminent engineers and inventors who nursed it on its way to the perfected form of to-day. Many particulars are also given concerning railroad development. A. Sinclair Publishing Company. Sutton, R. S. (2020). John McCarthy’s definition of intelligence. Journal of Artificial General Intelligence, 11(2), 66–67. Wisskirchen, G., Biacabe, B. T., Bormann, U., Muntz, A., Niehaus, G., Soler, G. J., & von Brauchitsch, B. (2017). Artificial intelligence and robotics and their impact on the workplace. IBA Global Employment Institute, 11(5), 49–67. Xu, M., David, J. M., & Kim, S. H. (2018). The Fourth Industrial Revolution: Opportunities and challenges. International Journal of Financial Research, 9(2), 90–95.

CHAPTER 3

Financial Technology (FinTech) an Introduction

Introduction The term “financial technology” (FinTech) refers to the development of new technology to enhance and automate the delivery of financial services as well as their use. FinTech, at its core, refers to the application of specialized computer software and algorithmic programmes that can be run on personal computers and, increasingly, mobile phones to assist consumers, business owners, and other individuals in better managing their financial activities, processes, and lives. This is accomplished using mobile phones. There is a growing likelihood that businesses fuelled by financial technology will develop as competitive alternatives to conventional financial intermediaries, markets, and infrastructures. However, the widespread use of modern technologies has not only brought about benefits but also opened the door to potential dangers. FinTech may lead to greater advances in efficiency in the financial sector, the provision of goods and services that are superior in quality and more precisely targeted, and an expansion of financial inclusion in emerging countries. On the other hand, it may also present problems if it is implemented in a way that weakens competition, trust, the transmission of monetary policy, and financial stability. According to Kou (2019), there is currently a proliferation of FinTech all over the world. This term refers to the proliferation of technological

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tools, platforms, and ecosystems that make financial services or products more easily available, more efficient, and more economical. Since the coining of the term “FinTech,” the state government, society at large, and academic institutions have been intensely focused on the application, advancement, and influence of financial technology (Kou, 2019). Not only is FinTech a topic of discussion in academic circles, but it has also emerged as an essential instrument for allowing previously excluded households to take part in financial innovation and even to influence financial markets. The convergence of technology and finance has led to significant advances in the development of novel financial products and service models, as well as in the enhancement of the overall customer experience and the reduction of transaction costs. Integrations of technology in the digital currency market, stock price forecasting, and portfolio optimization have garnered a growing amount of attention as the financial services industry transitions from the discovery phase to the application phase. FinTech is currently not only a popular topic of discussion in the mainstream media about the future of the financial sector, but it is also a real project that changes banking and financial services, specifically allowing low-income households to have access to formal financial services and its impact on sustainable development goals. Given the important role that FinTech plays in the operation of the financial system and its infrastructure, the increased regulatory scrutiny that has been drawn as a result of the fast rise of FinTech seems to be reasonable. As was previously mentioned, many people view FinTech as the result of a relatively recent marriage between the worlds of financial services and information technology. However, there has been a long-standing connection between the worlds of finance and technology. The evolution of both fields has been inextricably linked and mutually supportive for a very long time. The Global Financial Crisis that occurred in 2008 was a turning point, and it is one of the reasons why FinTech is now growing into a new paradigm coupled with the COVID-19 pandemic that occurred recently. This new development presents issues not only for market regulators but also for market participants, notably in striking a balance between the potential advantages of innovation and the possible risks of innovation. The developing world has the greatest difficulty in maintaining this delicate equilibrium between competing priorities. The purpose of this chapter is to provide an introduction to FinTech by discussing its history, as well as some of the technology and other topics that are related to it.

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Historical Evolution of Payment System Purchasing items and making payments for those purchases is something that occurs frequently in our lives. As proposed by Zeidy (2022) the following is an examination of the historical development of the various payment systems. The Fig. 3.1 outlines the historical evolution of the payment system. The historical progression of payment methods, which is depicted in Fig. 3.1, began with the exchange of goods for services, moved on to the use of coins and paper money, and has now arrived at the use of digital payment methods and real-time gross settlements.

Fig. 3.1 Historical evolution of the payment system

Historical Evolution of Payment System

Barter

Coins

Paper money and Bank Notes

Bills of exchange and checks

Cards

Digital payments

Real Time Gross Settlement (RTGS)

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Barter Trade The first phase is the bartering process. The exchange of products or services between two or more persons without the utilization of money or any other form of monetary media, such as a credit card, is an example of bartering. In its most basic form, bartering entails one party providing another party with one good or service in exchange for the other party providing them with another good or service. According to Zeidy (2022), there is evidence that a barter system existed as early as the Neolithic, beginning with the development of an agricultural and animal culture most likely before 7000 BC. The transition from a nomadic, huntergatherer way of life to farming by some human communities marked the beginning of the Neolithic era. Coins Around the fifth or sixth century BCE, coins were first used as a form of currency in several countries. However, it is believed that coins were first minted by the Lydians, and Aristotle claims that the first coins were minted by Demodike of Kyrme, the wife of King Midas of Phrygia. The invention of coins is still shrouded in mystery; however, it is believed that coins were first minted by the Lydians (Crabben, 2011). Others maintain that the earliest use of coinage occurred somewhere between the years 680 and 560 BC in the region that is today known as Turkey (Zeidy, 2022). The usage of coins came about because of the challenges that were occasionally presented by bartering for goods and services, as well as the fact that certain kinds of payment were ephemeral and hence could not be stockpiled. As a direct consequence of this, coins composed of precious metals came into being. Paper Money and Banknotes Their purpose was to take the place of coins since it was inconvenient to carry huge quantities of coins. There is evidence that banknotes were in circulation in China as early as the seventh century, but it wasn’t until 812 that their usage was sanctioned (Zeidy, 2022). Up until the 1970s, the government of a country was required to have a particular amount of gold to back each new issuance of banknotes that they issued.

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Bills of Exchange and Cheques The use of bills of exchange can be traced back to Italy in the twelfth century. This document served as a guarantee that the debtor would pay the creditor or any other person who was specified in the commercial document as being entitled to receive the money. The use of cheques, on the other hand, may be traced back to the period around the eighteenth century and is associated with the Crown of England. Cards In 1914, the Western Union firm produced a loyalty card for its most exclusive customers. With this card, these customers were granted access to a line of credit that did not incur any additional fees. This was the first credit card ever issued. However, banks did not begin issuing cards as a form of payment until the year 1958. The initial card eventually became known as a Visa card. Digital Payments In the 1990s, the Internet and the World Wide Web system were introduced, and shortly thereafter, people started selling goods and services over this newly established communication channel. Peapod was one of the first companies to provide consumers with the opportunity to shop for groceries online from the comfort of their own homes using their personal computers. It is now feasible to make payments using a mobile phone or a digital watch as a direct result of the digital revolution that has taken place in recent years as well as the advent of new technologies. Mobile access and the Internet have been transformative in that they have made it possible for the benefits of technological progress to be shared directly with billions of individual consumers. Consumers’ mobile devices have become portals through which they can access a comprehensive range of financial services (Mhlanga, 2020, 2022c). Through Application Programming Interfaces, other parties are also able to augment their functionality (APIs) (Mhlanga, 2022c). This tremendous decentralization is paving the way for direct person-to-person transactions (also known as P2P transactions), as well as the direct funding of businesses (crowdfunding). It has significant repercussions for financial inclusion as well

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because it makes it possible for “unbanked consumers in low-income nations to obtain financial services for the first time.” Real-Time Gross Settlement (RTGS) These are specialized systems for the transmission of funds in which the movement of funds or securities from one bank to another can occur in “real time” and on a “gross” basis. The transfer can take place between any two banks. Settlement in “real-time” indicates that there is no waiting period associated with a payment transaction. Instead, transactions are completed as soon as they are processed, eliminating the need for any kind of waiting period. When a transaction is said to have “gross settlement,” it indicates that it was settled on a one-to-one basis, without being bundled or netted with any other transactions. When a payment has been “settled,” it signifies that the transaction is complete and cannot be reversed.

What Is FinTech? The term “financial technology” is condensed to “FinTech,” and it refers to a significant and expanding portion of the new financial services market of the twenty-first century. This business is particularly fueled by a variety of technologies that emerged during the Fourth Industrial Revolution. FinTech is a company that sells items related to FinTech. Start a business if it will provide a solution to a problem that is caused by financial technology. Alternatively, a corporation might offer these kinds of solutions without becoming a FinTech company. One example of this is Apple, which is a company that specializes in technology and media but also invented and now distributes the solution known as Apple Pay. The term “FinTech” refers to the unique processes and goods that become available for use in the financial services industry as a direct result of improvements in digital technology. To be more specific, FinTech refers to any financial innovation that is enabled by technology and that has the potential to result in new business models, applications, processes, or products while simultaneously having an associated material effect on financial markets and institutions as well as the provision of financial services (Leong & Sung, 2018; Mhlanga, 2022a; Vijai, 2019).

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Elements of FinTech FinTech start-ups, technology developers, governments, financial customers, and traditional financial institutions are some of the various elements that make up FinTech ecosystems. FinTech can be applied in many different contexts, and each of these contexts has its own unique set of elements that make up the FinTech ecosystem (Lee & Shin, 2018; Mhlanga, 2022b; Ye et al., 2022). The components that make up the FinTech ecosystem are broken down into the categories shown in Fig. 3.2. These categories include FinTech start-ups, technology developers, governments, traditional financial institutions, and financial clients. Big tech platforms such as social networking, e-commerce, and ride-hailing have enabled new business models and encouraged a new wave of FinTech by utilizing very large user bases and scale efficiencies thanks to platform ecosystems (Ye et al., 2022). The field of financial technology can be broken down even further into four primary subfields, each of which is comprised of its distinct parts (Vijai, 2019; Ye et al., 2022). Figure 3.3 provides an outline of the numerous subfields and components that make up FinTech. The fundamental components of various FinTech technologies will be discussed in the next section.

Building Block Technologies for FinTech The primary technologies or trends that are enabled by technology that, individually or collectively, make it possible for current and future FinTech developments are outlined in Fig. 3.4. Figure 3.4 outlines the major technologies or technology-enabled trends that, individually or collectively, facilitate current and future FinTech innovations. As supported by many scholars like Ye et al. (2022), Li and Xu (2021), and Mhlanga (2022c). From the information presented in Figs. 3.1–3.3, the general domains for FinTech can be deduced as shown in Fig. 3.5.

General FinTech Domains Figure 3.5 outlines the general domains of FinTech. As digitalization becomes the mainstream practice in the financial services industry, today’s FinTech innovations mainly focus on digital services as shown in Fig. 3.5.

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Platform ecosystems

Traditional financial institutions

Fintech start-ups

Elements of FinTech

Technology developers

Financial customers

Governments

Fig. 3.2 Elements of FinTech

Digital Payments/Electronic Payment The transfer of money from one payment account to another using a digital device such as a mobile phone, Point of Sales (POS) or computer, or a digital channel of communications such as mobile wireless data or Society for the Worldwide Interbank Financial Telecommunication is an example of a digital payment, which is also sometimes referred to as an electronic payment (SWIFT). It is possible to make payments through bank transfers, mobile money, and several types of payment cards, such as credit, debit, and prepaid cards. Digital payment can either be somewhat digital, mostly digital, or digital. A partially digital payment is one in which both the payer and payee use cash via third-party agents, and providers make digital bank transfers in the background of the transaction. A largely digital payment may be one in which the payer initiates the payment digitally to an agent who receives it digitally, but the payee receives the money in cash from that agent. This would be an example of a payment that might be considered to be primarily digital. Over the

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FinTechs

Asset Managment

Finanncing

Credit and Factoring

Alternative methods of payment

Insurance

Robo-Advice

Cryptocurren cies and Blockchain

Mobile Banking

Personal Financial Management

Online ecommerce purchases

Technology IT and Infrastructure

Investment and Banking

Online banking

Quick response code(QR Codes)

Digital wealth management

Peer-to-peer payments

Biometric Payments

Contactless payments

Central Bank Digital Currencies

Incentive based crowd funding Incentivebased crowdsourcing

Social lending

Other Fintechs

Social Trading

Crowdfunding

Crowdfunded donations

Payments

Fig. 3.3 Segments and elements

past two decades, there has been a tremendous shift in the landscape of payment options, from online payment methods such as mobile payment methods such as Apple Pay, AliPay, and WeChat Pay to cryptocurrencybased payment methods such as coin base (Xu, 2022). The use of cardless payment methods is rapidly becoming the standard in many nations (Xu, 2022; Ye et al., 2022). The global payment networks not only make it possible for merchants to access new prospects on a worldwide scale, but they also make it easier for customers to make seamless use of financial services via digital mobile devices. Digital Capital Raising The process of digitally raising capital is closely related to the process of digitally lending money, such as peer-to-peer lending; in fact, the

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•Cloud technology

•Robotic Process Automation

Open source software, serverless architecture, and software-as-aservice (SaaS)

Advanced Analytics

Smart Contracts

Block chain

•Artificial Intelligence

•No-code and low-code development platforms

Fig. 3.4 Building block technologies for, FinTech

two processes are frequently combined on the same platform (Butticè & Vismara, 2022; Tambe et al., 2020). One of the most fundamental methods of digital capital raising is known as “crowdfunding.” This is the practice of obtaining funding for a business or project by collecting monetary contributions of a relatively small amount from a large number of individuals. Once more, it is possible to divide it into two main categories: investment-based crowdsourcing and non-investment-based crowdfunding. The former typically involves topics such as equity, revenue, profit sharing, real estate, and commodity, whereas the latter frequently involves topics such as donations and/or rewards. The use of digital tokens supported by blockchain technology has become a popular method of crowdfunding. Both issuers and investors are shifting their focus to various digital capital-raising options that can comply with the regulations that are now in place around the world as regulators in those jurisdictions begin to prescribe regulatory frameworks for digital tokens. Some platforms, like Globacap and Realbocks, fall into this category;

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Digital payments/Electronic Payment Digital lending Digital capital raising Digital savings Wealth management and investment Insurance Digital currencies Digital asset and consensus services

Fig. 3.5 General FinTech domains

they can offer unified solutions for the digital capital-raising market by combining Blockchain technology and machine learning (ML) with their offerings (Xu, 2022). Digital Lending The administration and processing of loans conducted over the Internet or via mobile devices are referred to as digital lending. The use of technologies to originate and renew loans, deliver faster services and decisions, better customer experience, and more cost-effective solutions is another definition of digital lending (Cornelli et al., 2023; Xu, 2022; Ye et al., 2022). Incorporating cutting-edge technologies such as cloud computing and data analytics helps expeditiously automate the process of applying for a loan. The process of digitalization begins with the collection of information, such as the online application form and the credit file. This is followed by information processing, such as Optical Character Recognition for identification documents and entity identification for Know Your Customer. Finally, the process encompasses decision-making data insight based on both rules and AI models, as well as loan collection activities such as loan status monitoring, auto nonfiction, and auto-calling.

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Multiple models, including transaction-based lending at points of sale, marketplace lending, and peer-to-peer (P2P) lending, can be utilized in the construction of digital lending platforms, which can be developed by banks, non-banking financial organizations, and FinTech companies. To accommodate the requirements of the future generation of customers, the services are provided with a greater degree of individualization (Xu, 2022; Ye et al., 2022). To put it another way, specialized digital lenders are trying to understand the specific needs of their customers to personalize their loan requirements on a large scale, particularly for the poor, the majority of whom live in rural areas, which helps to increase the level of financial inclusion. Digital Savings Customers can open, monitor, and transact through their accounts from the convenience and security of their own homes while using a digital savings account, which is extremely important during the Pandemic (Xu, 2022). Traditional savings accounts are sometimes less flexible and slower than their digital counterparts, which is why many people prefer digital savings options. In many instances, a customer’s first account is a digital savings account, and these accounts typically give a higher yield than those offered by traditional banks with physical locations. Certain online services combine the capabilities of budgeting, investing, saving money, and borrowing money into a single wealth management application. The holder of a digital savings account has complete access to all of the account’s features and perks, regardless of where they are or what time it is, 24 hours a day, seven days a week. However, to maintain or run a Regular Savings Account, the account holder is required to physically visit the branch of the bank during normal business hours and days. Wealth Management and Investment Wealth managers are often responsible for providing clients with a comprehensive variety of professional financial services, including investment advice and general financial planning. In essence, these advisers with multiple skills guide clients who are wanting to manage both their portfolios and their money. The application of technology in the financial sector, known as FinTech, is making its influence felt now by developing digital solutions that are transforming the investment asset management

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industry (Beketov et al., 2018; Phoon & Koh, 2017; Xu, 2022). The advent of robo-advisors has, in a sense, altered the relationship that traditional financial advisors have with the people they serve. A financial adviser traditionally acts as a consultant for clients seeking guidance on investment management, wealth management, and many other financial services, for which they are charged a fee. When it comes to providing financial planning and investment advice, robo-advisors rely on artificial intelligence and machine learning. When compared to traditional investment advisors, the services provided by robo-advisors are typically the same or more advanced than those offered by traditional investment advisors, but at a lower cost. Again, FinTech makes it possible for wealth managers to improve their service, which has resulted in the emergence of an altogether new skill set. Wealth technology, which is frequently powered by artificial intelligence (AI) and machine learning (ML), employs complicated algorithms to advise clients on the most advantageous investment or savings strategies, with only a limited amount of involvement from humans. These online programmes use algorithms to calculate and choose investments depending on the potential clients’ chosen risks and objectives, so taking on the job of human advisers in the field of FinTech wealth management and replacing the need for those advisors. As a consequence of this, they are often able to perform the same functions as a wealth manager while charging far less money for their services. Robo-advisors reduce the amount of human interaction required and spread the expense of portfolio management across a larger number of clients, making them a more cost-effective choice. The other significant aspect of FinTech is retirement, which is essentially a subclass of robo-advisors. These digital platforms specifically focus on retirement investing and are considered to be an important part of the retirement industry. However, robo-advisors are not without their flaws and restrictions. For example, in retirement, they use algorithms to recommend investments based on a client’s desired outcomes and risk profile, and there are restrictions when it comes to customizing their portfolio. Clients won’t be able to tailor their investment portfolio to focus on specific companies, nor will they be able to enjoy personal human interaction with an advisor who keeps specific preferences in mind. In addition, there are restrictions when it comes to customizing their portfolio. Again, even though robo-advisors are widely regarded as the superior choice for straightforward financial choices, it’s possible that they aren’t the ideal fit for more

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involved choices like those involved in estate planning. In actuality, roboadvisors provide users with a limited number of well-defined options for how to invest their money. A wealth manager, on the other hand, welcomes and values the opinion of their clients and may, for instance, steer them away from investing in particular markets that the client deems risky. Insurance Insurtech is short for “insurance technology,” and it refers to the application of technological advancements that are meant to find cost savings and efficiency within the existing model of the insurance sector (Hargrave, 2022). The term FinTech served as the basis for the creation of the phrase insurtech, which is a combination of the words “insurance” and “technology” (Hargrave, 2022). In other words, InsureTech is the term that is used when FinTech is employed in the insurance industry. This includes the use of technology innovations to improve the efficiency of the current players in the insurance industry, such as direct insurers, tied agents, smart insurance planers and management, insurance marketplaces aggregators, P2P insurance, and many others. InsureTech is a term that is used when FinTech is employed in the insurance industry (Gomber et al., 2017; Vriens & De Moor, 2020). The rise of the sharing economy coincides with the expansion of the P2P insurance networks. Friendsurance, the world’s first peer-to-peer (P2P) insurance firm, was established in Germany in 2011. Since that time, numerous other insurance companies, like Guevara, Lemonade, Uvamo, and insPeer, have also established P2P insurance networks. In general, they work with three distinct categories of clients: those who insure their risks, those who cover risks for other people, and those who insure risks for other people in exchange for a premium. Some digitalized insurers, such as Clover Health, ZhongAn Insurance, Root Insurance, PolicyBazaar, Lemonade, Acko, PolicyStree, SingLife, and DirectAsia, aim to provide customers with individualized insurance policies that are both affordable and flexible (Xu, 2022). In general, they make use of AI, Big Data analytics, Blockchain, and IoT to construct consumer profiles, improve risk management, swiftly optimize product features, and increase customer experience (Xu, 2022).

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Digital Currency—Cryptocurrencies and Central Bank Digital Currency (CBDC) The term “digital currency” refers to a sort of money that is solely accessible in digital or electronic format. It is also known as “cybercast” and “digital money.” All cryptocurrencies are digital currencies, but not all digital currencies are crypto (Xu, 2022). The primary characteristic of cryptocurrencies is decentralization, which refers to the fact that the currency in question is neither managed nor issued by any central body, like traditional central banks. Encryption is the basic organizing concept of virtual currency, hence its name (Xu, 2022). The code or hash that is exclusive to each unit acts as a unique identification for those units. Each unit has its code or hash. Since the introduction of the first cryptocurrency, Bitcoin, the market has witnessed the emergence of thousands of additional cryptocurrencies. Some examples of these include Ethereum, LiteCoin, PeerCoin, EOS, and Cardano (Xu, 2022). Even though confidence in cryptocurrencies is growing for instance, electric automobile manufacturer Tesla accepts Bitcoin as payment for car purchases and the value of cryptocurrencies is hitting new highs, many central banks and monetary authorities have imposed stricter regulations to prevent potential risks such as money laundering and tax evasion. Tesla accepts Bitcoin as payment for car purchases (Arli et al., 2020; Xu, 2022). On the other hand, the CBDC is attracting increasing attention because it is issued by central banks in a regulated form. We are not going to get into a discussion about which type of digital currency is better than the other in this book because the existence of both types serves a useful purpose. In addition, it is equally important to take note that tokens are the most essential kind of digital currency and to be called a digital currency, a token needs to fulfil several requirements first. It is necessary for there to be a central party, such as a central bank, that is responsible for issuing the monetary unit in question. Again, it must gain acceptance from an ecosystem of participants before it can be used to buy and sell goods and services and it should be simple to trade and store (Xu, 2022). Digital Asset and Consensus Services The term “digital asset” refers to a broad category that incorporates financial instruments such as digital currency, funds, private equity, and commercial real estate. In its broadest sense, it refers to any kind of digital

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data that can be encrypted and kept on a Blockchain. The majority of the time, it is connected to a tangible asset, such as a bond, stock, car, or another item. Not only does it cover cryptocurrency and CBDC, but it also covers software, contracts, and any other sort of data that might be interpreted as a representation of ownership right. Keep in mind that careful consideration must also be given to the storage of digital assets (Xu, 2022).

Open Application Programming Interfaces (API) A publicly available application programming interface (API) gives developers programmatic access to a web service or a proprietary software application. Open application programming interfaces make it possible for new service providers to create their products on top of current infrastructure. The significance of these techniques lies in the fact that they decrease the barriers to entry for new companies in the financial technology industry. This, in turn, encourages innovation and makes it possible for end users to benefit from increasingly frictionless digital payment systems. Distributed Ledger Technology (DLT) A database that is shared and synced across different locations, institutions, or geographical areas with the consensus of those involved. This database architecture provides a solution to the problem of trust among numerous stakeholders as well as the so-called “double spend” problem, which refers to the challenge of guaranteeing that a digital asset is not spent twice. DLT enables decentralized digital payment systems that do not depend on a single central authority, such as a bank or a public organization, because all users of the network always retain a copy of the ledger. Examples of such authorities include governments and financial institutions. QR Codes A Quick Response bar code or square-shaped code that is twodimensional and carries data. It is a fast and simple method of exchanging information, and it has the potential to significantly reduce the expenses associated with accepting payments, all of which have contributed to its

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rise in popularity. A digital gadget equipped with a camera and connected to an account is all that is necessary for the transaction to be completed successfully. Biometric Payments Payment verification and authorization can be accomplished through the use of biometric identification in the context of biometric digital payments. The term “biometric identification” refers to any method by which a person can be identified in a one-of-a-kind manner by analysing one or more differentiating biological characteristics. A person’s fingerprints, hand geometry, earlobe geometry, retinal and iris patterns, voice waves, DNA, and signatures are all examples of distinctive identifiers.

History of FinTech A banker from New York is credited with being the one who first used the term “financial technology” in 1972. Companies that are considered to be a part of this sector provide services such as payment options, online marketplace lending, mobile apps, financing, foreign exchange and remittances, investments, distributed ledger technology, digital currencies, mobile wallets, artificial intelligence and robotics in finance, crowdfunding, insurance, and wealth management. An expanded definition of this sector is considered to include ancillary technology solutions targeted at financial services, such as digital asset wallets (RegTech). As a result of this, the sector of the economy that deals with financial services has become highly impacted and influenced by growing trends that are enabled by technological advancements and that encourage innovation. According to Arner et al. (2015), FinTech is frequently viewed as the new marriage of financial services and information technology in the modern world. Despite this, the relationship between the worlds of finance and technology dates back a long time and has developed in three major phases. The period between 1866 and 1987 known as FinTech 1.0 was the first time that globalization of the financial sector was enabled by technological infrastructure such as transatlantic transmission cables (Arner et al., 2015; Blakstad & Allen, 2018; Mhlanga, 2022a). This was then followed by the FinTech 2.0 period, which lasted from 1987 to 2008 and saw an increase in the level of digitization utilized by financial services companies. Since 2008, a new age of FinTech has evolved in both the

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FinTech 1.0 1865-1966 Trans Atlantic Cable Fedwire in the USA The first electronic fund transfer system

Fintech 2.0 1967-2008 Era of splendor Digitisation and globalisation First Digital Bank

Fintech 3.0 Fintech 3.5 2008-2014 Global Financial 2014-2017 Crisis Traditionl Financial expansion in digital System got changed banking around the globe Improvements in fintech technology Increasing number of new entrants

Fintech 4.0 2018-Till now Disruptive technologies Artificial Intelleigence, Blockchain technologies and open banking are continuing to drive the innovation. Fintech is rapidly expanding.

Fig. 3.6 FinTech evolution

developed and developing globe. This era is not defined by the financial products or services that are offered; rather, it is defined by who distributes those products and services. This most recent iteration of FinTech, which is being driven by start-ups, creates issues for regulators as well as market participants, in particular when it comes to striking a balance between the possible benefits of innovation and the probable risks of new approaches (Arner et al., 2015). Figure 3.6 outlines the evolution of FinTech from FinTech 4.0 up to FinTech 4.0 as propounded by Arner et al. (2015), Zigurat (2022) and Zeidy (2022) among others. FinTech 1.0 (1886–1967) FinTech 1.0 is predicated primarily on existing infrastructure, and it is within this period that we may for the first time begin to discuss financial globalization (Zeidy, 2022; Zigurat, 2022). It began with innovations in technology such as the telegraph, which, along with railroads and steamships, made it possible for the first time for financial information to be transmitted quickly across international borders. This timeline’s most important events include the laying of the first transatlantic cable in 1866

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and the establishment of Fedwire in the United States in 1918. Fedwire was the first electronic fund transfer system and relied on now-obsolete technology like the telegraph and Morse code. The origins of FinTech can be traced back to the nineteenth century and even further. In the year 1860, a device known as the PENTELEGRAPH was created to validate signatures by financial institutions. The first transatlantic cables were laid in 1866, marking the beginning of an era characterized by the establishment of network infrastructure and linkages all over the world. The first baby step towards the digitalization of money was taken in 1918 when Fedwire established electronic fund transmission using Telegraph and Morse code. This was the first baby step. Credit cards were first introduced in the 1950s, making the burden of carrying cash much lighter. Diner’s Club was the first to market its credit card in 1950, and the American Express Company launched their product the following year in 1958 (Zigurat, 2022). The two World Wars also saw a new set of coders and codebreakers emerge, primarily for the sake of the military. Despite this, the concept of coding and future digital progress was established at this time. The publishing of the book “The Economic Consequences of Peace” in 1919 is the first idea on the FinTech-driven future, and in general, FinTech historians ignore one significant and life-altering event of FinTech 1.0, and that event is the introduction of Diner’s Cards in 1950 (Zeidy, 2022). Even though the service initially only applied to payments made at restaurants, this was the first genuine attempt to make it possible to make purchases without using cash. This was then followed by the establishment of the American Express Credit Card in the year 1958. In 1960, Quotron was the first company to introduce screen-based stock data, which was a significant step forward for the financial market (Arner et al., 2015, Zigurat, 2022; Zeidy, 2022). FinTech 2.0 (1967–2008) The focus of FinTech 2.0 is on banks. Traditional financial institutions are the ones who are spearheading the transition from analogue to digital during this period. In 1967, the contemporary era of financial technology got its start with the introduction of the first handheld calculator and the first automated teller machine, both of which were installed by Barclays bank (Zigurat, 2022). Several important developments started to take shape in the early 1970s, such as the launch of NASDAQ, the world’s

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first digitized stock exchange, which marked the beginning of how the financial markets operate today. This development was only one of several that took shape during this period. Significantly modernizing the process of going public for the first time, sometimes known as an IPO, was one of the effects of this transition. This is widely regarded as one of the most significant advancements in financial technology that has ever taken place. The society for Worldwide Interbank Financial Telecommunications (SWIFT) was established in 1973. It is the first and the most commonly used communication protocol between financial institutions, and it helps facilitate a large volume of cross-border payments. SWIFT was named after the acronym for “Society for Worldwide Interbank Financial Telecommunications.” The 1980s was the decade that saw the rise of bank mainframe computers, and the 1990s was the decade that saw the flourishing of online banking thanks to the Internet and the development of e-commerce business models. People’s attitudes towards money and their connections to financial institutions underwent a profound transformation as a direct result of the rise of online banking. In 1982, Trade plus, also known as E-trade, was the company that pioneered the electronic trading market. 1983 was the year that also saw the introduction of mobile phones for the very first time. The development of more advanced computing systems facilitated the introduction of processes and goods that were both more innovative and dynamic. The development of electronic commerce in the middle of the 1990s was a big step forward that contributed to the growing importance of digital financial systems. In 1998, the online payment system known as PAYPAL was established; it would go on to become an industry leader in the years to come. The dotcom bubble of the year 2000 eventually broke, and the years that followed witnessed a rapid development of technology in the financial sectors. This development was mostly employed by traditional banks as a support function to their major channels. At the turn of the twenty-first century, all of the activities that took place within a bank, including its internal operations as well as its interactions with outside parties and retail clients, had been completely digitized. This era came to an end in 2008 with the onset of the global financial crisis. The global financial crisis of 2008 prompted a significant shift in perspective about the FinTech industry, and the resulting need for innovation paved the way for the industry to see a genuine boom in the years that followed.

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FinTech 3.0 (2008–2014) Start-ups are the focus of FinTech 3.0. The public’s mistrust of the traditional banking system grew as their understanding of the causes of the Global Financial Catastrophe, which quickly mutated into a broader economic crisis, increased. This was because the causes of the crisis were first poorly understood. This, together with the fact that a large number of financial experts were without jobs, led to a paradigm shift, which in turn opened the way for the birth of a new industry known as FinTech 3.0 (Zigurat, 2022). As a result, this age is distinguished by the appearance of new companies, particularly start-ups in the financial technology industry, in addition to the players that already existed such as banks. The introduction of Bitcoin in 2009 is another event that has had a significant influence on the world of finance. It was quickly followed by the boom of several cryptocurrencies, which was then followed by the big crypto crisis in 2018. The creation of Bitcoin in 2009, the world’s first cryptocurrency, and the introduction of peer-to-peer payment systems in 2011 were both momentous occasions. The widespread adoption of smartphones, which has increased the number of individuals throughout the world who have access to the Internet, is another significant element that has played a significant role in the development of FinTech. The smartphone has also emerged as the most common device through which individuals access the Internet and make use of a variety of financial services. Google Wallet was first made available to users in 2011, whereas Apple Pay wasn’t made available until 2014 (Zeidy, 2022; Zigurat, 2022). FinTech 3.5 (2014–2017) The Focus of FinTech 3.5 Is on Internationalization FinTech 3.5 heralds a shift away from the western-dominated financial sector and ponders the development of digital banking across the world as a result of advancements in FinTech technology. This move indicates the beginning of FinTech’s third iteration. The actions of consumers and how they connect to the Internet in developing countries become the centre of attention as a result. This era is distinguished by the growing number of new entrants and the advantages enjoyed by those who were the last to enter the market. China and India, which have the world’s two largest populations, have seen nonlinear growth in their financial

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technology industries since the year 2014 and onward. The financial technology industry in these two countries expanded at a breakneck speed due to the absence of huge banking conglomerates and complicated physical infrastructures. This, combined with the improvements that have been made in FinTech in Africa, is expected to be the growth engine for 2014– 2018. As a result of this, numerous innovative financial services have emerged across the globe, including M-Pesa in Africa, payment banks in India, and Alipay in China, to name just a few (Mhlanga, 2022d; Zeidy, 2022; Zigurat, 2022). FinTech 4.0 (2018–today) Disruptive technologies are at the centre of FinTech 4.0. Both open banking and blockchain technologies are continuing to be major drivers of the innovation that will shape the future of financial services. Neo banks are the ones that are going to change the game here because they are challenging the pricing and complexity of traditional banks while also earning the trust of clients with streamlined, digital-only experiences that cost little to nothing. “Neo” is Greek for “new.” These are cuttingedge financial institutions that are completely virtual and do not have any physical branches. They offer digital and mobile-first financial solutions for a variety of transactions, including payments, money transfers, and lending. Customers can deposit and withdraw money from their accounts. In addition to other services, they provide debit cards and investment facilities. Machine learning, on the other hand, is changing the way customers engage with financial institutions like banks and insurance firms to receive tailored offers and assistance. For instance, in 2019, the German financial institution N26 reintroduced its premium account to adapt to the individual requirements and preferences of its users. This included providing discounts at coworking spaces and online vacation booking sites (Zigurat, 2022). Machine learning also has applications in the security sector to combat card fraud and money laundering. These applications involve the development of in-depth insights and predictions regarding customer behaviour to dynamically identify new card fraud patterns without the intervention of humans. Another significant development during this period is the emergence of a new generation of integrated payment providers. These providers have developed platforms that enable them to deliver payment services as an additional component of existing extensive company management systems.

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FinTech Today We tend to see banks and FinTech companies as competing entities that are vying for their respective portions of the market because technology is playing an increasingly important role in the financial industry. The truth is that both camps are dependent upon one another just as much as they are on their respective competitors’ websites (Zigurat, 2022). On the one hand, FinTech start-ups have received finance from traditional financial institutions and frequently rely on partners in the banking, insurance, and back-office industries to supply their primary products. On the other side, banks have been making investments in or acquisitions of FinTech companies to take advantage of new forms of technology and methods of thinking to improve their present operations and products. There is no doubt about it, the financial technology industry is expanding at a rapid rate. In addition, innovation in the financial technology sector is spreading to an increasing number of facets of the digital economy. The major argument of this book on FinTech is that FinTech may broaden access to financial services, which is vital for achieving several of the Sustainable Development Goals (SDGs).

Chapter Summary The purpose of this chapter was to provide an overview of FinTech. The term “FinTech,” which stands for “financial technology,” refers to the digitization of traditional financial services such as those provided by banks, credit card companies, credit unions, investment banks, and other businesses operating in the financial sector. The traditional method of handling financial transactions is being increasingly eclipsed by a new system called FinTech, which stands for financial technology. At its core, FinTech is disruptive because it leads to the emergence of numerous new kinds of financial firms, each of which has an ecosystem that can support itself. FinTech is seen as a key factor in the expansion of the economy over the long term because it is an emerging industry that possesses characteristics that set it apart from the more traditional financial sector. Because of the optimistic projections made regarding the development of FinTech in the coming years, investors from all over the world have increased their financial commitments to the industry.

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Mhlanga, D. (2022c). Digital financial inclusion in the Fourth Industrial Revolution. In Digital financial inclusion. Palgrave Studies in Impact Finance. Palgrave Macmillan. https://doi.org/10.1007/978-3-031-16687-7_7 Mhlanga, D. (2022d). COVID-19 and digital financial inclusion: Policies and innovation that can accelerate financial inclusion in a post-COVID world through FinTech. African Journal of Development Studies, 2022(si2), 79. Phoon, K., & Koh, F. (2017). Robo-advisors and wealth management. The Journal of Alternative Investments, 20(3), 79–94. Tambe, P., Hitt, L., Rock, D., & Brynjolfsson, E. (2020). Digital capital and superstar firms (No. w28285). National Bureau of Economic Research. Vijai, C. (2019). FinTech in India—Opportunities and challenges. SAARJ Journal on Banking & Insurance Research (SJBIR), 8(1), 42–54. Vriens, E., & De Moor, T. (2020). Mutuals on the move: Exclusion processes in the welfare state and the rediscovery of mutualism. Social Inclusion, 8(1), 225–237. Xu, J. (2022). FinTech innovation and strategy. In The future and FinTech: ABCDI and beyond (pp. 1–36). World Scientific. https://doi.org/10.1142/ 12686 Ye, Y., Chen, S., & Li, C. (2022). Financial technology as a driver of poverty alleviation in China: Evidence from an innovative regression approach. Journal of Innovation & Knowledge, 7 (1), 100164. Zeidy, I. A. (2022). The role of financial technology (FinTech) in changing financial industry and increasing efficiency in the economy. https://www.comesa. int/wp-content/uploads/2022/05/The-Role-of-Financial-Technology.pdf Zigurat. (2022). Evolution of FinTech: The 5 key eras. https://www.e-zigurat. com/innovation-school/blog/evolution-of-FinTech/

CHAPTER 4

A Historical Perspective on Sustainable Development and the Sustainable Development Goals

Introduction The concept of sustainable development can be summed up as “development that meets the requirements of the current generation without compromising the ability of future generations to meet their own needs” (Mhlanga, 2022a). The term “sustainable development” refers to a practice that “incorporates two major constructs: the needs of the poor, especially their basic needs, that must be made a priority, and the limitations placed on the environment’s capacity to meet current and future expectations by the state of technological and social organization” (Mhlanga, 2022a). According to Duran et al. (2015), “sustainable development” is best understood as “the juxtaposition of two major characteristics.” According to Duran et al. (2015), “the first phrase, durable, alludes to long-term viability and sustainability while development describes the process of expanding or developing one’s capacity; gradually bringing one’s possibilities to a fuller, larger, or better condition.” The term “sustainability” refers to a multifaceted strategy that is currently being debated at a time when environmental issues resulting from numerous emission of greenhouse gases and at the same time the inescapable changes caused by the Fourth Industrial Revolution, demand immediate action and remedies. The term “sustainability” refers to the strategy that is being debated

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at a time when environmental concerns caused by numerous industrial activities and at the same time the unavoidable changes. According to Dasgupta (2007), the term “sustainability” rose to prominence after the World Commission on Environment and Development published the Brundtland Commission Report. This report defined sustainable development as “development that meets current needs without jeopardizing future generations’ ability to meet their own needs,” as defined by Mhlanga(2022b). This report also contributed to the rise in popularity of the term “sustainability.” Dasgupta (2007) went on to say that “the concept of sustainability is that each generation should leave its successor at least as large a productive foundation as it acquired from its predecessor in terms of their respective demographic bases.” In that case, “the successor’s economic prospects would be no worse than those it had when inheriting capital assets from its predecessor,” which is a very positive prognosis (Dasgupta, 2007). The productive basis of the country is comprised of cultural coordinates, as well as financial help and institutional support. Big data, a post-industrial possibility that is fueling the development of AI and is sometimes referred to as the new oil of the twenty-first century, has been incorporated, however, into the country’s productive capabilities as a result of the ongoing revolution. This has allowed the country to extend its productive potential. The point that we are trying to make with this post is that data and AI need to be implemented with people in mind for them to be effective as new drivers of development and as part of the productive basis. Since the productive foundation of the country is the source of its well-being, the people must be at the centre of the deployment of these resources to achieve sustainable development. This is a requirement that must be met to achieve sustainable development. According to the ideas presented by Rees (1989), “Sustainable development is the good socio-economic progress that does not damage the natural and social systems upon which communities and society are dependent.” Its successful implementation involves integrated policy, planning, and social learning processes, and its political viability depends on the complete support of the people it touches through their governments, their social institutions, and their private activities. Because of this, it is abundantly evident that the problem of applying AI in a way that is centred on people is given top priority. This is because if this issue is not resolved, achieving sustainable development will be difficult. Tomislav (2018) proposed that ever since it was first

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conceived, the idea of “sustainable development” has progressed through several distinct stages of development. The idea has developed throughout time to accommodate the requirements of a more sophisticated global ecosystem, but the fundamental ideas and goals, in addition to the difficulties involved in putting them into practice, have remained roughly the same (Tomislav, 2018). This is the key justification for why we should start having conversations about things like AI that are oriented around people. According to Redclift (1992), the idea of sustainability is commonly confused. Several academics are concerned about the viability of the natural resource base, whereas others are concerned about the levels of output and consumption that are either occurring now or will occur in the future (Redclift, 1992). According to Redclift (1992), there are considerable differences of opinion regarding the best way to ensure environmental sustainability and achieve sustainable development. Therefore, it is vital to research the many different facets of sustainability on their own, as well as the kind of global policies that would be required to achieve sustainable development.

History Behind Sustainable Development The publication of Our Common Future in 1987 was a pivotal moment in the evolution of ideas on environmental policy, economic growth, and political structure (Sneddon et al., 2006). The World Commission on Environment and Development (WCED), which was sponsored by the United Nations (UN), and was led by Gro Harlem Brundtland, issued a bold call to recalibrate institutional mechanisms at global, national, and local levels to promote economic development that would guarantee “the security, well-being, and very survival of the planet.” This call was issued in response to a report that was issued by the World Commission on Environment and Development (WCED) (Mhlanga, 2022c; Sneddon et al., 2006). The call for sustainable development was a realistic answer to the issues that were prevalent during that era, and it was a reorientation of the Enlightenment goal. If we take a close look at the conferences that took place between 1972 and 2002, we can see that there was a shift in the political debate from a primary emphasis on environmental issues at the 1972 Stockholm Conference, to a shared focus on environmental, social, and economic development at the Rio de Janeiro Earth Summit in 1992, to what could be considered a primary emphasis on poverty alleviation at the Millennium Summit in 2000 and at the Johannesburg World Summit

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in 2002. This shift can be attributed to the fact that there was a shift (Sneddon et al., 2006). This does not necessarily mean that the protection of the environment has been effectively pushed to the background, with the primary emphasis being on its capacity to alleviate poverty. Rather, what began as a demand to safeguard the environment in the service of human progress has evolved into a more particular appeal to prioritize increases in the well-being of the very worst-off people both in the now and in the future. The most significant obstacle to sustainable development is still raising awareness across the globe, from individual households to corporate boardrooms, about the significance of addressing the challenges posed by the Industrial Revolution, which include limitless human and environmental exploitation. Sustainable development was first brought to the attention of the international community in 1987 by the Report of the World Commission on Environment and Development (WCED). In 1992, at the Rio Earth Summit, political leaders from more than one hundred and seventy countries formally endorsed the concept of sustainable development (Meadowcroft, 2000). The word “sustainable development” was not initially coined by the WCED; rather, it was this organization that gave the phrase reasonable content and a heavy dose of credibility. Noting that a large portion of the world’s population was still living in poverty, that there were serious vast differences in trends of resource use among rich and poor nations, and that worldwide ecosystems were already actually suffering extreme stress, it called for an international consensus to re-orient economic activity to prioritize the immediate developmental needs of the poor and to prevent irreversible damage to the global environment. In addition, it noted that there were grave disparities in patterns of resource use between rich and poor countries (Meadowcroft, 2000). The World Commission on Environment and Development (WCED) proposed a “new era of growth” as a solution to the problems of poverty and underdevelopment. This plan called for more industrialized states to make a concerted effort to improve the efficiency of their use of energy and materials, as well as to refocus economic activity along lines that were less taxing on the environment. What was necessary was for all nations to make a unified commitment to ensuring that their respective forms of development are “sustainable.” In a phrase that has since become well-known, the World Commission on Environment and Development (WCED) Report defined sustainable development as development that

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satisfies the requirements of the present without impairing the capacity of future generations to satisfy their own needs. It is comprised of two fundamental ideas: the first is the notion of “needs,” more specifically the fundamental requirements of the world’s impoverished population, which should take precedence over all other considerations; the second is the concept of limitations that are imposed by the current level of technological development and social organization on the ability of the environment to meet both current and future requirements (Meadowcroft, 2000). According to Paul (2008), sustainable development has emerged as a prevalent topic of discussion in both the business world and the academic community. Again Paul (2008) argued that the concept of sustainability has been present in scholarly papers, the curricula of faculties, the boardrooms of local authorities and enterprises, and the offices of public relations officers for the past several decades. Unfortunately, sustainability has become a “fashionable” notion in principle, but it is regarded as exceedingly expensive to put into practice by major enterprises, firms, and local or national governments. This is the case even though sustainability has become “fashionable.” The progression of the idea of sustainability is something that most people fail to appreciate and remember, though. It’s possible that knowing a concept’s background and how it’s developed over time, even though it might not seem relevant at first, can help us anticipate future trends and problems. Additionally, it will assist us in ensuring that the twenty-first century will be known as “the Century of Sustainability.”

The Emergence of the Concept The first genuine worldwide conference to be dedicated solely to environmental concerns took place in Stockholm, Sweden in 1972 and was called the Meeting on the Human Environment. This conference was attended by representatives from 113 states and 19 international organizations. There, a group of 27 environmental and development specialists outlined the connections between the two, indicating that “while in certain cases there were tensions between environmental and economic concerns, they were fundamentally two sides of the same coin” (Paul, 2008). Another outcome of the Stockholm Conference was the establishment of the United Nations Environment Programme (UNEP), whose mission is “to provide leadership and encourage partnership in protecting the environment by encouraging, informing, and empowering nations

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and individuals to enhance their quality of life without making compromises that of future generations.” The UNEP was established as a direct result of the Stockholm Conference (Paul, 2008; Singh, 2016a, 2016b). The following passage of international accords concerning ocean dumping, pollution from ships, and the trade in endangered species were largely facilitated as a direct result of the catalytic role that this meeting performed (Prizzia, 2017). It also adopted the “Stockholm Declaration on the Human Environment,” which included forward-looking principles such as Principle 13,167, which declared the necessity for integration and coordination in development planning to allow for environmental protection. This was done to allow for the protection of the human environment (Paul, 2008; Singh, 2016a, 2016b). However, the effectiveness of the conference in Stockholm was limited since environmental protection and the need for development, particularly in developing countries, were seen as competing needs and were therefore dealt with in a separate, uncoordinated manner at the conference. This was especially true in developing countries. Some detractors concluded that the conference was more focused on finding ways to balance economic growth and environmental protection than it was on fostering harmonic connections between the two (Paul, 2008; Singh, 2016a, 2016b). After the summit in Stockholm, even documents produced by the United Nations acknowledged the fact that little had been accomplished to concretely integrate environmental concerns into development strategies and plans. It was abundantly evident that a more integrated perspective was required, one that addressed both concerns regarding economic development and environmental sensitivity. The World Commission on Environment and Development was established in 1983 by the United Nations General Assembly. It was later renamed the Brundtland Commission after its chairperson, Gro Harlem Brundtland, who served as the Prime Minister of Norway at the time and went on to become the Director General of the World Health Organization. The Brundtland Report, also known as Our Common Future, was distributed by the Commission in the year 1987. It built upon what had been accomplished at Stockholm and provided the most politically significant of all definitions of sustainable development: “sustainable development is the development that meets the needs of the present without compromising the capacity of future generation to generation to meet their own needs as articulated before.” The concept of sustainability in the Brundtland Commission Report has been criticized by some

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for being overly optimistic and lacking in clarity. The Commission likely believed that, for the discussion to be accepted, it needed to be hopeful; nevertheless, given the circumstances, it was important for it to be imprecise and inconsistent to avoid giving the impression that it was pessimistic (Paul, 2008). The next thing that took place was the United Nations Conference on the Environment and Development (UNCED), which took place in Rio de Janeiro, Brazil, during the summer of 1992. This was an unprecedented historical event, as it featured the largest gathering of 114 heads of state, as well as 10,000 representatives from 178 countries and 1400 non-governmental organizations represented by additional thousands of people (Paul, 2008; Singh, 2016a, 2016b). The conference in and of itself turned out to be an international event of a size that had never been seen before, with heads of state vying for the opportunity to leave their mark on what became known as the Rio Earth Summit. The conjunction of “linking Environment and Development” in the title was suggestive of North–South bargaining at the United Nations, in which desires for increased development aid and technology transfer were put against demands for international action on the environment. The Rio Declaration 172, Agenda 211 73, and the Commission on Sustainable Development 174 were the three most important products that came out of the Conference. As a result of the fact that all of these issues are directly related to sustainable development, the completion of the Earth Summit marks the official debut of this idea on the global stage. Agenda 21, the key document of the summit, is a collection of agreed-upon healthy practices and bits of advice for achieving sustainable development in almost any area on the surface of the earth. It clearly articulates the commitment of leaders from around the world to sustainable development, which was clearly articulated in the document. Activities related to Agenda 21 are categorized according to the following environmental and economic development themes: quality of life, protection of the global commons, efficient use of natural resources, management of human settlements, and sustainable economic growth. It acknowledges that maintaining extreme poverty in some regions of the world while maintaining a standard of living that is based on the wasteful consumption of resources in other regions is not a model that can be sustained, and that environmental management must be practised in both developing countries and industrialized countries alike.

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At the summit in 1992, it was decided that for countries to successfully implement Agenda 21, they should first draft a national strategy for sustainable development. Although sustainable development served as the organizing premise for the whole Rio Summit, there was much debate on the concept’s definition and the repercussions it could have. The UNCED process tried to offer direction in the process of putting sustainable development into practice by outlining a set of guiding principles as well as a plan of action that was based on the concept. The debate in Rio focused less on the notion of sustainable development and more on the construction of mechanisms to ensure that it is put into practice. Some detractors argue that “implementing the equity principles and living within ecological limits can only be achieved if social, political, and economic systems have the adaptability to be redirected toward sustainability as well as integrated and the environment.” This is because, according to these detractors, “implementing the equity principles and living within environmental limits can only be achieved if social, political, and economic systems have the flexibility to be applied in different settings.” At the climate change conference that took place in Kyoto in 1997, developed countries agreed on specific goals for reducing their emissions of greenhouse gases. This led to the creation of an overarching framework that came to be known as the Kyoto Protocol, with specifics to be worked out throughout the following few years. The European Union demanded a reduction of 15% in emissions, whereas the United States proposed just to maintain their current level and not to lower them at all. At the end of the day, a compromise was reached, and developed nations agreed to cut their emissions of greenhouse gases by an average of 5.2% below their levels in 1990 throughout the period from 2008 to 2012. However, due to the intricacy of the negotiations, there was a great deal of confusion regarding compliance even after the Kyoto Protocol itself was ratified. This was because the protocol only outlined the fundamental components of compliance but did not explain the extremely important rules governing how they would function. Even though 84 countries signed the Protocol, signalling their intention to ratify it, many other countries were hesitant to even take this first step. At the Millennium Summit that was held in New York in September of 2000, the leaders of the globe came to an agreement on the 173 Millennium Development Goals, the majority of which have the year 2015 as a deadline and utilize the year 1990 as a benchmark. These objectives are both

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attainable and worthy of pursuit. “the livelihoods and well-being of the world’s poor are increasingly conceived in terms of access to opportunity and lack of instability and vulnerability,” as demonstrated by the Millennium Development Goals (MDGs). They are an example of a principle of sustainable development that emphasizes maintaining a balance between its three pillars economic, social, and environmental in a way that is more applicable to real-world situations. They include halving the number of people who live on less than a dollar a day and those who suffer from hunger, accomplishing primary education for all and advancing gender equality, lowering child mortality and trying to improve maternal health, trying to reverse the spread of HIV/AIDS, and increasing access to clean water and sanitation for all. Incorporating the concepts of sustainable development into the policies of the country, to cut in half the number of people who do not have access to clean drinking water. Unfortunately, the world still needs to address “this dangerous blend of indifference and concealment” and ultimately rebuild the trust between people, businesses, and the government. This is something that is desperately needed if we are going to have any chance of achieving the Millennium Development Goals by the year 2015, which are to reduce levels of poverty, disease, and deprivation. The World Summit on Sustainable Development (WSSD), which took place in Johannesburg in 2002, was a watershed event in the business of forming partnerships between the United Nations, governments, businesses, and non-governmental organizations (NGOs) to collect resources to address global issues about the environment, health, and poverty. The Millennium Goals were reaffirmed at the Johannesburg Summit, and in addition to those goals, several new ones were established. These new goals include reducing by half the number of people who lack access to basic sanitation, minimizing the harmful effects of chemicals, and putting a stop to the loss of biodiversity. According to the opinions of a few authors, the summit represented an “advance in moving the notion of sustainable development toward a more constructive investigation of the link between economic development and environmental quality.” The World Social and Sustainable Development Goals (WSSD) filled some gaps in Agenda 21 and the Millennium Development Goals and addressed some newly emerging issues, including the following: to halve the proportion of people who do not have access to basic sanitation by the year 2015; to use and produce chemicals by the year 2020 in ways that do not lead to significant adverse effects on human health

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and the environment; to maintain or restore depleted fish stocks to levels that can produce the maximum sustainable yield as quickly as possible and wherever it is possible; and to maintain. The Conference in Johannesburg gave credence to a pattern that has been observable ever since the Conference in 1992: namely, an upward trend in the importance placed on the socioeconomic pillars of sustainable development. The environmental agenda at the two previous United Nations conferences had been sustained by peaks in the public “attention cycle” of major industrialized countries. These peaks occurred during the conferences held in Rio de Janeiro and Johannesburg. The World Summit on Sustainable Development (WSSD) was first referred to as “the implementation summit” because it incorporated the idea of sustainable development into its debates. Unavoidably, “demands for increased financial resources and technology transfer continued,” even though a significant portion of the argument had already been pre-empted by the establishment of the Millennium Development Goals in the year 2000.

Sustainable Development Goals In 2015, world leaders came to an agreement on the Sustainable Development Goals (SDGs), which will serve as the guiding principles for international development until 2030. The Sustainable Development Goals (SDGs) is an international initiative to eradicate global poverty, promote environmental preservation, and provide economic prosperity for all people. They are a collection of 17 goals that have 169 corresponding targets. They serve as a focal point for the development efforts of the world community until the year 2030 and serve as the measuring stick against which progress will be evaluated (Mhlanga, 2022b). Each of the 17 goals is connected to the others, and it is intended that they be worked on collectively rather than separately. 2015 marked the year when member states of the United Nations unanimously agreed to adopt the 2030 Agenda for Sustainable Development (Mhlanga, 2022c) This agenda provides a shared road map for achieving peace and prosperity for people and the planet, both now and into the foreseeable future (Mhlanga, 2021, 2022c). The 17 Sustainable Development Goals are the central focus and driving force of the agenda 2030. (SDGs). These aims acknowledge that any effort made to eradicate poverty should also involve implementing policies that will lead to improvements in health and education, as well as a reduction in inequality and stimulation of

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economic growth (Mhlanga, 2021, 2022d). In addition to combating climate change and striving to protect the world’s oceans and forests, all of these measures should be made (Mhlanga, 2022d). Table 4.1 contains a summary of the seventeen sustainable development goals. According to the United Nations (2019), countries had approximately four years after signing Agenda 2030 to integrate their goals and targets into their national development plans. This opportunity existed for as long as Agenda 2030 was in effect. According to the report on the Table 4.1 Sustainable development goals Sustainable development goals Goal 1 Goal 2 Goal 3 Goal 4 Goal 5 Goal 6 Goal 7 Goal 8 Goal 9 Goal 10 Goal 11 Goal 12 Goal 13 Goal 14 Goal 15

Goal 16

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Put an end to poverty worldwide in all its manifestations Reduce food insecurity, boost nutrition, and advance sustainable agriculture to end world hunger Get every one of every age to live healthy and happy lives Promote learning opportunities throughout one’s life and work to ensure that everyone has access to a high-quality education Bring about full gender parity and grant equal rights to all women and girls Make water and sanitation accessible to everyone and manage them sustainably Assure that everyone has access to energy that meets their needs and is affordable, reliable, sustainable, and cutting-edge Encourage economic growth that benefits all remains stable over time, and creates full and productive employment and good work for all Invest in long-term infrastructure, push for an inclusive and environmentally responsible industrial revolution, and encourage creative problem-solving Lessen inequality both domestically and internationally Bring inclusiveness, safety, resiliency, and sustainability to cities and human settlements Make sure your consumption and production habits are sustainable Act quickly to mitigate the effects of climate change For long-term prosperity, it’s essential to take care of the oceans, seas, and marine resources Land ecosystems must be safeguarded, degraded, and deforested responsibly, desertification must be fought, and land degradation and biodiversity loss must be stopped in their tracks Ensure that everyone has access to justice, and create inclusive, accountable, and effective institutions at all levels to facilitate sustainable development in peaceful communities Improve the tools for action and reinvigorate the international coalition for sustainable development

Source Author’s analysis of United Nations (2019) data

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sustainable development goals for 2019, even though there was progress made towards the accomplishment of SDGs, problems remain in several sectors (United Nations, 2019). One of the areas that were highlighted was climate change, and the report did indicate that if action is not taken to cut record-high greenhouse gas emissions, then it is projected that global warming will reach 1.5 degrees Celsius in the decades to come. This was one of the areas that was highlighted (United Nations, 2019). Many people feel that the combined impacts of climate change will be disastrous and impossible to reverse. According to the United Nations (2019), some of the effects of climate change and greenhouse gas emissions include an increase in ocean acidification, erosion of coastal the coastal area, existence of extreme weather conditions, frequency and severity of natural disasters, land degradation, loss of important species, and the general collapse of the ecosystem. All these effects have been attributed to human activity. The fact that all the effects of climate change will render some areas of the planet uninhabitable is the thing that worries me the most, and the effects on the poor will be greater in comparison to the effects on the rich (United Nations, 2019). Another one of climate change’s troubling effects is how it will affect food production. This, in turn, will affect widespread food shortages and hunger, as well as the rate at which we can achieve the Sustainable Development Goals (SDGs). There is still a significant problem with poverty, starvation, and sickness in the world’s poorest and most vulnerable individuals and nations (United Nations, 2019). The issue caused by COVID-19 is also affecting the achievement of sustainable development objectives, particularly concerning the potential for the pandemic to afflict persons and nations with lower incomes. Mhlanga and Ndhlovu (2020) did indicate that the COVID-19 pandemic, while it is a public health catastrophe, there is a general concern about the implications of the virus for both local and global food systems and their capacity to guarantee safe and affordable food accessibility and utilization as well as adequate incomes for those located specifically in the smallholder sector of developing countries. Mhlanga and Ndhlovu’s study also indicated that the virus, like others that have caused pandemics in the past such as Ebola, will have an effect on the incomes of smallholder farmers as well as their general life (Mhlanga & Ndhlovu, 2020). Over ninety percent of maternal mortality occurs in nations with low or intermediate levels of income. Children in Southern Asia and sub-Saharan Africa make up three-quarters of the world’s total population of children

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who are stunted. Persons living in fragile states have roughly a fourfold increased risk of not having access to basic drinking water services and a twofold increased risk of not having access to basic sanitation systems when compared to people living in non-fragile settings. Young people have a risk of unemployment that is three times higher than that of adults. Women and girls are responsible for a disproportionate amount of unpaid domestic labour, and they have little to no say in the decisions that affect their lives. Since close to 80% of people who are living in severe poverty are in rural regions, the United Nations (2019) did indicate that the promotion of sustainable agriculture can help to alleviate hunger and poverty. Despite this, the presence of COVID-19 will have a significant impact on the efforts that many member nations are making towards accomplishing sustainable development objectives, particularly the reduction of poverty and other obstacles.

Nations and Selected SDGs Targets As stated by the United Nations, with just under ten years left until the target date of 2030 to achieve the Sustainable Development Goals, world leaders gathered at the SDG Summit in September 2019 called for a Decade of Action and distribution for sustainable development, and promised to mobilize financing, promote national implementation, and strengthen institutions to achieve the Goals by the target date of 2030, leaving no one behind. The Secretary-General of the United Nations issued a call to action to all segments of society, urging them to mobilize for a decade of action on three levels: global action to secure greater leadership, more resources, and smarter solutions for Sustainable Development Goals; local action to embed the necessary transitions in the policies, budgets, institutions, and regulatory frameworks of governments, cities, and local authorities; and people action, including action by youth, civil society, the media, the private sector, and universities. In this part, we will emphasize the progress that has been done by various regions of the world regarding the attainment of the various targets.

Reduce Child Mortality Child mortality refers to the death rate among children under the age of five, whereas infant mortality refers to the death rate among infants under the age of one. Both terms are used in demography. The United

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Fig. 4.1 Has the country already reached the SDGs target on child mortality in 2020 (Source Our World in Data)

Nations Sustainable Development Goals (SDGs) have a target number 3.2 that aims to lower the rate of child mortality, which is defined as the percentage of children who pass away before reaching their fifth birthday, to less than 25 deaths for every 1,000 live births (Fig. 4.1). The bulk of the territories that have not yet met the Sustainable Development Goals is in Africa, India, Bolivia, and Afghanistan, among other nations, as seen in Fig. 4.2. However, when compared to the number of regions that have not yet met their targets, the number of regions that have already accomplished the target is significantly higher.

Universal and Equitable Access to Modern Energy Services---Electricity Access Expanding the availability of electricity and cooking and heating fuels and innovations, as well as increasing energy efficiency and increasing the share of renewable energy, are all necessary steps towards achieving the goal of providing universal access to energy services that are affordable, reliable, and sustainable. Has the country already met the Sustainable

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Fig. 4.2 Has the country already reached the SDG target on electricity access in 2020

Development Goal objective for access to energy by the year 2020? Access to modern energy services should be universally and equitably available since this is one of the targets of the United Nations’ Sustainable Development Goals (SDGs). Africa and India make up most of the world’s population that does not have access to modern energy services on universal and equal terms, as demonstrated in Fig. 4.3 that was just presented. Egypt, Tunisia, and Morocco are the only African nations to have accomplished this goal. Afghanistan and Pakistan are two other examples of nations that are currently facing difficulties. When compared to the rest of the globe, Africa is the region that has the biggest number of states that have not accomplished this target. This is the target that they are aiming for. The percentage of the world’s population that has access to clean fuels for heating and cooking is depicted in Fig. 4.3.

Access to Clean Fuels for Cooking and Heating Target 7.1 of the United Nations’ Sustainable Development Goals (SDGs) is to guarantee that all people have access to energy services that are affordable, dependable, and up to date. This is shown here as the percentage of the total population that has access to clean fuels that can be used for heating and cooking. According to the World Health Organization (2022), access to clean fuels and technologies for cooking

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Fig. 4.3 Has the country already reached the SDG target on clean cooking fuels in 2020?

is not uniformly distributed around the world. The percentage of people who had access to clean cooking fuels and technologies increased by only approximately 1% per year on average from 2010 to 2019. A significant portion of this increase was attributable to improvements in access to clean cooking in the five low- and middle-income countries with the most populous populations: Brazil, China, India, Indonesia, and Pakistan; the rate in other low- and middle-income countries has remained relatively stable. The informative illustration can be found in Fig. 4.3. Target 7.1 of the United Nations’ Sustainable Development Goals (SDGs) is to guarantee that all people have access to energy services that are affordable, dependable, and up to date. This is shown here as the percentage of the total population that has access to clean fuels that can be used for heating and cooking. The number of countries that have not accomplished this goal has expanded, and they now include Africa, Russia, Brazil, India, China, and perhaps Mexico to some extent.

Improved Water Access in 2020 Achieving universal and equitable access to clean and cheap drinking water for all is the objective of Target 7.1 of the United Nations Sustainable Development Goals (SDGs). In this context, we assume that the target threshold for access to an improved water source is at least 99%. Rainwater, as well as water that is piped into a residence, plot, or yard; water that is piped into a neighbour’s plot; public taps or standpipes; tube wells

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Fig. 4.4 Has the country already reached the SDG target of improved water access in 2020?

or boreholes; protected dug wells; and protected springs are all examples of improved water sources. According to the data presented in Fig. 4.4, there are a significant number of countries and nations that have not yet achieved their goal of providing improved access to water by the year 2020. There are numerous nations in Africa, practically all of which are still working towards achieving objective 7.1 of the United Nations Sustainable Development Goals (SDGs), which state that everyone should have universal and equitable access to safe and affordable drinking water. Africa is a region.

Access to Sanitation The percentage of a population that has access to and makes use of better sanitation facilities is what’s meant to be understood as having “access” to sanitation. Goal 6.2 of the United Nations’ Sustainable Development Goals (SDGs) is to ensure that all people have access to sanitation and hygiene services that are adequate and equitable. The point at which 99% of the population has access to facilities with improved sanitation is marked here as the cutoff point. Figure 4.5 illustrates whether a country has met the Sustainable Development Goal target for access to sanitation by the year 2020. Africa, India, Russia, and Brazil are just some of the many places. Access to safe

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Fig. 4.5 Has the country already reached the SDG target for access to sanitation in 2020?

drinking water and sanitation facilities that are managed securely is used by many international organizations as a measurement of progress in the fight against poverty, disease, and mortality. In addition, having access to these services is regarded as a human right and not a privilege; this applies to everyone, including men, women, and children. Even though there has been significant progress made to give people all over the world clean drinking water and sanitation, there are still billions of people who do not have access to these services daily. What we have come to see is that Africa is coming dangerously close to falling short of all the goals outlined in this chapter.

Chapter Summary The purpose of this chapter was to examine sustainable development and Sustainable Development Goals from a historical point of view. The phrase “development that satisfies the demands of the present generation without compromising the ability of future generations to satisfy their own needs” is one meaning of the term “sustainable development.” The concept of sustainable development is based on two key ideas: first, it is of the utmost importance to meet the needs of people, especially the minimal needs of the underprivileged; and second, the ability of the environment to meet both present and future needs is constrained by the current state of technology and social organization.

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Both ideas are fundamental to the concept of sustainable development. The United Nations consistently promotes the Sustainable Development Goals (SDGs), asserting that these goals are universal, that they cannot be separated, and that they are interconnected. Additionally, the organization states that these goals are interconnected with one another. The Sustainable Development Goals (SDGs) are possible to be carried out on a global basis while also being adapted to the capabilities, conditions, and stages of development of a wide range of different countries. This is because the Sustainable Development Goals were designed to be applicable on a global scale. According to a study that was conducted by the Commission on the Environment in 1987, all countries, regardless of whether they are considered developed or developing, should identify long-term goals and objectives for economic growth and social development that are consistent with sustainability.

References Dasgupta, P. (2007). The idea of sustainable development. Sustainability Science, 2, 5–11. https://doi.org/10.1007/s11625-007-0024-y Duran, D. C., Gogan, L. M., Artene, A., & Duran, V. (2015). The components of sustainable development-a possible approach. Procedia Economics and Finance, 26, 806–811. Meadowcroft, J. (2000). Sustainable development: A new(ish) idea for a new century? Political Studies, 48(2), 370–387. Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability, 13(11), 5788. Mhlanga, D. (2022a). The role of artificial intelligence and machine learning amid the COVID-19 pandemic: What lessons are we learning on 4IR and the sustainable development goals. International Journal of Environmental Research and Public Health, 19(3), 1879. Mhlanga, D. (2022b). Stakeholder capitalism, the Fourth Industrial Revolution (4IR), and sustainable development: Issues to be resolved. Sustainability, 14(7), 3902. Mhlanga, D. (2022c). Human-centered artificial intelligence: The superlative approach to achieve sustainable development goals in the fourth industrial revolution. Sustainability, 14(13), 7804. Mhlanga, D. (2022d). The Fourth Industrial Revolution: An introduction to its main elements. In Digital financial inclusion. Palgrave studies in impact

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finance. Palgrave Macmillan. https://doi.org/10.1007/978-3-031-16687-7_ 2 Mhlanga, D., & Ndhlovu, E. (2020). Socio-economic implications of the COVID-19 pandemic on smallholder livelihoods in Zimbabwe. Preprints, 2020, 2020040219. https://doi.org/10.20944/preprints202004.0219.v1 Our World in Data. (2020). Has country already reached SDG target on child mortality? https://ourworldindata.org/grapher/sdg-target-on-childmortality Paul, B. D. (2008). A history of the concept of sustainable development: Literature review. The Annals of the University of Oradea, Economic Sciences Series, 17 (2), 576–580. Prizzia, R. (2017). Sustainable development in an international perspective. In Handbook of globalization and the environment (pp. 19–42). Routledge. Redclift, M. (1992). Sustainable development and global environmental change: Implications of a changing agenda. Global Environmental Change, 2(1), 32– 42. Rees, W. E. (1989). Defining “sustainable Development.” Centre for Human Settlements, University of British Columbia. Singh, S. K. (2016a). Sustainable development: A literature review. The International Journal of Indian Psychology, 3(3), 63–69. Singh, Z. (2016b). Sustainable development goals: Challenges and opportunities. Indian Journal of Public Health, 60(4), 247. Sneddon, C., Howarth, R. B., & Norgaard, R. B. (2006). Sustainable development in a post-Brundtland world. Ecological Economics, 57 (2), 253–268. Tomislav, K. (2018). The concept of sustainable development: From its beginning to the contemporary issues. Zagreb International Review of Economics & Business, 21(1), 67–94. United Nations. (2019). Transforming our world: The 2030 agenda for sustainable development. https://www.unfpa.org/resources/transformingour-world-2030-agenda-sustainable-development. Accessed on 11 November 2022. World Health Organization. (2022). WHO publishes new global data on the use of clean and polluting fuels for cooking by fuel type. https://www.who.int/ news/item/20-01-2022-who-publishes-new-global-data-on-the-use-of-cleanand-polluting-fuels-for-cooking-by-fuel-type

PART II

Advancing the Sustainable Development Goals (SDGS) with FinTech and Artificial Intelligence

CHAPTER 5

FinTech and Artificial Intelligence in Addressing Poverty, Towards Sustainable Development

Introduction Although the world has made enormous strides towards reducing poverty since 1978, there are still many obstacles to overcome before poverty is completely eradicated. Many academics, including those in the fields of sociology, economics, and finance, have taken an interest in the global topic of poverty alleviation (Ye et al., 2022). One of the key strategies for effectively reducing poverty around the world is the rapid development and innovation of the financial sector. The recent growth of the financial sector has been aided by financial innovation, including financial product and financial process innovation. Fintech, or technology-driven financial innovation, has accelerated financial development worldwide, especially in developing nations like China and India. The growth of digital financial services has been spearheaded by emerging nations, which have a thriving fintech sector and a vast user base. Some aspects of fintech, such as those relating to payments, wealth management, and online insurance, have evolved far more quickly in places like China than they have in the United States (Ye et al., 2022). The growth of fintech in China can be separated into three phases. The finance IT stage made up the first phase (2005–2010). During this phase, IT technology aided financial institutions’ operational effectiveness. The second stage, which lasted from 2011 to 2015, was internet finance.

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Internet technology was created during this phase to support financial, payment, investment, and information intermediary services. The third stage, which began in 2015, combines traditional finance with new financial technologies, such as big data, blockchain, cloud computing, and artificial intelligence (Bu et al., 2022; Cheng & Qu, 2020). There is no doubt that digitalization and the use of technology like artificial intelligence are changing the financial and capital markets. How to best use this disruption to assure financing for Sustainable Development Goals (SDGs) is less obvious. New legislative and regulatory alternatives are made possible by the rising digitalization, which is also causing fundamental changes in business models, markets, and the layout of physical infrastructure. It covers a developing technical ecosystem that includes blockchain, cryptocurrencies, artificial intelligence, the “Internet of Things,” as well as more fundamental yet crucial mobile payment platforms. The way we pay for everything, from food to energy to healthcare, is already changing as a result of digitization. Beyond that, technology is altering the nature of these products and services, how we see their relationship to us, and consequently how we value our decision-making alternatives. Many of our local and global objectives, such as ensuring that people have good livelihoods and slowing the rate and consequences of climate change, are now possible thanks to digital. According to Khamis et al. (2019), there is a paradigm shift taking place in the way that individuals access, use, and consume goods and services, as well as how businesses run, develop, and overcome obstacles in a constantly changing environment. The unpredictability of this shift is a result of rapidly advancing technological developments. Because AI has produced a robust and increasingly varied commercial revenue stream, it is likely the technology industry that is growing the fastest. Politicians, economists, and decision-makers have been prompted to pay closer attention to the outcomes due to the predicted advantages and risks of the widespread usage of AI (Khamis et al., 2019). Some academics believe AI poses a serious existential risk to humanity due to its opaque internal decision-making process, while others support boosting the technology’s exploitation (Khamis et al., 2019). According to Appiah-Otoo and Song (2021), one of the biggest problems facing humanity in the twenty-first century is poverty. Ending poverty in all its forms remains a top priority for decision-makers and international organizations like the United Nations and the World Bank because it implies a lack of financial resources and is linked to illness, the formation of risky social groups,

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a lack of leisure activities, stigma, a low standard of living, economic hardships, and a poor diet. Without a doubt, Appiah-Otoo and Song (2021) concur that financial development is crucial for reducing poverty because it increases the likelihood that the poor will have access to financing by addressing issues with financial market imperfections like information asymmetry and the high cost of lending to borrowers. Financial development also enables the poor to invest their savings or borrow money to launch microenterprises, all of which encourage greater access to financial services, increase employment and earnings, and so lessen poverty. Again, the trickle-down hypothesis is used to support the claim that financial development will accelerate economic expansion and hence decrease poverty. However, it is still difficult for traditional financial institutions to provide financial services to the poor, particularly in developing and emerging economies. Fintech, also known as internet finance or digital financial inclusion, has just come to be recognized as a substitute source of funding for this underserved demographic and is expanding quickly in emerging nations. Fintech has exploded, supporting much-needed financial inclusion, thanks to the quick development of the Internet, information technology, mobile phones, and digital technologies in the financial sector. Financial inclusion is thought to be crucial for promoting enterprise development, eliminating income inequality and child labour, empowering women, and lowering poverty (Khaki et al., 2022). The development of technology and creative methods for distributing financial products and services over the past ten years has seen financial inclusion advance above its traditional boundaries, particularly in Africa (Khaki et al., 2022). The inadequately established financial systems, subpar infrastructure, insufficient brick-and-mortar banking outreach, and extreme poverty define African economies. Khaki et al. (2022) used the supervised machine learning (ML) approach to determine the most effective predictive multiple regression model and the best mixture of technological, financial inclusion, and fintech variables to evaluate the impact on poverty alleviation in the region. They looked at and revisited the relationship between economic inclusion, digital/technological penetration, and poverty alleviation in African countries as digital financial inclusion platforms evolve. According to Khaki et al. (2022), among the technological factors, mobile phone outreach significantly affects the reduction of poverty. Automated teller machine (ATM) coverage and access to formal credit

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have been found to significantly contribute to the decrease of poverty in the region among the conventional financial inclusion variables, however, brick-and-mortar banking outreach does not affect lowering poverty. In contrast to other fintech variables, according to Khaki et al. (2022), the number of mobile money agents, mobile money transactions, and the use of mobile and Internet to access accounts all significantly contribute to reducing poverty. The present chapter will examine the role played by Fintech and artificial intelligence in alleviating poverty and achieving sustainable development goals in light of this rich history.

Definition of Important Terms The phrases “fintech” and “artificial intelligence” (AI) were explained in their entirety in the chapters that came before this one; nonetheless, for the convenience of the readers, this section shall describe these concepts once more.

Poverty Being unable to meet one’s basic demands for resources is the condition of being in poverty, which is a complicated and diverse term. Poverty is defined as having limited access to the resources, opportunities, and services needed to sustain a respectable quality of life. Economic, social, cultural, and political viewpoints can all be used to understand poverty. Economically, poverty is frequently determined by income or consumption levels, and those who make less than a specific amount of money are seen to be poor. Socially speaking, poverty can be viewed as a state of social exclusion, when people lack access to the institutions, networks, and connections that would otherwise allow them to participate fully in society. The inability of people to participate in social, political, and economic life can be correlated culturally with poverty through the absence of education, skills, and cultural capital. From a political perspective, poverty can be viewed as the outcome of the unfair allocation of power and resources. Poverty can have negative and wide-ranging repercussions. Malnutrition, illness, substandard housing, and restricted access to education are just a few of the negative effects of poverty. Social exclusion, prejudice, and fewer prospects for social mobility are some effects of poverty that can prolong poverty cycles through generations. Poverty is not only a problem in underdeveloped nations; it also exists in rich

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nations. The reasons for poverty can be intricate and interconnected, and they may include things like a lack of educational opportunities, unemployment, low pay, discrimination, and restricted access to opportunities and social services. To combat poverty, policies and programmes that improve access to social services, healthcare, and education are frequently combined with initiatives that support economic growth, lessen income inequality, and give marginalized people more power. Absolute poverty can be characterized by a specific dollar amount, whereas relative poverty is measured in comparison to the standard of living of the rest of the population (Suckling et al., 2021). A poverty line is the level of income at which a person is considered to be living below what is considered to be the minimum necessary to meet their basic requirements. If a person’s income, expenditures, or wealth are below this level, then they are said to be living in poverty. A person is living in relative poverty if their income, expenditures, or wealth are much lower than the average for the rest of the population. This contrasts with absolute poverty, which is evaluated independently of others. Throughout the years, the term “poverty” has been defined with an emphasis on monetary aspects; however, as time passes, academics are beginning to shift the definition of poverty to multidimensional issues such as political participation and social exclusion, demonstrating that poverty is a phenomenon that involves multiple dimensions (Mhlanga, 2020a). This indicates that poverty is not the result of a single cause but rather the outcome of a collection of causes. As a direct consequence of this, the United Nations (UN) categorized poverty according to two different dimensions: absolute poverty and overall poverty. Absolute poverty is a state in which people are unable to meet their most fundamental human requirements, such as obtaining adequate nutrition, medical care, adequate housing, clean drinking water, adequate sanitation facilities, education, and access to the latest information (Davids & Gouws, 2013; Mhlanga, 2020a). Because of this, poverty can be attributed to a variety of causes in addition to a lack of income (Davids & Gouws, 2013). People are living in a state of overall poverty if they are unable to gain access to sources of income and other resources that can be used for constructive purposes. Overall, poverty is characterized by several factors, including but not limited to “hunger and malnutrition, poor health, an inability to obtain the education, an increase in morbidity and death due to sickness, a shortage of housing, dangerous conditions, social marginalization and prejudice” (Davis & Sanchez-Martinez, 2015). Poverty in general

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“involves a lack of participation in decision making in civil, social, and cultural life,” according to the World Bank (Davis & Sanchez-Martinez, 2015; Mhlanga, 2022a). A second definition of poverty, known as the multidimensional poverty index, was also developed by the United Nations (Mhlanga, 2020b). The United Nations adopted a definition in 2010 that takes into account a variety of factors, including education, health, standards of living, and many more. The Joseph Rowntree Foundation (JRF) provided its definition of poverty in 2013. According to this definition, a person is considered to be living in poverty if their material means are insufficient to meet their minimum needs, and if social engagement is also taken into account (Mhlanga, 2020a, 2021a). The World Bank placed a lot of emphasis on individuals’ incomes and consumption levels as major factors that can cause a person to be poor. This is especially true if the individual does not reach a predetermined income or consumption threshold, which is more commonly known as the poverty datum line (Davids & Gouws, 2013; Mhlanga, 2022a). The purpose of this study is to evaluate, using a variety of definitions of poverty as a starting point, how AI can have an impact on different aspects of poverty. According to Suckling et al. (2021), to live in poverty is to be unable to acquire the resources that are necessary to fulfil one’s fundamental requirements. Consideration of a person’s economic resources can be one method for determining whether they are living in poverty. These resources can be broken down into three categories: the amount of money a person earns, also known as their income; the amount of money they spend, also known as their expenditure or consumption; and the amount of money they have saved, also known as their wealth. This definition of poverty refers to economic destitution. One measure of the needs of the poorest people is economic poverty, but this is not the only one. There are several dimensions to poverty, including the social, nutritional, cultural, and multifaceted. The worldwide definition of extreme poverty does not explicitly take into account these other aspects of living conditions.

FinTech Fintech refers to companies that utilize technology to improve or automate financial services and operations. The terms “financial” and “technology” are combined to form the term (Daley, 2022). The phrase refers

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to a quickly expanding sector that benefits both consumers and companies in various ways. Fintech offers a plethora of uses, ranging from cryptocurrencies and investment apps to mobile banking and insurance.

Artificial Intelligence (AI) The field of computer science known as artificial intelligence (AI) encompasses a wide range of subfields and focuses on the development of intelligent computers that can carry out activities that would normally need human intelligence (Alyssa, 2022). The field of artificial intelligence is interdisciplinary, and it may be approached from a variety of angles; nevertheless, recent developments in machine learning and deep learning are producing a paradigm change across the board in the technology industry. Artificial intelligence enables machines to model the capabilities of the human mind and even improve upon those skills. Artificial intelligence is becoming increasingly integrated into many aspects of modern life, including the research and development of self-driving automobiles as well as the rise of digital assistants such as Siri and Alexa. As a direct consequence of this, numerous IT companies operating in a wide variety of sectors are making investments in AI technologies.

Poverty in the World It was projected 698 million people, or 9% of the world’s population, will be living in extreme poverty in 2021, which is defined as having a daily income of less than $1.90. 1,803 million people live below the higher $3.20 poverty line, or more than one-fifth of the world’s population; 3,293 million people live below the lower $5.50 poverty line (Suckling et al., 2021). According to some estimates, the COVID-19 pandemic and the ensuing worldwide economic collapse caused an estimated 50 million more people to live in extreme poverty between 2019 and 2020. As the world economy began to recover in 2021, the number of people living in extreme poverty is predicted to have decreased, but there are still an estimated eight million more people living in poverty now than there were in 2019. (Suckling et al., 2021). This comes after years of remarkable poverty reduction. In 2010, an estimated 1.1 billion people, or 16% of the world’s population, were reported to be living in extreme poverty, compared to over 2 billion in 1990. Although there has been a decrease in

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extreme poverty globally, the fight against poverty continues to be waged on a global scale at a gradual rate. Extreme poverty decreased as a percentage of the global population, from 10% in 2015 to 10% in 2010, and from 36% in 1990 to 16% in 2010 (World Bank, 2020a). The numbers indicate that the globe is not on track to reduce the proportion of people living in extreme poverty by 2030. (World Bank, 2020a). However, according to the baseline estimates, 6% of the global population will still be living in extreme poverty in 2030, falling short of the goal of eradicating poverty (World Bank, 2020a). Extreme poverty persists as a deeply ingrained deprivation that is occasionally made worse by violent conflicts and a vulnerability to disasters. It is suggested that stringent social safety nets and significant public spending on essential services can assist persons who are poor in emerging from poverty. However, these services must be strengthened and expanded (Mhlanga, 2020a; World Bank, 2020b). Despite having a job, 8% of households in 2018 were still living in extreme poverty, according to Mhlanga (2020a). The situation is worse in Sub-Saharan Africa, where about 38% of the working poor live there as of 2018 (World Bank, 2020b). The countries with the largest decrease in the number of persons in extreme poverty are China and India. Between 2010 and 2021, more than 407 million people in those two nations emerged from extreme poverty. Between 2010 and 2020, there were more persons in extreme poverty in 26 of the sub-Saharan African nations. Angola (9.4 million), the Democratic Republic of the Congo (8.8 million), and South Sudan (7 million) have seen the highest growth. In 2021, countries in sub-Saharan Africa will be home to 66% of the world’s population who live in extreme poverty (Suckling et al., 2021). In nations impacted by violence and fragility, poverty has also risen. Yemen experienced the biggest global growth in the number of people living in extreme poverty between 2010 and 2021, with 16 million more individuals living there in 2021 than in 2010. Although precise figures are difficult to come by in these situations, the number of individuals in Syria and Venezuela who live in extreme poverty is reported to have climbed by 6.7 million and 10 million, respectively (Suckling et al., 2021). The World Bank agreed that SubSaharan Africa continues to be the region with the highest concentration of poverty. Despite a drop in poverty rates across the board in 2019, the World Bank noted that progress was unevenly being made (World Bank,

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2020b). According to the World Bank, roughly more than half of the world’s poorest people reside in Sub-Saharan Africa (World Bank, 2020a). In 2015, it was estimated that 413 million people in Sub-Saharan Africa were living on less than US$1.90 per day and that the number of poor households increased by 9 million. This exceeded the total for the rest of the planet (Mhlanga, 2020b; World Bank, 2020a). Additionally, it is predicted that by 2030, nearly 90% of the world’s extremely impoverished people will live in Sub-Saharan Africa (Beegle & Christiaensen, 2019).). Most of the world’s poor population lives in rural areas, which is another concerning factor. According to the World Bank, most of these individuals have low levels of education, work in the agricultural industry, and are under the age of 18 (Mhlanga, 2020c; World Bank, 2020a). The World Bank however stated that there are still numerous obstacles to overcome to end extreme poverty, including weak growth rates in many regions of the world (World Bank, 2020b). Therefore, the goal of this project is to find out how using AI may help solve the global problem of poverty. One of the main goals of the Millennium Development Goals was to reduce global poverty (MDGs). The MDGs set a goal of halving the proportion of people living in extreme poverty between 1990 and 2015. This was accomplished on a global scale in 2012, three years ahead of schedule. The Sustainable Development Targets (SDGs), which were introduced in 2015, now place a priority on eradicating poverty; the first of its 17 goals is “no poverty by 2030.” The remainder of the paper will go through the many ways that AI and Fintech can aid in fighting poverty.

FinTech and Poverty Alleviation The advancement of financial technology benefits efforts to reduce poverty in a variety of ways. Fintech and poverty reduction have become highly attractive partners through the medium of financial inclusion. Due to its ability to provide individuals with affordable, convenient, and secure financial services, fintech has expedited the growth of inclusive finance. This is because it has the potential to reach financially vulnerable communities (Ye et al., 2022). One of the major new drivers of inclusive finance in many developing nations has been the fintech industry’s explosive rise. For instance, because of their accessibility, affordability, and safety, crowdfunding, mobile banking, and third-party payments are all regarded as crucial tools to encourage financial inclusion. Financial inclusion has gained widespread recognition as a potent instrument in the fight against

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poverty since the G20 conference in 2010. The amount of inclusive financial growth in emerging economies has typically been shown to have had a major positive influence on income poverty, according to existing literature at the macro level and the household level. By 2025, digital financing might add $3.7 trillion to emerging economies’ GDP (Ye et al., 2022). Figure 5.1 outlines how FinTech can help in alleviating poverty. FinTech generally could contribute to making sure that financial decisions more fully account for social and environmental externalities, such as climate risk, community effects, and labour standards. For instance, U.N. Women is utilizing blockchain to increase women’s financial security and sovereignty. Among other FinTech strategies, distributed solar technology is funded through crowdsourcing and made available to underprivileged populations through mobile payment methods (Ye et al., 2022). A list of the ways that fintech can assist combat poverty is provided below.

Fintech and Access to Credit by the Poor and Vulnerable

Improvement in the Availability of Information

Promotion of Entrepreneurial Activities

Social Networks Expansion

Fig. 5.1 FinTech and poverty alleviation

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FinTech and Access to Credit by the Poor and Vulnerable Through easing credit restrictions, FinTech and digital finance may help to lower the prevalence of poverty. Low-income and underprivileged rural households frequently face severe credit restrictions and are hampered by access to insufficient financial services, making it challenging for them to better their economic circumstances (Chen & Zhao, 2021). Rural people live more dispersedly, loans are frequently available only on a small scale, and traditional financial institutions have high unit expenses and lower overall returns when providing agricultural credit. As a result, obtaining formal financial services from traditional financial institutions is challenging for poor rural households, and they are unable to acquire additional funding for investments or other production. Digital finance, in comparison to traditional financial institutions, only requires a smaller initial investment for system creation and development, and by integrating a large amount of online user data, it can lower the degree of information asymmetry and the danger of adverse selection. This increases the growth of financial inclusion and lowers the percentage of impoverished people who are financially excluded (Mhlanga, 2021b, 2022b). Additionally, one advantage of digital finance is that loan applications only need to be completed on Internet terminals, which is more convenient and accommodating for rural households with little financial literacy. By expanding their access to financial services and streamlining the loan application procedure, digital finance through Fintech assists low-income rural households in relieving their credit limits (Chen & Zhao, 2021). Reduced credit restrictions for rural households may lead to higher family incomes and better risk-taking capacities, which would lower the prevalence of poverty (Mhlanga, 2021b).

Improvement in the Availability of Information In addition to difficulties in gaining access to finance, rural households with low incomes and low socioeconomic status also confront significant informational barriers. There is a significant digital divide between those with low income and those with middle or high income in terms of productivity, employment, and quality of life. Several studies have shown evidence that the wealth disparity is exacerbated by the digital divide.

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Fong (2009) evaluated the development relationship between the adoption rate of Information and Communications Technologies (including the Internet, mobile phone, pager, personal computer, and telephone) and the per capita income gap between the urban and rural areas in China, primarily between the years 1985 and 2006. According to the findings of Fong (2009), there has been a strong correlation in the developing relationship between this income gap and the adoption of the internet, mobile phones, personal computers, and telephones during this period. This correlation was found in the relationship between the development of the income gap and the relationship between the two. Picatoste et al. (2022) also suggested that digitization has the potential to be an effective instrument to promote the involvement of the most vulnerable social groups and to support gender equality. Again, the quick growth of information and communication technology (ICT), the use of cell phones, and the accessibility of the Internet all play a key part in the rise of individual income. The combination of information and communication technology (ICT) and traditional financial services has given rise to a new financial model known as fintech, or digital finance. Therefore, the development of digital finance may further improve the role of ICT in reducing the income gap and further promote the elimination of poverty by alleviating the information restrictions of poor and low-income households. This may be accomplished through the usage of digital finance. In addition, persons with low incomes typically lack the information necessary to manage their finances and have restricted abilities to acquire and recognize data from the Internet. Although the growth of FinTech makes it simpler and less expensive for low-income people living in rural regions to acquire information and also makes it easier for low-income people to obtain information, it may still be challenging to benefit low-income groups. Digital finance will be able to provide information that is both more valuable to customers in terms of helping them better their economic conditions and that is more compatible with the characteristics of users thanks to the support of financial platforms and big data technology. Digital financial platforms make it simple for people living in rural areas to access current information regarding agricultural output and management, jobs, and finances, as well as other topics relevant to daily life. After being analysed using large amounts of data, this portion of the data is now strongly matched with users and is both more accurate and transparent. It may assist to boost employment and enhance the effectiveness of agricultural production, which will ultimately lead to

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an increase in their income and a reduction in the number of people living in poverty. In addition, rural households have greater opportunities to optimally reallocate resources and increase their ability to cope with external risk shocks, even if the information obtained is only about day-to-day life. This is true even if the information received is solely about daily life.

Social Networks Expansion Using digital money, FinTech may be able to assist rural households in expanding their social networks and strengthening their links with relatives, friends, and neighbours. In developing economies, social networks are an important form of institutional social capital, which may help to explain the impact that digital financial development plays in reducing the amount of poverty experienced by rural households. Previous research revealed that individuals’ incomes, employment situations, and career choices were highly tied to the social networks that they maintained. Even more so in relational societies like the typical one in which we live, social networks play a significant part in helping rural households climb out of poverty. People now have access to a more accessible way to pay thanks to digital banking, which has also led to an increase in the frequency of social involvement. People now have access to an efficient method of communication and social contact thanks to the advent of digital banking, which is based on the Internet platform. WeChat is the most popular online social platform in China, and it was leveraged in the development of services such as WeChat Pay. The experience of participating in social interactions online has been much improved because of the incorporation of traditional Chinese elements into the practice of sending red envelopes on WeChat (Chen & Zhao, 2021; Hsiao, 2011). Additionally, the expansion of digital financial services has the potential to expand people’s access to the Internet and make it easier for them to participate in online social networking (Chen & Zhao, 2021; Liébana-Cabanillas et al., 2018).

Promotion of Entrepreneurial Activities Fintech, namely digital money, has the potential to reduce poverty by encouraging rural households to engage in entrepreneurial activity. Numerous studies have investigated the use of entrepreneurial endeavours as a means of alleviating poverty. The findings of these studies have

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led researchers to the conclusion that entrepreneurial endeavours, particularly informal entrepreneurial endeavours, which serve as an important source of increasing household income, are an effective way to lift rural households out of the poverty trap (He, 2019; Si et al., 2015). However, severe credit constraints will discourage entrepreneurial behaviour, which is especially problematic for low-income and low-wealth families. On the other hand, the financing function of digital finance helps increase the credit availability of potential entrepreneurs and has a positive impact on the entrepreneurial activities of rural households. Farmers who are interested in starting their businesses can obtain a large amount of information related to entrepreneurship and strengthen their cooperation with buyers or other entrepreneurs with the assistance of digital financial platforms. This allows them to evaluate the feasibility and market prospects of entrepreneurial projects more accurately (Xie et al., 2018). In addition, mobile payment can lessen the financial burden of transactions while simultaneously improving their convenience and security. The elimination of both transaction costs and hazards leads to a rise in the potential profits that can be made by business owners (Mhlanga, 2021b).

AI Reducing Poverty and Boosting Shared Prosperity: Identifying the Development Opportunities of AI Technology-based disruptions such as those brought on by artificial intelligence, blockchains, the Internet of Things, and many others are rapidly threatening the conventional routes taken by a nation’s economic development (International Finance Corporation, 2021). AI is very disruptive because it has the potential to drastically alter how we receive information, produce goods, or communicate. It can also result in a step change in the cost of, or access to, goods or services. The twin objectives of eradicating poverty and fostering shared prosperity are becoming increasingly dependent on harnessing the power of technologies like AI while also attempting to reduce the risks involved as development challenges become more and more intertwined with technology-based disruptions. Basic AI is already being used in emerging markets, including some of the world’s poorest nations, to address pressing development issues, particularly in the provision of financial services to underserved and unserved

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communities. An increasing number of technology users, early advancements in fundamental machine learning algorithms, and the reduced load of legacy technologies have allowed emerging economies to implement fundamental AI solutions like credit scoring and targeted advertising. Early instances of artificial intelligence (AI) providing financial services to the most vulnerable include Ant Financial in East Asia, M-Shwari in East Africa, M-Kajy in Madagascar, and MoMo Kash in Cote d’Ivoire. M-Shwari provided small loans to 21 million Kenyans by the end of 2017 by using machine learning to forecast the likelihood of default of potential borrowers (International Finance Corporation, 2021). Applications of artificial intelligence (AI) have the potential to address problems faced by people at the bottom of the income distribution, especially the bottom 40%. These people can take advantage of AI-as-a-service solutions through their mobile devices even though they do not have the financial resources to buy AI technology or equipment that is AIenabled. A machine learning app called Nuru has recently been used on farms in Kenya, Mozambique, and Tanzania to detect leaf harm in photos taken by farm owners and to send information to authorities to help oversee the existence of an intrusive pest that jeopardizes farm income and food security throughout East Africa. Mobile AI apps can provide microlending, individualized tutoring, health diagnostics, and prescription guidance because data gathered through mobile phones can be significantly connected with financial status, educational achievement, and health status (International Finance Corporation, 2021). Additionally, the speech recognition and speech-to-text capabilities of AI eliminate the literacy barriers that the poorest people normally have when using text-based apps. Additionally, it is possible to evaluate farmer microinsurance claims in far-off rural villages using picture recognition (International Finance Corporation, 2021; Khamis et al., 2019). Artificial Intelligence (AI) has the potential to help address poverty in several ways as shown in Fig. 5.2 which include Identifying and targeting poverty Improving access to education and healthcare, and Supporting social safety nets where AI helps governments and organizations to efficiently deliver social safety net programmes, such as cash transfers, food assistance, and healthcare benefits, to people in need. This can help reduce poverty and improve the lives of vulnerable populations. Another way where AI can contribute is through predictive modelling, where AI is used to build predictive models that can help forecast poverty and identify its root causes. This can help governments and organizations prioritize

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AI Identifying And Targeting Poverty.

AI Improving Access To Education, Healthcare

Supporting social safety nets

AI and Digital Financial inclusion AI and Productivity

Fig. 5.2 Channels in which AI addresses poverty

interventions and allocate resources more effectively. Finally, can help in the automation of low-skill jobs and help to increase productivity, which can reduce poverty by increasing wages and job opportunities for workers. This is particularly relevant in industries such as agriculture, where labourintensive tasks can be automated with the use of AI-powered machines. The points outlined in Fig. 2.2 are well explained.

AI Productivity and Poverty Reduction One way that AI might broaden and increase development opportunities, particularly in emerging nations, is by directly improving productivity. Many businesses in developed markets already leverage improved business efficiency that results from the automation of fundamental business operations and the development of human capital to dramatically reduce manufacturing costs. Growth in AI-enabled productivity directly increases output and employment and indirectly does so through higher consumption (International Finance Corporation, 2021; Khamis et al.,

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2019). Cost savings resulting from the automation of some tasks can be combined with easier access to financing to lower overall business costs, which is crucial benefit AI technologies are currently providing. The number of bankable business possibilities and the level of competition within markets and industries may both rise because of this. By supporting product innovation in the form of new business models and leapfrogging solutions designed to target previously unserved and underprivileged groups, AI solutions can also help emerging economies overcome their lack of infrastructure and significant information asymmetries (International Finance Corporation, 2021; Khamis et al., 2019). Through automation, AI is expected to significantly reduce costs across all essential corporate processes, including human resources management, marketing, accounting, and inventories. AI has the potential to help businesses achieve considerable productivity increases. For instance, the expensive process of reviewing dozens of candidate profiles during staff recruitment can be automated by utilizing AI technology. Automation of the hiring process often reduces the time it takes to find a new employee from 10 to 2 weeks. It also reduces the time it takes to narrow the field of candidates from 2–3 weeks to nearly nothing. As a result, when the poor can afford them, cheaper goods and services are supplied, lifting people out of poverty. The expansion of informal firms, which can account for up to two-thirds of the GDP in some low-income countries, is also likely to be fuelled by these gains, which also result from more effective human capital investments made possible by automation. Through carefully focused and personally tailored human capital expenditures, AI can transform high-quality education and learning. The ability to increase learning and employment through the integration of online courses and AI will be discussed further below. It also offers the chance to increase access to inexpensive education.

AI and Access to Essential Services by the Poor and Vulnerable The ability of AI to manage unstructured data has the potential to support product innovation in industries including pharmaceuticals, logistics, and transportation. Emerging markets frequently lack the structured data required to enable business analytics solutions because of their poor statistical capacity. However, AI can manage unstructured data, which is generally available in developing countries, such as audio recordings or

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videos, to find new methods to serve clients, provide new medications, and develop new preventative healthcare solutions. For example, drug discovery entails looking through a nearly unlimited number of molecular combinations; AI is much more effective at this task than medicinal chemists are. Through automation that provides cheap services, AI is also pushing innovations in business models, opening up the market to underserved consumers. In Cote d’Ivoire, for instance, TaxiJet, a ridehailing start-up with a business model similar to Uber, uses AI to connect customers with taxi drivers at a lower price than regular taxis. In South Africa, Bolt and inDiver provide similar services to Uber at costs that lowincome people may afford. By offering innovative and affordable methods to deliver social services to those who need them most, particularly distant areas, AI can also ease the restrictions caused by inadequate infrastructure in emerging countries. AI is being utilized in telemedicine for early disease diagnosis, taking advantage of mobile networks’ large coverage to cut expenses involved with maintaining a vast network of community health workers. Similarly to this, insufficient data frequently prevents the planning of educational resources from taking into account the geographic distribution of learning outcomes, which results in an uneven distribution of resources. Automated processing of student performance can assist in identifying problem areas. The ability of AI to match students and instructors can help increase access to higher-quality education. Increased productivity, lowered entry barriers, the development of new markets, and market expansion all have the potential to boost output and consumption. To the advantage of the overall economy, developing and extending markets can aid in boosting consumption and generating jobs. A bigger economic potential for AI in emerging markets than in developed markets is suggested by the importance of market expansion and productivity increase in informal companies. These gains could be strengthened much more by improvements in public service delivery made possible by AI. Due to possible large cost reductions, enhanced social service delivery, and greater risk management, governments in emerging nations may gain from AI. Through automated and real-time analysis of online activity, including social network and telecommunications metadata, there are also prospects for risk management, illness prevention, management of natural disasters, management of humanitarian crises, and citizen participation. All of these work together to reduce poverty and raise peoples’ standards of living.

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AI Identifying and Targeting Poverty It is essential to gain an understanding of the factors that contribute to poverty before attempting to find technological solutions to the issue. Because of natural disasters, war and other forms of violence, the inability to acquire food, and a lack of education and other life skills. Using AI, one may determine which areas in a region have the greatest need for assistance. AI has the potential to assist with the distribution of aid in poor areas, regions that are wracked by conflict, or areas that have been ravaged by natural disasters. This can be accomplished by enhancing farming lands and agricultural practices, expanding educational opportunities, and assisting residents in acquiring new skills that can be used to support communities. When it comes to being able to fight poverty, one of the most important steps is to locate areas that are affected by it. Researchers can do exactly this thanks to satellite images. It is possible, with the help of many photos captured by satellites, to determine the worldwide activities that are reflective of wealthy and impoverished places. For instance, regions that have a high density of light when it is night are often wealthier than those that do not have any light at all. This is because people who live in darkness have limited or no access to electricity during the hours of darkness. According to estimates provided by the World Bank, 736 million people are living in severe poverty around the globe; yet the majority of these individuals are concentrated in just five nations: India, Nigeria, the Democratic Republic of the Congo, Ethiopia, and Bangladesh. When it comes to determining whether they are succeeding in their mission to reduce global poverty, the World Bank and the United Nations rely significantly on research and data (Weber, 2019). According to the Decentralized AI Alliance (DAIA), the fact that it is impossible to collect data is a direct result of poverty (Mhlanga, 2020b). It is commonly held that location is one of the most important factors to consider while attempting to eradicate poverty in all of its myriad guises and every part of the world. It is claimed that governments are not gathering as much data as they should be and are not expanding their usage of traditional household surveys to determine the number of individuals living in poverty and the areas in which they can be found. This situation is made much more difficult by the fact that many countries cannot afford to conduct traditional home surveys (Mhlanga, 2020a). Nevertheless, AI has the potential to help change this. A group of researchers from Stanford University

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recently conducted a study in which they used satellite pictures to present an alternative to mapping poverty (Mhlanga, 2020a; Schmidt, 2020). The research was conducted across five different countries in Africa: Nigeria, Tanzania, Uganda, Malawi, and Rwanda. A group of social scientists and computer experts came up with the concept of employing high-powered satellites to detect poverty through an analysis of the images captured by these satellites. The researchers employed reliable survey data as a means of providing support for the hypotheses that were drawn from the study (Schmidt, 2020). AI can successfully map poverty by combining highresolution satellite imagery with strong machine learning algorithms and predicting whether regions around the world are rich or poor. This can be done in several different ways. AI may be used to efficiently offer information such as the distance from the nearest water sources, the nearest metropolitan market, or the location of agricultural fields, as well as a great number of other essential characteristics that are employed when assessing poverty (Mhlanga, 2020a). The Qatar Computing Research Institute (QCRI), which is a component of Hamad Bin Khalifa University, is also partnering with several organizations including the World Bank and the United Nations to combat poverty and other global challenges using artificial intelligence (Weber, 2019). The Artificial Intelligence for Digital Response (AIDR) platform was also developed by QCRI. This technology analyses data during natural catastrophes such as storms such as Hurricane Dorian. These evaluations are a big help in mapping the locations where assistance is required the most and the amount of work that is required to react to a crisis (Weber, 2019). The QCRI also works directly with relief organizations to develop technologies used in the analysis of large data in times of disaster to analyse the conditions for the distribution of resources. These technologies are used to determine how to best distribute available resources (Weber, 2019). The QCRI is the organization that develops poverty maps by utilizing anonymous advertising data from Facebook. These maps are produced with machine learning (Weber, 2019). New methods of monitoring and assessing development initiatives that are tailored to individuals with the greatest requirements can be made possible by technology powered by AI. The statistics that are required to fine-tune development programmes are frequently lacking in developing nations. The ability of artificial intelligence to process unstructured data including text, photos, and audio can help obtain the information needed

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to improve development outcomes. Other experiments include using machine learning on VAT tax data in India to better target firms for audits; predicting travel demand patterns after hurricanes; and determining where food insecurity will occur to assist in targeting aid interventions. These are just some of the ways that machine learning is being tested. Even though AI may provide some dangers, missing out on the chances it presents could end up being an even more expensive endeavour. AI has the potential to drastically speed up progress towards the Sustainable Development Aim and the World Bank Group’s twin goals of reducing poverty and increasing shared prosperity. AI may also accelerate the economic and societal revolutions brought about by disruptive technologies. However, countries will be left in the dust if they are unable to compete in the global economy of the future. Countries and companies in the private sector will need to implement innovative approaches to expand AI’s opportunities and mitigate its risks to fully capitalize on the potential of new business models, new ways of delivering services, and shifting sources of competitiveness. This will be necessary to successfully navigate the shifting competitive landscape.

AI Improving Access to Education There is a strong correlation between poverty and a lack of education, yet AI has the potential to positively influence education levels in communities with lower incomes. It won’t be long before we see intelligent chatbots filling the role of teachers for pupils who don’t have access to any other types of schooling. An artificially intelligent instructor could help guide students through a curriculum if they have access to a computer and the Internet. Individual students’ learning levels and skill sets could be evaluated by artificial intelligence using real-time analytics and machine learning. This has the potential to eliminate the financial obstacles and inequity that so many people all around the world face in the education system. Customized education is one further method in which artificial intelligence might be able to contribute to raising educational standards. There is no one right technique to learn; some people are better at taking in information through listening, some are more visual, and yet others learn best through doing. The application of AI could assist in determining the learning needs of students and in optimizing the process. Students who come from disadvantaged backgrounds will be able to acquire the abilities necessary to succeed if they are given the

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same beginning as their more privileged peers. When it comes to professional opportunities, this would make the playing field more even. It is common knowledge that low-income households are unable to provide their children with formal education. According to statistics provided by the World Bank, approximately 39% of the world’s most impoverished people do not have access to any type of formal education. The cost of education and the limited space available in higher education institutions are two factors that prevent many families from having access to educational opportunities. A great number of establishments have a maximum number of customers that they can serve (Wong, 2020). The use of adapted learning techniques that make use of computer algorithms to encourage interaction with the learner, as well as the development of education that is individualized to meet the requirements of each learner, are two of the many ways in which artificial intelligence (AI) can contribute to improving educational outcomes for disadvantaged children (Mhlanga, 2020a). It is very possible, with the assistance of AI, to determine the individual learning needs of each student and to be able to satisfy these requirements utilizing a variety of different learning methods. Intelligent chat boards are utilized by teachers in some one-of-a-kind situations, which helps remove the financial barrier to education for students who originate from poor places. This will assist address access difficulties, and eventually be able to combat inequality at the same time. According to the findings of the research conducted by Mhlanga and Moloi (2020), titled “COVID-19 and the Digital Transformation of Education: What We Are Learning in South Africa,” it was discovered that even though there was a social distancing restriction that was necessitated by COVID-19, technology helped a great number of people to be able to have access to classes through the use of online education. The research also revealed that technological advancements have the potential to expand access to educational opportunities, particularly if traditional classroom settings are replaced with virtual learning environments in which physical constraints such as location are eliminated (Bennington-Castro, 2017). AI is being utilized to help educate millions of kids in rural areas of Kenya, Ghana, and Côte d’Ivoire as part of a programme called Eneza Education, which is a social enterprise. This programme is one of the education initiatives in which AI is assisting in education. Artificial intelligence is contributing significantly to the improvement of educational standards in this manner. This also makes it possible for education to be inclusive and egalitarian, which helps create

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and promote opportunities for lifelong learning for everyone (Mhlanga, 2020a).

AI and Digital Financial Inclusion and Agriculture Through improved data processing and the wider availability of financing, innovation in financial services is being driven by artificial intelligence (AI). AI can reduce information asymmetry in contexts where borrowers lack credit history by relying on non-traditional data such as mobile phone call records, mobile money transaction data, text messages, and address books. This opens the door for first-time borrowers and the unbanked to have access to financial services. By automating credit scoring, a procedure that traditionally involves the involvement of human resources within traditional financial institutions, artificial intelligence (AI) offers the potential to reduce the cost of financial services. Users in underdeveloped countries can receive immediate credit scores thanks to the capabilities of machine learning algorithms, which can read enormous amounts of data stored on mobile phones. After a user has been approved for a loan, the credit scoring algorithm will continue to get better by learning from the user’s credit history. One company that takes this strategy to heart is Branch, a financial technology firm with operations in Kenya, Nigeria, India, and Mexico. The branch caters to consumers who have never taken out a loan before as well as those who do not have bank accounts. Reach to households that are not financially active or those that are unable to make use of formal financial services that are tailored to match their requirements, digital financial inclusion is seen to reach out to those households in an effective way (Alameda, 2020; Mhlanga, 2020a). According to Mhlanga (2020a), the people who are not allowed to participate in the formal financial sector include women, young people, and the impoverished, particularly those who live in rural areas. Mmeteoric PESA’s rise to prominence as one of the world’s most successful payment technologies, which began in Kenya, is largely credited with propelling digital financial inclusion into the mainstream (Wang & He, 2020). In Asian nations such as China, digital financial inclusion refers to more than just the introduction of new payment methods; it also encompasses digital investments, payments, and investment methods (Wang & He, 2020). The capacity of digital finance to make use of information

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and communication technology (ICT) to expand the reach of financial services to people who are excluded from the formal financial market and to increase the number of people who utilize those services is an important aspect of the field (Lauer & Lyman, 2015; Mhlanga, 2020a). The advent of information and communications technology (ICT) and artificial intelligence (AI) made it possible for financial inclusion to transition to a digital format and made it possible for vulnerable populations to get access to financial services (Mhlanga, 2020b). Mhlanga (2020a), agrees with Wang and He (2020) that one of the criteria for a business to be successful when it is done with people at the bottom of the pyramid is the use of radical innovations such as the usage of AI. Mhlanga (2020a) cites AI as an example of one of these requirements. The use of AI in digital financial inclusion differentiates it from traditional financial inclusion because, with digital financial inclusion, there is a massive reduction in transaction costs, particularly in the rural areas because of lower marginal costs caused by the fact that, digital financial inclusion, service providers don’t necessarily need to have physical outlets. This is because digital financial inclusion allows for the elimination of the need for physical outlets (Wang & He, 2020). Additionally, the application of AI and various tools provided by ICT allows for the elimination of the most significant barrier to traditional forms of financial inclusion, which is the imbalance of information (Mhlanga, 2020a). Customers have access to a great amount of information that, in the absence of digital services, would not otherwise be available to them through the usage of a variety of online services and products (Mhlanga, 2020a). According to Peric (2015), one of the benefits of digital financial inclusion is that it helps to preserve the disposable incomes of households. This is because digital financial services and products are typically offered at a lower cost to both the customer and the service provider. Another benefit of digital financial inclusion is that it allows poor people who were previously excluded from formal financial services to access those services. Customers are also able to conduct transactions in irregularly tiny quantities, which might help them better manage their inconsistent incomes, thanks to the usage of AI (Koh et al., 2018; Mhlanga, 2020a). One further advantage of using digital financial services is that they help mitigate the dangers offered by cash-based transactions, which include the possibility of theft, loss, and other types of fraud (Mhlanga, 2020a; Muneeza et al., 2018). In addition, the availability of digital financial services improves economic empowerment by lowering the entry

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barriers for women, young people, and those living in rural areas to accumulate assets, which in turn raises their levels of economic participation (David-West, 2015; Mhlanga, 2020a). The third Sustainable Development Goal focuses on ensuring healthy lifestyles for all people and fostering well-being for all people. The analysis of data can lead to the development of new pharmaceuticals. The use of AI enables models of healthcare that are preventative, predictive, cognitive, and individualized. AI algorithms can determine the appropriate medication, dosage, and administration time for each patient when personalized healthcare is provided. To improve patient care, a brand-new field known as surgical data science has emerged, which is predicated on the methodical utilization of big data and AI technology. The promotion of chances for learning that last a lifetime and the provision of an education that is inclusive, equitable, and of high quality are the primary foci of the fourth goal. AI contributes to better education by facilitating the development of novel and interactive massive open online courses and tools, as well as global classrooms, challenge-based learning, intelligent knowledge systems, machine teaching, and strategies for individualized learning. Achieving gender equality and giving all women and girls the ability to make their own decisions is the fifth aim. The use of descriptive analytics software allows for the monitoring and tracking of gender bias, as well as the provision of decision-makers with actionable insights, to encourage balanced hiring. Empowering women economically can be accomplished using haptic and immersive reality, which can be used for skill development as well as training in practical trades. The provision of potable water and adequate sanitation facilities is the focus of the sixth Sustainable Development Goal (SDG), which also seeks to improve access to drinking water and the management of freshwater ecosystems and sanitation facilities at the local level in several developing countries. Not only can AI assist in mapping existing resources, but it can also be used to optimize the production of new infrastructure, develop ways for wastewater filtering and management, and build capacity to safeguard the environment. The use of AI in agriculture also helps fight against poverty. Agriculture is the primary means of subsistence for people living in many of the world’s most impoverished places. AI experts from Carnegie Mellon University are behind the development of a project called FarmView. They do this by employing robotics and artificial intelligence to make staple food crops in underdeveloped countries more resilient. Researchers are integrating technologies

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such as drones, robotics, and machine learning in their investigation of the crop known as “sorghum.” They are researching to determine the most effective strategy for fostering the growth of these plants. Sorghum is utilized in the preparation of food, beverages, and even biofuels. The ability to cultivate sorghum would be of enormous assistance to impoverished regions, most of whose residents depend economically on agriculture for their livelihood. This can be accomplished with the assistance of machine learning by entering the data acquired throughout the growing season into an AI model. This model can then help farmers determine the best ways to cultivate this crop.

Chapter Summary This chapter’s objective was to investigate the potential contributions that artificial intelligence (AI) and financial technology (FinTech) could make to the achievement of the Sustainable Development Goals (SDGs), with a particular emphasis on SDG 1, which has the objective of reducing global poverty. Because it is one of the most pressing issues on a global scale, fighting poverty is one of the most important things that can be done to encourage economic expansion and broaden people’s access to opportunities in developing countries. Through a comprehensive examination of the part that AI and FinTech play in tackling the issue of poverty reduction, the purpose of this chapter is to show how these cutting-edge technologies may considerably advance the Sustainable Development Goals (SDGs). It will specifically investigate how AI could be used to broaden access to financial services, improve the effectiveness and efficiency of aid delivery systems, and extend financial inclusion. This chapter will also examine how artificial intelligence can be used by FinTech companies to make financial transactions more secure and efficient, make it easier to obtain loans, and give support for the growth of small and medium-sized businesses. In conclusion, the purpose of this chapter is to highlight the transformative potential of various innovative approaches in the hopes of inspiring policymakers, researchers, and practitioners to continue looking for new and inventive ways to utilize AI and FinTech in the pursuit of Sustainable Development Goals (SDGs).

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CHAPTER 6

The Role of FinTech and AI in Agriculture, Towards Eradicating Hunger and Ensuring Food Security

Introduction One of the most serious issues facing individuals, groups, and countries around the world is providing access to enough and safe food supply, as stated in goal 2 of the sustainable development plan. “Food security” in this sense refers to the accessibility, affordability, and availability of food that is healthy and nutrient-dense for everyone. To achieve food security, it is important to manage several interrelated factors. They include political unrest, climate change, poverty, and technological advancements. By the year 2050, it’s predicted that there will be over 10 billion people on the planet, making the issue of feeding this growing population a major one. It is projected that factors including urbanization, economic inequality, and climate change would all increase the frequency of food insecurity over the coming decades. Also, the COVID-19 outbreak and the crisis in Russia and Ukraine have highlighted how fragile food systems are and the need to develop more durable and environmentally friendly means of food production and transportation. The Food and Agriculture Organization of the United Nations (FAO) predicts that by the year 2050, there will be more than nine billion people on the planet. Agriculture production will need to increase by a factor of 70 to meet this demand. Just 10% of this enhanced production will probably originate

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from undeveloped places, with the remaining 90% coming from an intensification of production that has already begun. Using the most recent technical developments is still a requirement in this industry to increase farming production. Higher-quality food is needed on the market, yet the methods currently being used to increase agricultural production need significant energy inputs. The improvement of the country’s overall food security can be achieved using data in agriculture. Data-driven farming is the technique of using data to support agricultural decision-making and, as a result, improve the performance of the food system in terms of crop yields, financial gain, environmental sustainability, and food security (Mehrabi et al., 2021). Recent advancements in three key areas, including the generation of data (for example, using mobile devices, field sensors, satellites, and “farmers as sensors”), the processing and predictive analytics of data (for example, using big data stacks, machine learning, and deep learning), and human–computer interactions, have increased the potential of data-driven farming to improve food systems (that is, human-centric approaches to create experiences that improve the ease with which people can interact with computers) (Mehrabi et al., 2021). Given their ability to provide low-income households with access to credit lines and information through FinTech, data-driven initiatives have a great chance of upending global food systems. To address a variety of problems, including low food yields, inadequate human nutrition, disease, and the inability to adapt to changing climates, big data and mobile technologies are being created. Nevertheless, to implement these solutions, access to a mobile network, device ownership, and reasonable cell service subscriptions are requirements. There are still substantial access gaps despite recent increases in mobile network coverage, phone ownership, and a drop in the price of mobile data. According to some estimates, there is still a sizable fraction of the global population without an internet connection. There is a rising worry that if the digital divide is not filled, the Sustainable Development Goals of the United Nations, particularly those relating to education, equity, health, and well-being, may not be achieved (Mehrabi et al., 2021). Mondato (2021) claims that financial inclusion programmes have long acknowledged the advantages of having access to financial services to fight hunger and poverty. When it comes to concerns about the production and security of food, this has been the case. Mondato (2021) went on to say that by providing platforms that reduce waste throughout the agricultural supply chain, digital money and FinTech have recently assumed a more

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significant position in food systems. He accomplished this by emphasizing the long history of FinTech and digital money. According to the FAO, there can be a 20% global reduction in food waste by 2025 and a 50% reduction by 2030. Only by using digital solutions at every stage of the agricultural lifecycle, from production to consumption, can this decrease be achieved. An estimated 1.6 billion tons of food, or more than a third of the global supply, are lost each year, costing $1.2 trillion in lost revenue (Mondato, 2021).

FinTech and Artificial Intelligence on Food Security, Important Literature It is essential, just as it was in the preceding chapter, to review the book’s two most fundamental concepts once more for the benefit of those who will be reading the book, FinTech and artificial intelligence

FinTech The term “financial technology,” which is shortened to “FinTech,” refers to the application of technology to enhance and automate financial services. This encompasses a diverse set of applications, including but not limited to mobile payments, online lending, and digital currencies. FinTech businesses frequently make use of cutting-edge technology such as artificial intelligence and blockchain to develop new products and services that are superior to conventional financial services in terms of their levels of productivity, convenience, and accessibility. The purpose of financial technology is to improve the effectiveness of existing financial services while also making them more available to consumers and businesses of all sizes, including those persons and companies that may not have access to the conventional banking system.

Artificial Intelligence Artificial intelligence (AI) is the term used to describe the process of imitating human intellect in computers by educating them to think and learn similarly to humans. These robots can be trained to do a wide range of tasks, including speech and picture recognition, making judgements, and problem-solving (Mhlanga, 2023). As shown in the preceding chapters, there are various forms of AI, such as rule-based AI, which

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uses systems with pre-programmed rules to make decisions, reinforcement learning AI, which learns from data, and deep learning AI. Natural language processing (NLP), a subset of machine learning that focuses on the interaction between computers and human language, and deep learning are all examples of machine learning systems that can learn from data and improve their performance over time. Deep learning uses neural networks that are modelled after the human brain to process and analyse large amounts of data. Self-driving cars, chatbots for customer service, and medical diagnosis are just a few examples of the many uses of AI. AI can increase productivity and efficiency across a wide range of industries and address difficult issues that cannot be addressed by people alone. Yet, it also brings up moral and societal issues including employment loss, privacy and security problems, and bias.

Food Security Food security is the term used to describe the accessibility, affordability, and availability of safe and wholesome food for all people (FAO et al., 2022). When everyone, at all times, has physical, social, and economic access to enough, safe, and nutritious food that satisfies their dietary needs and food choices for an active and healthy life, that is when food security occurs, according to the FAO. The availability, access, usage, and stability of food are the four basic components that make up the idea of food security (Ingram, 2011). Access refers to a person’s or a household’s capacity to obtain the food they require through a variety of means, such as paying for it outright, exchanging it for something else, or receiving help. Availability refers to the existence of sufficient food to meet the needs of the population. Utilization, on the other hand, refers to people’s capacity to prepare and consume the food they have access to properly, considering things like cultural customs, dietary requirements, and the presence of illnesses or other health issues. The ability to sustain food security over time, despite shocks or other disruptions, is referred to as stability. On the other side, food insecurity happens when one or more of these requirements are not met. This can manifest in a variety of ways, such as hunger, malnutrition, and health issues linked to eating. Almost 660 million people may still experience hunger in 2030, in part owing to the long-lasting consequences of the COVID-19 pandemic on global food security, according to the World Food Programme. Around 690 million people experienced hunger in 2019, and many more

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experience food insecurity in other forms (Food & Agricultural Organization, 2021). One of the main causes of food insecurity is poverty, which makes it difficult for low-income people and households to buy enough food. In the coming decades, it is anticipated that urbanization, economic inequality, and climate change will all worsen food insecurity. The COVID-19 pandemic has also brought attention to the vulnerability of food systems and the need for more robust and sustainable methods of food production and distribution. Addressing several interconnected issues, such as poverty, climate change, political unrest, and technology improvements, is necessary to ensure food security. To ensure food security for all, a multimodal strategy that incorporates technology advancements with governmental interventions, community-based projects, and education and awareness campaigns can be implemented. Figure 6.1 shows the model of FinTech and artificial intelligence in addressing food insecurity issues.

Artificial Intelligence

Food Security, Zero Hunger Fintech

Fig. 6.1 Artificial Intelligence, FinTech, and Food Security

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Financial Inclusion and Food security

Fintech and Efficiency in Agricultural Supply Chains

FinTech and Insurance for Smallholder Famers

Food Distribution and Transparency in Food Assistance Programs

Fig. 6.2 FinTech and Food Security and Hunger

FinTech and Food Security and Hunger Financial technology, or FinTech, has the potential to be very important in maintaining food security. As previously stated, and discussed in earlier chapters, FinTech is the use of technology to enhance and automate financial services including banking, lending, and payments (Leong & Sung, 2018). FinTech companies can offer more effective and accessible financial services by utilizing technology, which can assist to address some of the underlying causes of food poverty. The potential contributions of FinTech to alleviating hunger and food insecurity are shown in Fig. 6.2.

Financial Inclusion and Food Security FinTech has the potential to make it easier for farmers and other people involved in the food supply chain to gain access to financial services, which could play a part in ensuring that food will always be available. Mobile banking and digital payments, for instance, can make it simpler for farmers to have access to credit and other forms of financial assistance, which in turn can assist them in investing in their businesses and boosting their overall output. One way that FinTech might help improve food security is by expanding people’s access to financial services (Mhlanga, 2020). In the

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context of economics, the term “financial inclusion” refers to the capacity of individuals and households, especially those at the base of the economic pyramid, to get access to and make use of formal financial services like banking, insurance, and credit (Mhlanga, 2022a). It is difficult for many individuals and households with low incomes, particularly in developing countries, to participate in formal financial systems. As a result, it can be challenging for these individuals and households to gain access to credit and other financial resources that are required to purchase food. FinTech companies can contribute to the expansion of financial inclusion by delivering digital platforms and services that are easier to use and less expensive to purchase than those offered by conventional banking systems. It has been demonstrated that the use of mobile money services, such as M-Pesa in Kenya, can increase the number of people who have access to financial services and enhance their access to credit, both of which can assist to improve food security (Mhlanga, 2022b). According to the findings of researchers, access to mobile money was largely responsible for lifting many households in Kenya out of poverty (Kikulwe et al., 2014; Suri & Jack, 2016). It had immediate ramifications not just on actual income, but also on aspects such as food security, health care, and sustainable investments. In addition, it had direct implications for real income. M-Pesa has been successful in improving food security in several ways, including facilitating time-sensitive money transfers, dispersing risks across geographical regions, establishing users as creditworthy borrowers, and increasing both the amount of locally produced food and the amount of locally purchased goods and services. M-Pesa served as a demand- and supply-side stimulant in a developing nation like Kenya, where food insecurity is a leading cause of mortality each year. As a result, it helped reduce the number of people who died.

FinTech and Efficiency in Agricultural Supply Chains FinTech can also assist in increasing the efficiency of agricultural supply chains, which can lead to an increase in the quantity of food that is available as well as a decrease in its price. Farmers can find new markets for their products with the assistance of digital platforms and marketplaces, which can connect them with customers and investors respectively. For instance, blockchain technology, which is a subset of FinTech, can be implemented to facilitate the creation of digital records of agricultural

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transactions such as the purchase and selling of crops. This can serve to promote openness and minimize the danger of fraud, both of which can contribute to an increase in the efficiency of supply chains, which in turn can make food more inexpensive for consumers. FinTech can also play a role in enhancing food security by making it simpler for consumers to obtain information about the food they purchase and by providing them with tools that can assist them in making decisions about what they should consume based on a higher level of knowledge and understanding. For instance, blockchain technology can be utilized to generate records of food production and distribution that are both secure and tamperproof. This can assist customers in tracing the origin of their food and ensuring that it is both safe and environmentally friendly. Binkabi and Agrocenta are two examples of FinTech platforms that may be used to link smallholder farmers with buyers and to assist those farmers in gaining access to the financing and other resources necessary to boost crop yields (Mezquita et al., 2020; Quayson et al., 2020).

FinTech and Insurance for Smallholder Farmers Farmers can be protected from crop failures and other dangers by using the new forms of insurance and risk management technologies that can be made available to them by companies that specialize in FinTech. Using technology to enhance and automate insurance services is one way that FinTech companies may make it easier for smallholder farmers to obtain insurance. The following are some of the ways that FinTech companies can assist smallholder farmers with their insurance needs. FinTech companies can, by utilizing digital platforms, establish digital platforms that simplify the process of gaining access to and purchasing insurance for smallholder farmers. These platforms can be accessed through cell phones or the Internet, which can be particularly valuable for smallholder farmers located in distant places who may not have access to traditional insurance providers (Cao et al., 2020; Yan et al., 2018). The evaluation of risk and the setting of premiums is an additional significant facet of FinTech’s role in the insurance industry. FinTech companies can improve the precision of risk assessments and pricing for smallholder farmers by utilizing cutting-edge technologies, such as artificial intelligence and data analytics. Smallholder farmers, who may not have been able to access insurance in the past due to high costs or a lack of data on their farming practices, may find that this helps make insurance more affordable and accessible

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to them. This can help make insurance more affordable and accessible for smallholder farmers. Because of FinTech, microinsurance presents yet another significant avenue for exploration. FinTech companies can provide smallholder farmers with microinsurance products that are specifically crafted to meet their unique requirements. Microinsurance is a term that is used to describe insurance policies and products that are intended for low-income individuals and households. Microinsurance policies and products often have lower coverage limits and premiums than standard insurance policies. Smallholder farmers may have difficulty obtaining insurance at a price that is within their means. Microinsurance solutions may help to remedy this problem. Furthermore, FinTech companies may make it easier for smallholder farmers to pay their insurance premiums by providing a variety of payment alternatives such as mobile money transfers and digital wallets. FinTech companies can also employ technology to automate the claims processing process, which can make it easier for smallholder farmers to get payouts in the event of a loss while also increasing the speed at which the process can be completed. FinTech companies can use digital identity verification technologies to improve the speed and accuracy of the underwriting process. This can help to increase access to insurance for smallholder farmers. The term “digital identity” refers to the practice of assigning a unique identifier to everyone who uses a computer. FinTech is extremely important for ensuring food security and getting rid of hunger, which will ultimately help FastTrack the achievement of the Sustainable Development Goals (SDGs).

Food Distribution and Transparency in Food Assistance Programmes With the provision of technology-based solutions to support financial transactions and logistics, FinTech can contribute to the distribution of food to at-risk areas. To ensure that food aid reaches its intended recipients, these solutions may include blockchain-based supply chain tracking, digital vouchers for food assistance, and mobile payments for food purchases. FinTech firms can also collaborate with government organizations and non-profits to create financial inclusion initiatives that give low-income people access to the resources they need to buy food. In general, FinTech can make food delivery more effective and efficient, particularly in places with limited traditional banking infrastructure.

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Figure 2.2 illustrates different ways FinTech might be used to improve transparency in food distribution to vulnerable areas like refugees. As seen in Fig. 6.3, FinTech can contribute to greater transparency in the food distribution process in several ways, including by using Blockchain technology to create a tamper-proof record of all transactions in the food distribution process, from the initial purchase of food to its delivery to the final recipient. This makes it possible to trace food aid in real time and guarantees that it gets to where it is supposed to. Refugees can receive food assistance using digital coupons. These mobile phone-based vouchers can be used to make food purchases at specific stores. This makes it possible to trace food aid in real time and guarantees that it gets to where it is supposed to. Food assistance for refugees can be provided using mobile payments. Mobile phone payments make it possible to track food aid in real time and guarantee that it gets to where it’s supposed to. Automating the food distribution process and ensuring that help reaches the intended beneficiaries are both possible using smart contracts. These agreements can be used to guarantee that food is delivered to the proper location and that the provider is paid. It’s crucial to remember that FinTech cannot guarantee food security on its own. It must be utilized in conjunction with other tactics, such as economic development, social protection programmes, and sustainable farming techniques. FinTech businesses also need to be aware of the potential drawbacks of their services, such as the possibility of discrimination and data privacy abuses. In conclusion, FinTech has the potential to significantly contribute to maintaining food security through increased financial inclusion, increased supply chain efficiency in agriculture, and increased openness and accountability in food aid programmes. To secure food security for everybody, it is crucial to consider the potential drawbacks of these services and combine FinTech with other tactics.

AI Food Security and Hunger AI can play a role in ensuring food security by improving the efficiency and productivity of the food supply chain. Some specific ways that AI can be used to improve food security include those outlined in Fig. 6.4.

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Fig. 6.3 Ways FinTech can be used to increase transparency in food distribution Crop forecasting

Crop yield prediction

Food Safety and Traceability.

Food waste reduction

Artificial intelligence and satellite imagery

Real-time data for better agricultural decisions

Precision agriculture

Fig. 6.4 AI Food Security and Hunger

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Crop Forecasting In the field of agriculture, artificial intelligence (AI) can be utilized to achieve agricultural forecasting by evaluating multiple forms of data to anticipate crop yields and identify potential problems. Machine learning strategies such as supervised learning, unsupervised learning, and deep learning can be utilized in conjunction with one another to accomplish this goal. To forecast future agricultural yields, supervised learning algorithms can be utilized to assess past data on crop yields, patterns of weather, and other factors. For instance, Jambekar et al. (2018) used a supervised learning algorithm to predict crop yields for crops like Rice, Wheat, and Maize in India by utilizing historical data on weather patterns, soil moisture, and other factors. Jambekar’s model was able to accurately forecast crop yields for these three crops. According to the findings of Jambekar et al. (2018)’s experiments, the performance of Multivariate Adaptive Regression Splines (Earth) is superior to that of Multiple Linear Regression and Random Forest Regression for the Rice and Wheat datasets, while the performance of Multiple Linear Regression is superior to that of Random Forest Regression and Multivariate Adaptive Regression Splines (Earth) for the Maize dataset. Jambekar et al. (2018) also found that the performance of Multiple Linear Regression is superior to that of Multivariate Adaptive Regression. It is possible to utilize unsupervised learning algorithms to recognize patterns in data even when doing so requires no prior knowledge of the outcome. Once again, deep learning algorithms can be utilized to examine enormous volumes of data, such as satellite photos, to recognize patterns and develop hypotheses. According to Ouhami et al. (2021), crop diseases are a significant problem in the agricultural industry since they have a negative impact on the quality and quantity of agricultural products. According to Ouhami et al. (2021), recent technical developments in sensors, data storage, computing resources, and artificial intelligence have demonstrated significant potential in terms of their ability to effectively control diseases. According to Ouhami et al. (2021), a growing body of literature recognizes the importance of using data from various types of sensors and machine learning approaches to building models for detection, prediction, analysis, and assessment. This recognition comes because of an increase in the number of studies that have been conducted on the topic. The overall process of crop forecasting can

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be made more accurate and efficient with the help of AI-based crop forecasting. This is accomplished by evaluating data from a variety of sources and seeing trends that may not be immediately obvious to a human observer. This can ultimately lead to greater crop yields and better use of resources by assisting farmers and agricultural organizations in making more educated decisions on planting, harvesting, and resource allocation.

Crop Yield Prediction In the field of agriculture, artificial intelligence (AI) plays a vital role in agricultural yield prediction by evaluating various forms of data to create predictions about crop yields and identify potential concerns. This allows AI to both predict crop yields and identify potential problems. Artificial intelligence can be used to assess data obtained from a wide number of sources, such as satellite photos, weather patterns, soil moisture, and other characteristics, and it can be used to produce forecasts about crop yields in specific locations and at specified periods. AI algorithms, such as machine learning and deep learning, can evaluate vast amounts of data from a variety of sources, including satellite photos, weather patterns, and soil moisture, to recognize trends and make predictions. Spotting trends that might not be immediately obvious to a human observer has the potential to make agricultural yield estimates more accurate and more efficient. It is vital to keep in mind that the application of artificial intelligence in the sector of agriculture can result in accurate crop output projections. This is something that should be always kept in mind. Thanks to the availability of smart technologies, farmers and other stakeholders may make the most informed decisions possible on the forecasting of crop yields (Hossain et al., 2022; Kamilari et al., 2018). It is possible to ensure that agricultural resources, such as nutrients, equipment, and fertilizers, are used in the most efficient manner possible by employing more intelligent farming practices. Farmers have the option of adopting methods like regression analysis, Bayesian networks, clustering, artificial neural networks, and decision trees to increase their capacity to predict crop yields. Other methods include clustering, decision trees, and artificial neural networks. In addition, we utilized a Bayesian network to evaluate the morphological, biological, and physiological characteristics of crop cultivars in terms of their resistance, production, and oil content (Hossain et al., 2022).

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Food Safety and Traceability Through evaluating data obtained from a variety of sources and providing insights that can assist in recognizing and avoiding concerns related to food safety, artificial intelligence (AI) plays a vital role in ensuring the safety of food and its capacity to be traced. Here are some examples of how artificial intelligence can be used to improve food safety and traceability. AI may be useful in implementing techniques of predictive modelling. AI may be used to construct predictive models that can recognize patterns and anomalies in food safety data, such as temperature records and inspection reports. These models can then be used to determine what caused the patterns and anomalies. These models can be utilized to identify possible food safety hazards before they manifest, hence allowing for early intervention and the promotion of preventative measures. According to Tamplin (2018), food products travel through intricate supply chains, which call for efficient logistics to ensure food safety and maximize shelf life. Predictive models provide an effective means of monitoring and managing the safety and quality of perishable foods, but these models require environmental data to estimate changes in microbial growth and sensory attributes. According to Tamplin (2018), multiple firms are currently producing Time–Temperature Indicators, which react at rates that roughly resemble predictive models. These devices are simple and cost-effective for food companies, and they are produced by several companies. Tamplin (2018), on the other hand, thinks that even better results could be achieved by using sensors that feed data to prediction models in real time. This strategy could assist the food sector to improve food safety by minimizing the danger of bacterial growth in perishable products. Predicting the shelf life of fresh chicken using data on temperature and humidity obtained during storage can be an effective use of machine learning algorithms. When it comes to image identification, AI algorithms can be taught to recognize and categorize photographs of food goods, such as determining whether food has gone bad or been contaminated. Technology can be utilized to automate the inspection process, which will enhance both the speed at which food safety hazards are identified as well as their accuracy. According to Khan et al. (2021), the term “food safety” refers to the process of preparing, transporting, and storing food to prevent foodborne illness and harm. From the farm to the factory and the factory to the fork, food products may be exposed to a variety of health risks. Therefore, food

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safety is essential, both monetarily and morally. The implications of failing to comply with food safety requirements are diverse, and the need for accurate, prompt, and nonpartisan quality assessments of these characteristics in food products continues to rise along with increased demands for nutritional materials and requirements for high-quality. Computer vision, as stated by Khan et al. (2021), offers an autonomous, non-destructive, and cost-effective method of ensuring food safety. Its utility for the evaluation and categorization of fruits and vegetables has been established by a substantial body of research (Khan et al., 2021). Public safety biomonitoring needs to have a rapid and accurate detection method for harmful microorganisms to prevent diseases that are transmitted by food and to guarantee food safety. It is possible to utilize deep learning algorithms to perform even classification of photos of rotten and unspoiled meat, hence attaining excellent accuracy rates in the identification of meat that has gone bad. There are further applications for AI in the tracing and compliance of food. It is possible to utilize AI to do data analysis on the entire food supply chain, from the farm to the consumer, to determine the origin of food safety problems. This can assist speed up the process of identifying potentially contaminated products and initiating recalls, as well as contribute to the improvement of traceability throughout the supply chain. When paired with AI, blockchain technology has the potential to improve traceability by producing records of the route that food products take from the farm to the consumer that cannot be altered. Similarly, AI may be used to automate the process of monitoring and enforcing compliance with food safety laws. This can save a significant amount of time and effort. Monitoring temperature logs, keeping track of food safety inspections, and locating potential compliance issues are all examples of things that fall under this category. For instance, systems driven by AI can automatically monitor temperature data in real time, sending notifications for any deviations from the prescribed temperature range. This ensures that food safety laws are complied with. In general, artificial intelligence plays a crucial part in the process of ensuring the safety of food and tracing its origin by producing insights that can aid in the detection and prevention of problems related to food safety. This can lead to an improvement in food safety, a quicker detection and recall of products that may be contaminated, and an improvement in compliance with legislation governing food

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safety. It is important to keep in mind that artificial intelligence technologies are still in the research and development stage, and they may require human supervision to be effective.

Food Waste Reduction Artificial intelligence (AI) has the potential to play a significant role in the reduction of food waste by improving several aspects of the food supply chain, such as production, supply chain management, and distribution. AI also has the potential to play a significant role in the development of new food products. The use of AI in precision farming, in which AIpowered sensors, cameras, and drones are used to monitor crop growth and predict crop yields to assist farmers in optimizing crop production and reducing food waste, is one application of artificial intelligence (AI) in the fight against food waste. For instance, precision farming can be used to estimate when fruits and vegetables will be ready for harvest. This enables farmers to plan harvests more effectively and cuts down on the quantity of food that is wasted. In the parts that follow, we shall go into further detail regarding this topic. Supply chain management is another application of AI that can help reduce the amount of food that is wasted. AI-powered analytics and machine learning algorithms can be used to optimize logistics, predict demand, and forecast inventory. This can help reduce food waste by ensuring that food is delivered to the right place at the right time, thereby reducing the amount of food that spoils while being transported. In addition, this can help ensure that food is delivered to the right place at the right time. The distribution and retail sectors also have opportunities to utilize AI to cut down on food waste. For instance, image recognition technology that is powered by AI can be used to sort and categorize food items in a speedy and precise manner, which can reduce the amount of food that is wasted as a result of rotting or incorrect labelling. In addition, artificial intelligence may be used to forecast client demand and optimize stocking levels, both of which can help retail establishments like supermarkets and other types of retailers reduce the quantity of food that is wasted. Additionally, artificial intelligence has the potential to play a significant part in reducing food waste by providing insights and automation that can assist in optimizing food production, distribution, and consumption through a variety of mechanisms including predictive modelling, computer vision, traceability, and decision support. This can

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be accomplished through several different mechanisms. AI may be used to construct predictive models that can forecast food demand and optimize inventory management. These predictive models can be employed in the context of predictive modelling. This can assist in lowering the amount of food that is thrown out by ensuring that food is produced and supplied in the appropriate quantity, at the appropriate time, and to the appropriate locations. Computer vision systems that are powered by artificial intelligence can be used to assess photographs of food goods, such as determining whether produce has become overripe or spoilt. Determining which goods should be consumed or sold promptly, can contribute to the reduction of wasted food. In addition, computer vision systems can be used to scan packed goods for flaws, such as damaged packaging, which can assist reduce the amount of food that is wasted by detecting products that need to be removed from the distribution chain. AI can be used to evaluate data from the full food supply chain, from the farm to the consumer, to determine where food waste is occurring and why it is happening there. This is related to traceability. This can help in identifying and addressing the underlying reasons for food waste, such as ineffective inventory management or inadequate logistics, which can be major contributors. Finally, AI can be utilized to assist producers, distributors, and retailers in the food industry with decision support, such as advising the most effective methods for cutting down on food waste. For instance, AI-powered systems may examine data on the weather, agricultural yields, and market circumstances to advise farmers on how to maximize crop yields while minimizing waste. These systems can even predict future weather patterns. In general, artificial intelligence plays a significant part in the reduction of food waste by offering insights and automating processes that can assist optimize the production, distribution, and consumption of food. The application of artificial intelligence (AI) throughout the food supply chain has the potential to drastically cut down on the amount of wasted food by enhancing crop production, supply chain management, and distribution. Yet, it is important to keep in mind that AI technologies are still in the research and development stage, and they may require human supervision to be effective. However, there are also some concerns that AI could make food waste worse by increasing the efficiency of food production, which could lead to overproduction and surplus. AI also requires enormous amounts of data and computational power, and it should be used following ethical and sustainable guidelines.

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Artificial Intelligence and Satellite Imagery The agricultural industry has been revolutionized by artificial intelligence, primarily in the context that satellite imaging has added value to the practice of precision farming. It is fascinating to note that precision farming is currently being pushed forward by the implementation of image-based insight generation (Dharmaraj & Vijayanand, 2018). Using drones for indepth field investigation, scanning of the field, and crop monitoring are all viable options for accomplishing this goal. Rapid answers are possible thanks to the data that can be gathered through drones, computerized vision technologies, and the Internet of Things. In a nutshell, artificial intelligence offers data and analysis of real-time images. It is common knowledge that the use of satellite photography in conjunction with artificial intelligence can be of use to farmers, particularly in Africa (Zha, 2020). In addition to projecting crop yields and future events, nongovernmental organizations such as the World Food Programme (WFP) are working to reduce vulnerability by providing a more granular understanding of the impact that shocks have on agriculture. It was discovered that countries such as China, Japan, India, and the United States of America are in the lead when it comes to farming using satellites (Dastagiri & PV, 2020; Wellington & Renzullo, 2021). It’s interesting to note that Germany and Israel are among the world leaders in satellite farming. The photos captured by satellites of farmers’ fields can be used for a variety of purposes, including observation, computation, and response. This is made possible by a global positioning system, which offers precise information for agricultural techniques. This will go a long way towards improving food security, which will ultimately lead to a reduction in poverty. Agronomists, policymakers, farmers, and those working in the food business all place a significant amount of weight on developing innovative space technology. Satellite photos have the potential to significantly increase crop output in places that are irrigated. The data that was collected can be connected to indices such as the Difference Index (DI), the Enhanced Vegetation Index (EVI), the Leaf Area Index (LAI), the Optimised Soil Adjusted Vegetation Index (OSAVI), the Normalised Multi-band Drought Index (NMDI), and the Normalised Difference Red Edge Index (NDRE) (Hoffmann et al., 2018).

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Real-Time Data for Better Agricultural Decisions Using the precision algorithm will improve the data collected on crop health, illnesses, and field management (Dharmaraj & Vijayanand, 2018). Applications that provide farmers with precise, fast, and customized agricultural data can be used thanks to artificial intelligence technologies, which can improve decision-making processes (Abdullahi et al., 2015; Song et al., 2021). It is noteworthy that remote sensing technology enables the FAO’s WaPOR project to shed light on the potential yield of food per unit of water. Policymakers will find this to be especially helpful since they will be better able to promote excellent farming practices and identify regions for optimal utilization (Dharmaraj & Vijayanand, 2018). Emerging space technologies can be used to generate a lot of data. By preventing consumer foodborne illnesses, artificial intelligence solutions enable food retailers to improve food safety throughout the whole supply chain. Food safety delivery from the producer to the consumer through retail demands a strong food safety culture, trustworthy data, effective database management, and technical know-how. The potential of artificial intelligence to advance and transform food safety processes and results has been proposed (Dastagiri & PV, 2020). Given that experts in food safety play a key role in honing the tools and algorithms that are used throughout the food sector, the need for artificial intelligence skills cannot be overstated. Artificial intelligence encourages the use of cutting-edge tools that assist farmers in safeguarding consumers from foodborne illness and minimizing reputational harm (Kudashkina et al., 2022).

Precision Agriculture As a result of advances in artificial intelligence, farmers now have access to high-precision positioning systems, remote sensing, geological mapping, integrated electronic communication, harvesting time estimators, and soil management systems. These advancements have been hailed for their contribution to the advancement of precision agriculture. Dharmaraj and Vijayanand (2018) and Siregar et al. (2022) emphasized that precision agriculture or smart agriculture can be seen in which farmers employ drones to detect and kill pests and rodents. This type of agriculture is known as “smart agriculture.” The origin of crop management systems may be traced back to 1986 when the first expert systems intended specifically for agriculture were developed. This indicates that

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the farmers were successful in predicting nighttime frost by utilizing artificial neural networks (ANN). An image-based artificial intelligence management system for wheat (Triticum aestivumL.) crop was proposed by Li et al. (2008). The system makes use of pixel labelling techniques for image strengthening. In the future, Siregar et al. (2022) emphasized that data-driven agriculture through the usage of disruptive technologies to increase productivity will be the primary emphasis of intelligent system agriculture. This suggests that it is possible to lessen the effects of poverty and economic inequality through the implementation of intelligent agriculture or digital agriculture. In this regard, agriculture can flourish and cater to the requirements of clients thanks to the application of artificial intelligence (Hossain et al., 2022). In the context of the agricultural industry, the connection between high-precision agriculture and artificial intelligence can be stated in a nutshell. To increase the number of soybeans that are produced, logic technology was developed in the Indian setting. This allowed the system to provide farmers with more accurate advice regarding the fertilizer that should be applied, crop selection, pest-related difficulties, and crop modelling (McGovern et al., 2017). Precision agriculture is a cuttingedge farming strategy that advises farmers on the type of crop that would provide the highest yield in their specific location based on research data on soil types, characteristics, and crop yields. This information is gleaned from studies conducted on various types of soil as well as crop yields. The concept of crop suggestion plays a significant role in precision agriculture and should be practised. The recommendations that are provided for each type of crop consider a wide variety of distinct characteristics. To overcome the difficulties that are normally connected with crop selection, precision agriculture tries to recognize factors in a manner that is particular to a certain location. This directly contributes to a reduction in the number of crop selection mistakes that are made, as well as an increase in overall output. In this article by Pudumalar et al. (2017), the authors propose a recommendation system employing an ensemble model employing a majority voting technique and making use of a Random tree, CHAID, K-Nearest Neighbor, and Naive Bayes as learners. This allows the authors to solve the problem that they set out to solve. The purpose of this method is to, with a high degree of precision and effectiveness, make a crop recommendation based on the unique parameters of the location. Even if the “site-specific” technique leads to better outcomes, it is still important to perform monitoring over such systems. This is the

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case regardless of whether the results have improved. There are a variety of methods for precision agriculture, and not all of them produce reliable results. Show how it is possible, via the use of methods such as remote sensing and artificial intelligence, to identify certain locations of the world that are suffering from a shortage of access to food. When it comes to agriculture, there are plenty of possibilities and unanswered questions. From one growing season to the next, the climate shifts, the weather is erratic, the prices of agricultural inputs fluctuate, the quality of the soil deteriorates, harvests fail, weeds choke out crops, and pests wreak havoc on crops. The burden of managing these unknowns falls on the shoulders of the farmers. The importance of soil, crops, diseases, and weeds to agricultural productivity is examined in this study. The significance of these aspects simply cannot be emphasized, even though the sector of agriculture encompasses a huge variety of different pursuits. It is vital to research the application of AI in agriculture for the management of soil, crops, diseases, and pests. This research must be done as soon as possible. The administration of the land: Having healthy soil, which is also the source of the nutrients that are employed in agricultural production, is the most important factor in achieving fruitful agricultural results. The soil serves as the primary resource for all production methods, including agricultural, forestry, and fishing operations, among others. The soil is responsible for retaining water, minerals, and proteins and making them easily available for the development and growth of healthy crops. This ensures that the crops may be grown in a healthy environment. Production of crops The cultivation of crops is an essential component that is necessary for the continued expansion of the global economy. Food, raw supplies, and prospects for employment are all made available to those in need. It is now generally accepted that important facets of crop production include not just marketing and processing but also distribution, after-sales assistance, and distribution as well. This idea has gained widespread acceptance in recent years. In regions of the world with a low real income per capita, the cultivation of crops and other critical industries are being given high priority. This is because these regions need the money more than anywhere else. It has been noted that a rise in the productivity and output of crops has a significant and positive impact on the overall economic development of a nation. This is something that is to the benefit of the nation. Moving forward, it will be essential, as well as prudent, to place a significant emphasis on the growth of crop

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output. Illness management, Plant diseases reduce both the amount and quality of agricultural produce at a time when the agricultural sector is attempting to provide food for a growing global population. Diseases that manifest themselves following harvesting have the potential to cause catastrophic losses for the agriculture sector. Weeds present a substantial obstacle to any kind of agricultural activity, and these obstacles must be effectively managed. Weeds can occasionally cause harm to cattle, as well as reduce production on farms and in forests, invade pastures, and suffocate crops. Weeds can also create the same problems in forests. They engage in intense competition with the crops for sunshine, nutrients, and water, which results in a drop in the overall productivity of agriculture and a reduction in the quality of the harvests.

Chapter Summary This chapter’s objective was to study the function of artificial intelligence (AI) and financial technology in agriculture to achieve food security. Farming has changed with time because of technical breakthroughs in computer science (artificial intelligence) and finance using financial technology. As a result, the agriculture business has been impacted in a variety of various ways. Agriculture is the primary industry in several countries around the world, and as the world’s population continues to grow (the United Nations predicts that it will rise from 7.5 billion today to 9.7 billion in 2050), there will be an increased demand for land to farm. However, there will only be an additional 4% of land available for farming by that time. In this chapter, it was revealed that artificial intelligence and financial technology are proving to be essential in assuring food security and eradicating hunger. This was a discovery that was made possible as a result of the previous chapter. FinTech companies can provide financial services that are both more effective and more easily available as a result of their use of technology. This, in turn, can assist to address some of the underlying causes that contribute to food insecurity. The application of technology that utilizes artificial intelligence is helping to improve the overall harvest quality and accuracy in a practice that is known as precision agriculture. The detection of plant illnesses, the extermination of pests, and the enhancement of agricultural nutrition are all areas that can benefit from the application of AI technology. Sensors powered by AI can identify and zero in on specific weeds, after which they may choose the most effective herbicide to apply there.

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CHAPTER 7

Financial Technology, Artificial Intelligence, and the Health Sector, Lessons We Are Learning on Good Health and Well-Being

Introduction The usage of AI and machine learning in the healthcare industry has increased (Mhlanga, 2022a; Senthilraja, 2021). The healthcare industry has employed AI in areas related to pandemic prevention and control, as well as diagnosis, categorization, detection, severity, and mortality risk. Many academics hold the view that AI has become more prevalent even before the epidemic, and that powerful AI algorithms have been built to handle challenging tasks successfully (Senthilraja, 2021). During the COVID-19 epidemic, AI was one of the key strategies utilized to monitor and control the virus’s spread. According to Senthilraja, medical practitioners began seeking methods to track and manage the outbreak (2021). Contrarily, Harrus, and Wyndham (2021) emphasized the fact that new technologies always have unintended as well as intended consequences. Yet, the use of AI to combat the harmful consequences of the disease that decimated the whole planet has made the technology’s potential so apparent. The number of AI-based applications soared as soon as the pandemic in China began. According to Harrus and Wyndham (2021), the COVID-19 pandemic prompted AI practitioners to alter their practices. The pandemic provided a once-in-a-lifetime opportunity to show how AI may benefit humanity. But the problems with technology use persisted,

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1_7

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much like the pandemic itself. Harrus and Wyndham (2021) claim that the use of AI in the fight against COVID-19 unintentionally exacerbated the issues already afflicting the underprivileged worse. Global social inequities worsened because of the COVID-19 epidemic. The use of technology like AI did not dramatically alleviate healthcare disparities, resource inequality, or lack of access, particularly for the most disadvantaged segments of society. The American Public Media Research Lab’s research, for instance, shows that trade workers and their families were more likely to be infected than other groups in a variety of industries, such as cashiers, cleaning crews, delivery services, restaurant servers, and trade workers. Senthilraja (2021) asserted that artificial intelligence (AI) was essential for tracking the virus’s spread, identifying high-risk patients, and real-time pandemic control despite misgivings and the pandemic’s detrimental impacts on humanity. Senthilraja (2021) went on to say that by skilfully assessing patients’ past data, AI helped in estimating the mortality risk. Senthilraja (2021) also brought attention to another important issue: AI helped in the fight against COVID-19 by screening patients, doing medical examinations, helping with notifications, and offering advice on infection control measures. Once more, AI improved COVID-19 patient planning and treatment (Senthilraja, 2021). According to Islam et al. (2021), AI should be applied to deal with the impacts of the COVID-19 epidemic, particularly concerning diagnosis, categorization, detection, severity, and mortality risk. Big data, machine learning, and other cutting-edge technologies must be utilized to their utmost extent to benefit healthcare generally, according to a different study by Vaishya et al. (2020a). In their review of the literature, Vaishya et al. (2020a) discovered that a variety of apps were used to spot clusters of COVID-19 cases and to predict the areas where the virus would strike in the future by accumulating and examining previous data. Vaishya et al. (2020a) assert that the management of viruses and the development of vaccines both greatly benefit from the use of decision-making tools like AI. A second study by Khan et al. (2021) found that AI has successfully mitigated the negative impacts of the deadly COVID-19 epidemic. AI has been used for the COVID-19 pandemic detection, screening, categorization, drug repurposing, viral prediction, and forecasting, according to Khan et al. (2021). FinTech, or financial technology, is another significant technical advancement that is transforming the healthcare industry. Peer-to-peer lending, crowdfunding, mobile payments, and

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money transfers were the first examples of FinTech, but its activities have since expanded to encompass more contemporary innovations like blockchain, cryptocurrencies, and robo-investing (Simon, 2021; Nour, 2022). According to Parvaiz Hussain (2022), an increasing number of start-ups are now utilizing the FinTech playbook to address a significant issue in healthcare: the changing relationships between patients, payers, and providers, as well as the flow of payments and associated electronic health records (EHRs). In addition to the shift in responsibility for payments and data flow from payers to patients, providers are currently dealing with a significant transition to new billing and engagement models, necessitating new technology and platforms to support the change. To improve its services, the healthcare industry has been progressively incorporating technology. Particularly the FinTech sector has greatly aided the healthcare industry in giving much simpler tools for payment during a time of need where the digital payment method has solved the problem of long waiting times, hence simplifying the payment process for both the hospitals and the patients (Parvaiz Hussain, 2022). Considering this, the chapter explores the potential contributions that AI and FinTech can make to the fight against the Covid 19 Pandemic. The research will move on to look at the goals of sustainable development and the things we are discovering about the Fourth Industrial Revolution. Particularly, the third objective is concerned with general health and well-being.

FinTech and Healthcare The ability of digital transformation to reach underserved and distant populations has been proven by the FinTech sector. Digital banking has made it possible to offer reputable, official financial services to people who would not otherwise have access to them through a combination of offline services (finance agents) and technology (mobile wallets). In addition to facilitating the transfer and storage of money, digital banking has aided unbanked people and small-to-medium-sized businesses in the establishment of credit, the creation of financial records, and the reduction or avoidance of transaction fees, third-party payment costs, lost wages due to time off from work, and labour costs related to cash-based payments. FinTech, like “health tech,” is an ambiguous phrase that refers to all technology created, applied, or improved for a certain business, in this case, the finance industry. Digital ID, mobile

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money, artificial intelligence, robotics, and blockchain are examples of technologies that frequently fall within the FinTech category. These technologies are applied to digital financial services such as payments, savings, loans, advising, and insurance in both business-to-consumer (B2C) and business-to-business (B2B) models. Despite the hoopla, not all of these ideas or technologies are instantly applicable to problems with health financing (Grassi & Fantaccini, 2022; Nathan et al., 2022). The three parts of FinTech for health technology are digital financial solutions, significant digital enablers, and the gateway to healthcare services. To provide unbanked or underbanked consumers with innovative health finance options, there are four primary categories to consider: digital health savings, digital lending, crowdfunding, and insurtech (Grassi & Fantaccini, 2022; Mistry, 2019; Nathan et al., 2022). In Fig. 7.1, numerous health finance options are outlined. As shown in Fig. 7.1 there are four main archetypes of innovative health financing solutions that can be offered to unbanked or underbanked consumers, using technology, digital health savings, digital lending, crowdfunding, and insurtech.

• Digital saving allow individuals to set aside money digitally for healtcare expenses

Crowd Funding • Donations-A pool of funds contributed by all members. • Mutual aid-a pool of funds, contributed by all members

Digital Savings

Fig. 7.1 Health financing solutions

• Insurtech and digital insurance are providing more choice in insurance offerings by therapeutic areas and consumer types.

Insurtech

Digital Lending • Digital lending supported by alternative credit scoring tools enables lending and extends reach to underdseved communities.

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FinTech and Healthcare Literature The use of financial technology in the healthcare sector is referred to as FinTech. Simplify and automate financial procedures including billing, claims administration, and patient payments, this can involve leveraging digital platforms, mobile apps, and other technology. The usage of financial services like insurtech, health savings accounts, and other financial products specially created for the healthcare business is another example of how FinTech is used in healthcare. The objective is to increase healthcare services’ general effectiveness, affordability, and accessibility for both patients and providers. As a result, a lot of studies are being done to attempt and understand how FinTech fits into the healthcare industry. According to Meiling et al. (2021), healthcare organizations have new goals for sustainable practices that will help them increase their financial performance without eroding social and natural capital. Maintaining a robust, resilient, and sustainable healthcare system helps economies achieve sustainable competitiveness, claim Meiling et al. (2021). Meiling et al. (2021) used panel data made from 11 Asia–Pacific nations to evaluate the impact of FinTech development on the sustainable performance of healthcare organizations. According to Meiling et al. results’s from 2021, financial institutions and FinTechs are working together to make financing more accessible to people and businesses. Meiling et al. (2021) discovered that the association between FinTech development and sustainable performance is positively moderated by financial and ICT development. Crowdfunding, which raises money directly from a larger and more varied audience of investors, is emerging as a substitute source of fundraising for medical objectives, according to Grassi and Fantaccini (2022). Grassi and Fantaccini (2022) conducted an exhaustive analysis of the literature on medical crowdfunding to ascertain how it relates to the healthcare sector. Grassi and Fantaccini (2022) claim that the healthcare sector has had difficulty creating research and business models that will be financially viable and appealing to investors, particularly in pharmaceuticals. Grassi and Fantaccini (2022) argued that regardless of the healthcare system archetype public, private insurance-based, or hybrid—patients and caregivers use web platform-based campaigns all over the world to fund their medical expenses, typically on a spot basis, using donationbased or even reward-based schemes. Academics, according to Grassi and Fantaccini (2022), have also concentrated on funding campaigns

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and success predictors that range from social behaviour and environment to the demographics and health conditions of the campaigners as well as social and regulatory issues like increased social inequality and stigma. According to Grassi and Fantaccini (2022), even though equity crowdfunding is changing the way many ventures and businesses look for funding, our research shows that, aside from a few anecdotal examples, there are no pertinent or reliable data on the practice of medical equity crowdfunding in the field of healthcare. Blockchain and distributed ledger technology are also described as disruptive forces in the healthcare industry by Ribitzky et al. (2018). Technology development, according to Hassan et al. (2022), has aided the transition to new financial services. Several industries have undergone a digital transition because of the growth of cashless payment systems and other cutting-edge technology, according to Hassan et al. (2022). In the Bangladeshi healthcare industry, Hassan et al. (2022) investigated the elements that influence patients’ intentions to use FinTech services. According to Hassan et al. (2022), there is a considerable correlation between the patients’ intention to embrace FinTech services and perceived simplicity of use, social influence, enabling circumstances, personal inventiveness, and perceived trust in those services. Additionally, according to Nour (2022), the emergence of Financial Technology (FinTech) as a disruptive technology that is now influencing the financial sector through services like crowdfunding, peer-to-peer lending, and alternative underwriting platforms has resulted from advancements in financial services. Nour (2022) suggested that, given the web’s importance as a source of information, accessible and useable online FinTech services can help individuals with disabilities utilize the available financial services to meet their needs, including getting high-quality care, on a basic level. According to Nour (2022), websites occasionally have many accessibility and usability issues that breed prejudice against people with disabilities and lead to a subpar user experience. Thus, Nour (2022) thinks that more must be done to spread the word about how important it is to create appropriate FinTech websites and services to help disabled individuals.

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FinTech in the Healthcare Sector FinTech, often known as financial technology, contributes to the delivery of healthcare by introducing novel and forward-thinking approaches to the management of and payment of healthcare services. This can involve the utilization of digital payment systems, electronic medical records, and data analysis to improve the efficacy of healthcare services and their accessibility. In addition, FinTech companies may also provide specialized financial tools and services, such as loans, insurance, and investment products, for both healthcare professionals and their patients. FinTech can improve the overall quality of the healthcare experience for both patients and providers by making use of various technological and data-driven tools. Digital payment is one significant area in which FinTech has the potential to play a role in the delivery of healthcare. Patients can quickly pay for healthcare services without having to use cash or a check by making use of digital wallets, mobile payments, and other digital payment options. Patients also have the option to pay with a credit card. Not only does this make transactions faster and more efficient, but it also lowers the likelihood that they will be fraudulent or contain errors. Patients can also benefit from increased flexibility and control over their healthcare spending when they use digital payment methods (Mhlanga, 2022b). M-TIBA, a digital payments and administration platform, was established in Kenya in 2015 to facilitate access to healthcare for the country’s low-income residents, who are the ones most negatively impacted by out-of-pocket medical bills. Even though it is not genderspecific, the M-Tiba service’s design and implementation were carried out in a manner that was executed in a way that can aid enhance financial inclusion, particularly for women (Mhlanga, 2022c). The FinTech and payments administrators Care Pay, Pharm Access Foundation, Safaricom, and UAP Insurance are among M-service TIBA’s partners. These partners offer a wide array of financial options, including health insurance for beneficiaries, health funds, commitment-based mobile savings accounts, and payment administration services for clinics and donors (Mhlanga, 2022d). Furthermore, M-TIBA offers management services to connected health insurers as well as healthcare providers. The use of electronic medical records is another area in which FinTech has the potential to play a role in the delivery of healthcare (EMRs). Electronic medical records (EMRs) enable medical professionals to safely store and retrieve patient information electronically, hence enhancing

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the likelihood that patient records will be accurate and comprehensive. EMRs can also relate to other healthcare systems and services, such as databases of prescription drugs and insurance claims processing systems, to improve the overall efficacy and coordination of the delivery of healthcare. In addition, FinTech has the potential to play a part in the delivery of healthcare by developing financial tools and services that are specifically geared toward the needs of healthcare practitioners and patients. For instance, FinTech companies may offer loans and other forms of finance to healthcare providers to assist them in the acquisition of pricey equipment or the expansion of their facilities. Insurance and investment products, such as health savings accounts or long-term care insurance, may also be provided by FinTech companies as part of their product line-ups. These products are typically geared specifically toward healthcare professionals and patients. In conclusion, FinTech has the potential to play a part in the delivery of healthcare by supplying data analysis and other tools to improve the quality of healthcare overall, both for patients and for healthcare professionals. For instance, FinTech companies may utilize techniques such as data mining and machine learning to analyse patient data to recognize trends that may be useful in the creation of novel medicines or in the identification of patients who are at an increased risk. According to Parvaiz Hussain (2022), FinTech has the potential to assist in expanding the availability of telemedicine to more marginalized individuals. The conventional medical system is under a great deal of strain as a direct result of the rising prevalence of lifestyle-related disorders and the accompanying rise in healthcare expenses. Despite this, modern technologies have the potential to lighten the load placed on hospitals by enabling real-time consultations with medical professionals via mobile devices such as smartphones, tablets, and laptops. Furthermore, FinTech platforms have ties to telemedicine and offer services in addition to insurance on the same platform. Companies that deal in digital payments have a far bigger reach and client market than firms that deal in digital health; as a result, they can package services like telemedicine and online pharmacy onto their payment systems. In general, the function of FinTech in the delivery of healthcare is multidimensional, and it has the potential to improve the overall experience of receiving and providing healthcare for patients as well as for providers. FinTech can improve the effectiveness,

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accessibility, and convenience of healthcare services by harnessing technology and data, which may ultimately lead to improved health outcomes for patients.

Artificial Intelligence in the Health Sector It is wonderful to see how the number of published materials describing the influence that artificial intelligence has had on COVID-19 is growing. Many authors, such as Senthilraja (2021), Adadi et al. (2021), Islam et al. (2021), and many more, have detailed how artificial intelligence (AI) has been applied in the realm of health, specifically in connection to the effects of COVID-19. Among these authors is Senthilraja., 2021. Despite this, there is widespread agreement among the researchers that reviewed the available literature regarding the application of AI in the medical field, particularly regarding the deleterious effects of COVID-19. The current study’s objective is to investigate the implications of the lessons we have learned about the Fourth Industrial Revolution and the sustainable development objectives, particularly goal three, which focuses on ensuring that people have good health and a happy and fulfilling life. Senthilraja (2021) discovered that artificial intelligence is necessary to counteract the adverse effects of COVID-19. For example, Senthilraja (2021) found that AI has been utilized to anticipate activities such as physicochemical properties. This was an interesting discovery. Senthilraja, in the year 2021, discovered additional evidence demonstrating the value of AI in the care and health monitoring of COVID19 patients. AI has been utilized at several different scales, including in medical, biological, and epidemiological applications, to monitor COVID-19. In addition, Senthilraja (2021) asserted that artificial intelligence has helped in COVID-19 research by contributing to the analysis of the data that is already available and the development of new pharmaceuticals. The information provided by Senthilraja (2021) is evidence that AI has been utilized to assist in mitigating the negative effects of COVID-19. According to the findings of another study (Adadi et al., 2021), AI was essential to the reaction to the COVID-19 epidemic. According to Adadi et al. (2021), the rising interest in employing AI to address COVID-19 problems has led to a growth in the number of articles and review studies that have been published in a short period as a result of the expansion in artificial intelligence (AI) research.

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According to a paper that was written by Islam et al. (2021), the pace at which AI is being used in the field of health has drastically increased as a result of the COVID-19 outbreak. Moreover, Yu et al. (2018) made a tangential mention of how AI is influencing how medical practice is carried out. According to Yu et al. (2018), one of the reasons for the rise in the use of AI in the health business is the breakthroughs that are being made in big data capture, machine learning, and the development of computer infrastructure. This was listed as one of the reasons for the rise in the use of AI. According to Yu et al. (2018), the usage of artificial intelligence (AI) has already begun to creep into spheres that were traditionally reserved for human activity as a result of advancements in data gathering and computational capability. Research conducted by Davenport and Kalakota (2019) in addition to Yu et al., provided support for the arguments that Yu et al. (2018) had made. According to Davenport and Kalakota (2019), the adoption of AI in the healthcare business is being driven by the expansion in the amount of data that is being collected. According to Davenport and Kalakota (2019), artificial intelligence is currently being utilized for a variety of administrative activities in addition to the referral and diagnosis of treatment options. It is also being utilized in some instances to enhance patient participation and adherence to treatment plans. Davenport and Kalakota (2019) noted that even though AI can already be used in many situations, including those in which people have traditionally performed the tasks, it will be difficult for humans to be completely replaced by AI shortly due to some implementation limitations. Even though AI can already be used in many situations, including those in which people have traditionally performed the tasks, in addition, Reddy et al. (2018) offer arguments in favour of the hypotheses proposed by Yu et al. (2018) and Davenport and Kalakota (2019). Furthermore, Reddy et al. (2018) asserted that the development of deep neural networks, robotics, computer vision, and natural language processing have all contributed to the exponential growth of AI technology in recent years. According to Reddy et al. (2018), the fact that all these AI technologies are being used in healthcare has reached the point where it is likely that AI will replace clinicians and administrators in their positions in the coming years. This prediction is because AI is currently being used in healthcare to such an extent. The argument that although AI will play a key role in the delivery of healthcare, it is erroneous to expect that AI would replace and take

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over the roles of human physicians was a particularly noteworthy one. This argument was presented by Reddy et al. (2018). On the other hand, AI will have a considerable impact on clinical decision support, health treatments, patient monitoring, and patient administration. According to Reddy et al. (2018), artificial intelligence will be an essential part of health systems that are either AI-enabled or AI-augmented. AI is reportedly helping in the fight against COVID-19 and giving speedy cures that were previously unreachable in several industries and applications, as stated by Sipior (2020). According to Sipior (2020), ever since the COVID-19 epidemic, there has been a rise in the investigation and utilization of AI, as well as a growth in the number of data analysis tools used in a wide variety of disciplines. Sipior (2020) discussed several management issues that must be taken into consideration before the successful deployment of AI applications. Some of these factors include planning, the potential for skewed outcomes, the value of data, and diversity in the membership of AI teams. Finally, Sipior (2020) concluded that, even though people are looking for easy solutions, a serious analysis of the difficulties associated with the development and application of AI is necessary. Vaishya et al. (2020b) investigated artificial intelligence’s role in the study, prevention, and control of pandemics like COVID-19 and others. According to Vaishya et al. (2020b), AI has been used in seven different ways, including the identification of cluster cases and the forecasting of the areas where the virus will have a significant impact in the future. Among these applications is the forecasting of the areas where the virus will have a significant impact in the future. According to Vaishya et al.’s (2020b) findings, artificial intelligence (AI) has the potential to be of great assistance in the process of creating vaccines, generating predictions, and monitoring both current and potential patients. It was also emphasized that artificial intelligence is very good at mimicking human intelligence. This point was emphasized. Based on the analysis of the relevant literature, AI is making substantial headway towards solving problems associated with COVID-19. The question that is still unresolved is, “What can we learn about the fourth industrial revolution and sustainable development goals from using AI to tackle Covid-19’s impacts?”.

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The Role of AI and Machine Learning in Overcoming Health Challenges AI can significantly impact the way that healthcare is provided. The following part will provide a full description of what AI can do in the delivery of healthcare with a greater emphasis on the COVID-19 epidemic. This section summarizes some examples of how AI is presently being utilized or could be used in the future. AI has a significant role to play in diagnosis. By analysing medical photos and other data, AIdriven diagnostic technologies can help clinicians detect diseases and disorders. For instance, a mammogram can be trained to look for symptoms of cancer, and this training of an AI system could speed up and improve the accuracy of tumour identification by radiologists. The availability of personalized medicine is another crucial component of AI. To develop individualized treatment strategies, AI can be used to examine a patient’s genetic information and medical background. AI-driven systems, for instance, can determine which medications are most likely to be successful for a specific patient and which ones should be avoided due to possible interactions or negative effects. The delivery of healthcare is also changing thanks to virtual assistants. By answering queries, setting up appointments, and sending notifications for medicine and other treatments, AI-powered virtual assistants can support patients in managing their healthcare. Clinical decision support is an additional crucial factor. AI may be used to analyse vast volumes of data from clinical trials and electronic health records to give doctors on-the-spot therapeutic recommendations. A further critical component is predictive analytics. AI can identify people who are most likely to get a given illness, like diabetes or heart disease, so that preventive actions can be performed. Robotic surgery is yet another crucial component of AI. To increase accuracy and lower the chance of complications, surgical procedures are increasingly using AI-powered robots. As if AI isn’t helpful enough for remote surveillance. AI can be used to remotely monitor patients with long-term diseases, such as heart failure, to spot signals of decline and take the necessary measures. Lastly, AI can aid in the search for new drugs. Artificial intelligence (AI) can be used to evaluate vast amounts of data to find potential new drugs and forecast how well they will function in the body. The emphasis on AI’s involvement in healthcare delivery will be highlighted in the part after this, with a focus on the COVID-19 epidemic. The strength of AI as a technology from the Fourth Industrial Revolution has been partially

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clear but undocumented since the beginning of the COVID-19 outbreak (Harrus and Wyndham, 2021). The health sector has employed AI applications for a variety of purposes, including disease predictions, population monitoring, medication development, and approval, to name a few. As a result of the pandemic, there has been a rise in the use of AI software to combat the virus’s negative consequences. The section that follows discusses the ways AI was applied to counteract the negative consequences of the virus.

Accelerating Complications of COVID-19 Research and Treatment One area where artificial intelligence has demonstrated its value and effectiveness in the battle against disease is the development of COVID-19 vaccinations. AI was used to design the COVID-19 immunizations as well as to identify old vaccines that may be used to build new vaccines and medications (Harrus and Wyndham, 2021). It has taken a long time and the integration of various basic science disciplines, such as pharmacology, chemistry, and biology, to create medications and vaccines (Harrus and Wyndham, 2021; Ho, 2020). The conventional approach to drug development and the associated timelines are summarized in Fig. 7.2. Figure 7.1 shows a schematic illustration of the traditional drug research and development process. It took between three and six years to complete the research and development process, which included target identification, compound screening, and lead discovery. Preclinical testing took around a year, and clinical studies spanned 4 to 7 years. It normally takes one to two years for drugs to be reviewed and approved. Out of the hundreds of thousands of chemical compounds that are developed and examined to locate the one that meets the standards, only one in 1000 drugs normally go from preclinical investigations to clinical trials. Only one in ten drugs that make it past phase 1 of clinical trials are ultimately developed for commercialization, according to data from the FDA (2015). It takes over 10 years and more than $2.5 billion to develop a pharmaceutical that will be approved by the US Food and Drug Administration, according to DiMasi et al. (2016). Pharmaceutical companies were driven by the COVID-19 pandemic to come up with new, inventive ways to develop COVID-19 vaccines that will aid to reduce expenses and the length of time it takes to combat the COVID-19 pandemic.

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Research and Development 3-6 Years

Preclinical Studies 1 Year

Clinical Trials 4-7 Years

Review and Approval 1-2 Years

Target Identification What will the drug affect?

Invitro Studies Latin for'In glass'

Phase 1 Trials Usually 20-80 people

Evaluation Evidence from Trials

Compound Screeing 10 000+ Compounds.

Invivo studies Latin for 'in the living'

Phase 2 Trials Usually 100-300 People

Approval and Manufacture Approx 75% Submissions approved

Lead Identification Which Compounds to test.

Required Standards Minimum 2 mamalian species

Phase 3 Trials Usually 1000-3000 people

Post-Release Monitoring Indefinite duration

Fig. 7.2 Traditional drug development process (Source Author’s Analysis Important Information was taken from Mhlanga [2022a])

Harrus and Wyndham (2021) assert that artificial intelligence (AI) can contribute to the Industrial Revolution 4.0, which will quicken and reduce the cost of medicine development. Around the middle of 2010, AI-based algorithms to produce pharmaceuticals started to demonstrate their full potential, according to Harrus and Wyndham (2021). Several pharmaceutical organizations have either purchased, merged, or formed collaborations with AI-focused software companies to take advantage of AI’s benefits in drug research. In the early stages of drug development, AI-based algorithms can be used, according to Smalley (2017), to reduce the number of compounds considered and omit the drugs thought to cause negative side effects. Harrus and Wyndham (2021) claim that the COVID-19 pandemic increased the use of artificial intelligence (AI) in drug research and the repurposing of already-approved drugs. Repurposing existing medications provides the advantage of hastening the process of drug approval because the treatments are already in use and have known and quantified negative effects. Drug approval will be based more on how well a drug performs in actual use than on the original use for which it received approval. According to Richardson et al. (2020), Benevolent AI, a startup that uses AI for medication research and identification, came up with

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the idea to treat COVID-19’s severe symptoms with the rheumatoid arthritis drug Baricitinib. The maker of the medicine, Eli Lilly (2020), has worked with the US National Institute of Allergy and Infectious Diseases, and positive clinical trial outcomes, according to the information that is now accessible. Without the aid of AI, it would be hard to establish a connection between arthritic medication and COVID-19 (Lilly, 2020; Simonite, 2020). AI-based algorithms were successful in developing and adapting drugs to counteract the negative consequences of the COVID19 pandemic. According to Harrus and Wyndham (2021), research on the development and repurposing of pharmaceuticals should continue to advance even after the COVID-19 epidemic. The Fourth Industrial Revolution and the technology powering it, such as artificial intelligence, have the potential to greatly help accomplish Goal 3 of the Sustainable Development Goals. This information about the capability of AI in creating and repurposing drugs is informing us of this.

AI, Forecasting, and Customer Communications Scaling The COVID-19 virus was first discovered by AI-powered forecasting programmes, and their success encouraged a significant increase in the use of AI in the COVID-19 pandemic response. Several AI apps were used globally to provide information about the disease. One example is BlueDot, a health monitoring company in Canada that forewarned its employees and customers of the possibility of a new outbreak of a disease like pneumonia coming from China’s Wuhan Province. According to Neiiler (2020), BlueDot informed its customers seven days ahead of the US Centers for Disease Control and Prevention and 10 days before the World Health Organization (WHO) issued a warning (CDC). Because of the use of enormous amounts of both health-related and non-healthrelated data, the company was able to apply AI techniques to forecast the disease pandemic and how the illness was going to spread. BlueDot’s ability to predict the cities where the disease would be discovered next was one of its amazing accomplishments (Neiiler, 2020). The prediction of the spread of COVID-19 and the level of threat the virus posed was another notable use of AI. For instance, a study that looked at the information that was available regarding the Hubei pandemic scenario was published by Li et al. in 2020. Because of the potential of big data, Li

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et al. (2020) was able to predict the viral evolution pattern using the data that was previously accessible. The study was successful in creating controls that were extremely helpful for the pandemic. Due to the availability of data, Li et al. (2020) was able to predict the pandemic’s development trends in countries like South Korea, Iran, and Italy. All this information shows that artificial intelligence was effective in predicting the propagation of the virus, which helped to limit its spread and negative impacts. Senthilraja (2021) asserted that AI systems had already discovered the virus’s breakout before the virus’s hazards were even acknowledged to the public. Even after the pandemic, it is critical to continue utilizing various Ai technologies to support decision-makers in the medical sector and environment so that every stage of the problem is successfully managed. The data presented above merely serves to demonstrate how much AI can help with the development of relevant policies that can help address potential epidemics and even sustainable development goals (Senthilraja, 2021). The other areas in which the use of AI in the fight against the pandemic was particularly effective were the diagnosis, containment, and monitoring of the virus.

Identification, Control, and Monitoring One of the equally important aspects of virus containment was a precise virus diagnosis. Given the speed at which the virus was spreading, one of the areas where the effectiveness of restricting it resided in the early detection and screening of the virus. According to Nguyen et al. (2020), various AI applications were proposed at the peak of the pandemic. These applications were applied differently in various circumstances because of the difficulties in creating efficient and effective AI models utilizing data that has problems accurately reflecting the population composition on which the AI models are applied. Some of these applications were used in small-scale studies rather than being effectively used on a big scale. Despite these problems, the importance of AI applications grew because of, among other things, the early detection of COVID-19 cases that resulted in a decrease in the demand for hospital beds. Applications utilizing various deep learning techniques reached more than 100 as early as March 2020 in China, and according to Zhou et al. (2020), similar applications were implemented in Italy as early as April 2020. (Castiglioni et al., 2020). Some AI applications reportedly assisted in differentiating

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between COVID-19 chest X-rays and other conditions like influenza pneumonia, according to Zhou et al. (2020). Several mobile applications were developed, according to Kondylakis et al. (2020), to decrease the negative impacts of the COVID-19 pandemic and smooth the curve brought on by the virus’s rising prevalence. Several mobile applications were used for information exchange, risk assessment, contact tracing, decision-making, and home monitoring, according to Kondylakis et al. (2020). Further evidence of the use of several mobile health applications, mostly for contact tracing and symptom monitoring, in the management of the COVID-19 disease was found by Singh et al. (2020). Patel and Verma (2020) once more mentioned passing the potential use of mobile applications for remote first-degree triage of individuals taking a cough test for additional screening and medical care. Most of these mobile applications, according to Patel and Verma (2020), are essential for lowering the frequency of unnecessary hospital visits and the inappropriate use of limited medical resources. Senthilraja (2021) went on to make the case that big data from media platforms, including social media, combined with information on the risk of infection and the rate of illness spread might be used to track and predict the characteristics of the virus. Another key element in creating pandemic response methods is AI’s capacity to predict positive cases and fatalities anywhere.

AI and How to Understand Spread, therapies, and cures for Diseases One of the applications that were frequently employed was geofencing, also referred to as “green passports.” Geofencing was used as a marketing strategy before the epidemic by tracking the position of the owner’s cell phone to find out where they were. This was done to inform customers about the goods and stores around. During the pandemic, commercial enterprises used geofencing extensively for quarantine. According to Hui (2020), geofencing was utilized in China to monitor people during quarantines. Health officials were alerted to infected places using geofencing as well. All of these were taking place because of the strength of AI (Culham, 2020). According to Wesner (2019), the United States of America did not employ geofencing since it could restrict personal freedom of movement and could be abused for personal advantage. As part of the implementation of a vaccine passport in Australia, the

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Australian Immunization Registry, a centralized database that keeps track of every immunized person, was established. Nations including Denmark, the European Union, Israel, and the Netherlands all accepted the vaccine passport. Senthilraja (2021) also noted how AI has become effective for diseases like COVID-19 due to the requirement for surveillance. Applications that may help in tracking human movement and the virus’s transmission were required because elements related to human activity, such as migration, were to blame for the virus’s global spread. One of the first instances was Blue Dot, a company that used AI, machine learning, and natural language processing to track and report the progress of the virus. Senthilraja (2021) also stressed the value of AI in the treatment and cure of COVID-19-related disorders, particularly when real-time data analysis is used. Data analysis often produces up-to-date knowledge that will aid in halting the spread of the disease. By predicting the places where infections will occur and where the virus will enter, the knowledge generated by AI can even go further to emphasize the need for beds and healthcare (Senthilraja, 2021). Senthilraja (2021) also argued that by figuring out the traits, underlying causes, and mechanisms governing the virus’s spread, AI can be a helpful tool in preventing future infections.

Fourth Industrial and Sustainable Development Goals and AI Lessons Looking at what AI was able to accomplish against COVID-19 in a short amount of time can teach us a lot about how it affects the third Sustainable Development Goal. How AI behaved in the conflict with COVID-19 can teach us a lot about the implications of the Fourth Industrial Revolution for accomplishing goal three, “Ensure healthy lives and promote well-being for all.” According to the United Nations (2021), “child mortality, enhancing maternal health, and combating HIV/AIDS, TB, malaria, and other diseases” have all witnessed considerable advances since the creation of the Millennium Development Goals (2021). The UN also observed that from a peak of 3.1 million to 2 million people annually, the number of people contracting HIV for the first time declined in 15 years. Furthermore, it was claimed that malaria saved the lives of 6.2 million individuals. The reduction in maternal mortality, which has declined by 45% since 1990, is the other achievement. It was reported that globally, “the number of preventable child deaths has decreased

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by more than 50%.” Despite all of these developments, the COVID-19 health emergencies highlighted the differences in how quickly various countries were able to recover from the crisis, and even though not all of the countries were on track to achieve the Sustainable Development Goals by 2030, their efforts had been thwarted by the pandemic. The audacious goal of “health and wellbeing for everyone, including the eradication of the epidemics of AIDS, TB, malaria, and other communicable diseases by 2030” underwent a substantial setback. Despite the dangers associated with the pandemic, we have witnessed the power of artificial intelligence (AI) in several contexts, including advancing the research and treatment of COVID-19-related complications, anticipating and enhancing customer communications during the pandemic, diagnostic testing, containment, and monitoring, and even deepening our understanding of how COVID-19 spreads and is treated and cured. Considering this, we believe that nations can advance beyond pre-Covid development routes if they invest in AI due to AI’s capacity to change economies. To three of the Sustainable Development Goals and its goals, what are the effects of AI on attaining sustainable development? Fig. 7.2 depicts the targets for goal 3 of the sustainable development goals. Everyone can contribute to achieving global goals, according to the United Nations (2020). Looking at what AI has been doing in the fight against the epidemic, we may conclude that AI may help to develop activities that can help in enhancing health and well-being as well as the achievement of goals. Figure 7.3 lists a few ways AI can help achieve sustainable development objectives: Figure 7.4 displays the data, and the success of AI in the fight against COVID-19 is proof that, if employed in healthcare, it may considerably speed up the attainment of Sustainable Development Goal 3 and its targets since healthcare will be more accessible and inexpensive as a result of AI. According to Vinuesa et al. (2020), the application of AI in numerous businesses necessitates careful evaluation, especially regarding its effects on sustainable development goals. Vinuesa et al. (2020) claim that 134 SDG targets can be accomplished with the help of AI. National governments should support AI development by providing regulatory direction and oversight for AI-based technology to ensure that sustainable development is accomplished. The study by Reddy et al. (2020) supported the idea that the use of AI in healthcare is becoming more evident and is expected to be included in routine clinical treatment shortly. Reddy et al. (2020) also referred to the fact that due to the

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Targets of SDG3

Ensure health lives and promote well-being for all

End All Preventable Deaths Under 5 Years of Age

Fight Communicable Diseases

Reduce Mortality from NonCommunicable Diseases and Promote Mental Health

Reduce Road Injuries and Deaths

Universal Access to Sexual and Reproductive Care

Achieve Universal Health Coverage

Reduce Illnesses and Death from Hazardous Chemicals and Pollution

Support Research, Development and Universal Access to Affordable Vaccines and Medicines

Increase Health Financing and Support Health Workforce in Developing Countries

Improve Early Warning Systems for Global Health Risks

Reduce Maternal mortality ratio to 70 per 1000 live

Fight Communicable

births

Diseases

Prevent and Treat Substance Abuse

Implement the WHO Framework Convention on Tobacco Control

Fig. 7.3 Targets of sustainable development goal 3 (Source Author’s analysis)

potential that AI offers, governments and technology companies all over the world are focusing more on investing in AI medical applications. According to Holzinger et al. (2021), AI is the driving force behind digital transformation and has a colossal potential to improve people’s lives and the environment. Holzinger et al. (2021) claim that AI can aid in the invention of novel solutions to the urgent issues that humanity faces in many areas of life, such as healthcare and agriculture. Notwithstanding the possibility that AI could assist in achieving Objective 3’s goals, authors such as Reddy et al., Morley et al. (2020), Truby (2020), and Holzinger et al. have noted serious issues (2021). Holzinger et al. (2021), for example, point out that while using AI can help with the identification of creative solutions to global problems, it is also associated with several unknown hazards. Governments, legislators, businesses, and academia must therefore make sure that these dangers are considered. According to Holzinger et al., “safety, traceability, transparency, explainability, validity, and verifiability of AI applications in our daily lives are ensured” (2021). The following is a list of some of the moral conundrums in Fig. 7.4

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The role of AI in heatlhcare that can assist in the attainment of the SDG3 and its Targets

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Reduction in costs of Visting Hospitals, drug development

Artificial intelligence personal asstance, AI is reducing time and cost of drug development

AI can help in serving time

AI is doing a lot of tasks that were previously done by doctors and reseachers

AI helps in accurate decision making

Through big data AI can easly do records analysis and provides accurate decision

Reduction in Human Mistakes

AI can help doctors in doing some complicated tasks such as data analysis

Vertual Assitants for Doctors

These can help to monitor patients sending real time data to doctors

AI can help in drug development through resech and development

AI can reduce time taken and cost in drug development

Fig. 7.4 The roles of AI The role of AI in healthcare that can assist in the attainment of SDG3 and its Targets in the post-Covid World

Figure 7.5 illustrates a few of the ethical and legal issues surrounding the use of AI in healthcare. Notwithstanding these ethical reservations, the study discovered that AI significantly contributed to reducing the risks and problems related to the COVID-19 epidemic. This discovery led to the notion that, when used effectively, AI and machine learning can considerably aid in the accomplishment of sustainable development objectives, with an emphasis on goal 3 and its associated targets. There are several ways to do this, as was already discussed, but more crucially, artificial intelligence (AI) can help discover healthcare access disparities and be used to guarantee that everyone has access to it.

Conclusion and Policy Recommendation The integration of financial technology (FinTech) and artificial intelligence (AI) in the health sector has immense potential to improve access to healthcare services, reduce costs, and enhance patient outcomes. While there are risks associated with the use of these technologies, such as data privacy and security concerns, the benefits outweigh the risks. This chapter has highlighted key lessons learned from the use of FinTech and AI in the health sector, including the importance of responsible use of

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Fig. 7.5 Ethical and regulatory aspects of the application of AI in healthcare

these technologies to ensure positive health outcomes for all. As the use of FinTech and AI in the health sector continues to grow, it is essential to ensure that these technologies are utilized in a way that promotes good health and well-being. This can be achieved by prioritizing patient privacy and security, fostering collaboration between healthcare providers and technology companies, and continuously monitoring the impact of these technologies on patient outcomes. By doing so, we can leverage the power of FinTech and AI to transform the health sector and improve health outcomes for people around the world. In conclusion, this chapter underscores the potential of FinTech and AI to revolutionize the health sector and emphasizes the importance of responsible use of these technologies to achieve good health and well-being. As we continue to explore the possibilities of these technologies, we must remain vigilant in ensuring that they are used in a way that promotes positive health outcomes for all.

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CHAPTER 8

Financial Technology, Digital Transformation, and Quality Education in the Fourth Industrial Revolution

Introduction With the beginning of the Fourth Industrial revolution, technology has completely reorganized and aggressively directed the fundamental teaching and learning methods that are employed all over the world using digital technology. This has caused a sea change in how education is delivered. The arrival of the Fourth Industrial Revolution has emerged as the key driving force behind the rise in the number of educational possibilities available to people all over the world. Individuals who work in higher education feel that the rapid development of technology in the twenty-first century has made it feasible to create an environment that is conducive to successful learning and teaching. This belief is shared by people who work in other sectors of education as well. Despite this, educational institutions that are in rural areas confront challenges, the most prevalent of which are low reading and literacy abilities, inadequate reasoning and logic skills, and inefficient usage of the technology assets that are accessible. According to Traif et al. (2021), in the age of contemporary technology, the world has been fighting for the greatest technological instruments, which have contributed significantly to improving living conditions while simultaneously delivering exceptional services to customers and businesses at the same time.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1_8

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FinTech is one of these technologies that is described as a characterization of an advanced technology that aims to improve the provision of financial services in automated and critical techniques with a huge potential that can decrease effort and time and save a significant amount of costs that were previously used. FinTech is one of these technologies that is described as a description of a modern technology that aims to improve the provision of financial services in automated and critical methods. The application of finance technology assists businesses, banks, business owners, and consumers in the management of financial services by utilizing pre-developed algorithms that are managed through computers and electronic clouds. This provides consumers with the ability to access data related to financial services whenever and wherever they choose. In addition, the term “FinTech” has only been used during the past few years. Initially, the term was used for the technologies that were utilized within the back-end systems of financial institutions, particularly banks. In today’s world, the term “FinTech” has come to encompass a broader range of technical breakthroughs and automated processes. These developments now include the front-end side, which assists customers in managing and operating the whole range of financial services offered by the financial sector. According to Traif et al. (2021), the proliferation of new mobile services has significantly contributed to the ease with which one can make a payment, the ease with which one can transfer funds between accounts, as well as account balances, and the provision of a great deal more professional services. Traif et al. (2021) also alluded to the fact that FinTech has unsettled just about every area of the world’s financial system; the traditional bankers began to work on creating electronic databases to cope with the great development of financial technology because of the hesitation of consumers to conventional banking that does not provide advanced FinTech services that help time and effort apart from that the FinTech has emerged as one of the pillars of competitive advantages that banks have. According to Glovory Tech (2021), to address the issue of affordability in the education sector, startups operating at the intersection of the FinTech and education industries are concentrating their efforts on developing cost-cutting strategies for educational institutions and developing methods by which students can obtain affordable education financing. Schools are looking for solutions that will help them improve their payment collection and tracking processes, apply for and receive loans

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with sector-specific underwriting to go towards facilities and curriculum and obtain insurance to mitigate risks associated with school infrastructure. These goals can be accomplished through a combination of these strategies. Families who have children enrolled in these schools require a method to save money for their children’s future education and a strategy to cover the costs of tuition across the entire spectrum of educational levels, including vocational school. In a separate piece of research, Kaddour et al. (2022) studied different viewpoints on the role that higher education plays in the growth of financial inclusion. Kaddour et al. (2022) argued that financial services, which were traditionally provided with the assistance of incumbents, are rapidly becoming unbundled through the development of a new set of start-ups and new technologies, following new models of collaboration, and a massive shift in power. Kaddour et al. (2022) believe that financial technology (FinTech) had a key part during the Covid-19 pandemic and will continue to expand. This trend demonstrates that FinTech imposes a tremendous challenge that is related to innovation and regulation. Because of this, Kaddour et al. (2022) feel that higher education should contribute to the improvement of this ecosystem by offering a consistent curriculum. Kaddour et al. (2022) presented a set of initiatives that may be taken in the short term, the medium term, and the long term to establish a specialized curriculum in the field of FinTech. According to Kaddour et al. (2022), to benefit from the high innovation capacity of students in these nations, measures that promote technology transfer should be addressed. Additionally, Traif et al. (2021) argued that financial technology is encouraging a variety of new practices, such as a decrease in the use of cash in various countries, an increase in the rate at which mobile payments are used, the introduction of new algorithms for high-frequency trading across national boundaries, etc., which is attracting a significant amount of attention. Nonetheless, Traif et al. (2021) feel that the continued usage of FinTech is still challenged by researchers. They aimed to comprehend whether higher education students, who are potential business owners, would be eager or hesitant to use FinTech and why this would be the case. According to the findings of Traif et al. (2021), perceived risk has a negative effect on the intent to continue using FinTech, while convenience has the largest favourable effect.

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The Fourth Industrial Revolution and Education Education, along with several other elements, is one of the primary contributors to the development of the positive and negative characteristics of any culture, region, or country (Fomunyam, 2019). According to Fomunyam (2019), the success of any civilization wherever in the world is reliant on the standard of its educational system. This is because enormous advances have been made in areas such as technology, the economy, and industrial know-how. Au (2008) stated that education is an essential and fundamental component of a nation’s economy to have for it to compete successfully in the international market. Education is one of the factors that can assist in connecting persons in the workplace, as stated by Munozovepi (2019), and it is correct that education works as a building brick that assists employment. Munozovepi (2019) followed by stating that education is the tool by which people in any society may actively engage in the economy and become economically self-sufficient. He stated that this is possible through education because education is the instrument. On the other hand, traditional employment opportunities are in danger of being extinct as a result of Industry 4.0, and certain industries are becoming less competitive. Simply said, this fact alone calls for a modification in the way that people think about education. Education has developed over the years and decades, and each revolution has required the application of a certain strategy to attain its ultimate goal; Industry 4.0 is not an exception to this rule (Fomunyam, 2019; Mhlanga, 2020). Historically, education has been structured along the lines of a single discipline, which has required students to limit the scope of their experiences and concentrate on a single topic as they move through their studies. As a consequence of this, those who had a traditional education would acquire an extremely specialized skill, which would enable them to qualify for a certain occupation or trade (Munozovepi, 2019). The thought process that went into deciding to pursue a career path that was limited to a single field was predicated on the notion that specialization was seen as a tool that might assist in increasing the economic value and productivity of an individual. This idea frequently played a role in determining the level of remuneration for human capital. One illustration of this would be the fact that a general practitioner typically receives far less money than a doctor who specializes in a certain field. This pattern is observable in a wide variety of professions. However, Industry 4.0 is now distinct from the old model of teaching and learning;

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it now requires people who are well-equipped in a variety of sectors outside of their specialized jobs. These individuals are referred to as having a “T-shaped” or “interdisciplinary” skill set. Thornburg (2002) argued that “we are on the cusp of a completely new era, and changes must be made in education to ensure that all students leave school prepared to face the challenges of a redefined world.” Changes need to be made in education to ensure that all students leave school prepared to face the challenges of a redefined world. People in twenty-first-century society have a lot of expectations placed on them because of the rapid changes that are occurring in the political, cultural, social, economic, and technological environments. These once-trivial inventions personal computers, social networks and platforms, and mobile phones are now having such a significant impact on people’s daily lives and the culture of society that it is difficult, if not impossible, to imagine life without them (Magout & Magout, 2020). These technologies influence nearly every facet of our day-to-day lives, including the workings of businesses, the conduct of educational institutions, and even how people are governed. Employers are unable to fill vacancies because hundreds of millions of young people around the world, notably in Africa, are either unemployed or underemployed and there is a youth unemployment crisis (Deloitte Global Business Coalition for Education, 2018). This is a problem that is made worse by the widening gap between the ability of young people and the expectations of employers. If nothing were done about it, the issue would almost certainly get worse as the pace of the technological revolution quickens (Deloitte Global Business Coalition for Education, 2018). Because of this, society will need to adapt its skills and its knowledge across the board to keep up with the shifting nature of the environment (Magout & Magout, 2020). Many people in many nations across the globe believe that educational institutions have a responsibility to equip teenagers with the knowledge and capabilities essential for this transition. This begs the question of whether the standard of education and teaching is up to par with the requirements of the current day. As a direct consequence of this, educational institutions are under a great deal of pressure to maintain compatibility with the rapidly shifting requirements and expectations of society. The widespread use of emerging technologies in the field of communication has had a considerable effect on the methods used in educational institutions all over the world. For students to be properly prepared for the twenty-first century, they need to possess skills that are

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significantly different from those that were necessary around twenty years ago (Reaves, 2019). Companies in today’s market are looking for young candidates who have developed new skills, such as the ability to solve problems, interact with others, and work effectively in teams. Learning that continues throughout one’s life and the part it plays in the growth of a knowledge society is also at the top of the agenda (Mhlanga, 2022a; Moon & Seol, 2017). Every learning that takes place should have as its primary objective the promotion of the growth of the skills and capabilities outlined above. Hence, for the government to successfully prepare students to live, work, and thrive in the twenty-first century, it must develop innovative approaches to the educational system. Learning that is guided by the learner’s interests, learning that is focused on collaboration, learning that is experiment-based, and active learning are all examples of modern educational techniques (Imenda, 2014). There is a debate going on in the educational community regarding whether technology can help mitigate many of the difficulties that are associated with societal alterations in attitudes and how education is delivered (Magout & Magout, 2020). When new educational technology becomes more generally available, it becomes increasingly vital to rethink traditional teaching and learning processes. This is because resources are becoming scarcer, and demand for higher education of higher quality is significantly increasing.

Industry 4.0, Education and Digital Transformation The term “Industry 4.0” is typically used to refer to the present trend of automation and data sharing in industrial technologies, including cyberphysical systems, the Internet of Things, cloud computing, and cognitive computing, as well as the development of the smart factory. On the other hand, according to Costley (2021), Education 4.0 is one of the “new experience-based education systems that use digital technology in place of the rote-based system and reacts to the demands of the modern world through individualized education.” Technology, individualism, and discovery-based learning are all combined to educate students for the workforce of the future in Industry 4.0 education. Personalized education and focusing more on success in life than tests are two of the core ideas of Education 4.0. (Costley, 2021). Some significant aspects that will

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Fig. 8.1 Education 4.0 (Source Author’s analysis)

result from the restructuring of the educational system in Industry 4.0 are succinctly depicted in Fig. 8.1.

Industry 4.0 in Education In contrast to the old method of learning, which is mostly centred on the rote-based approach, 4.0 relates to new elements. These elements of independent education in time and place, continuous improvement, data interpretation, project-based learning, flexibility in learning, personalized learning, guidance-oriented, and student participation in the curriculum are depicted in Fig. 8.1. Students are exposed to situations where they can learn anywhere and whatever they desire, which is known as time and space autonomous education. Education is becoming more independent of place and time thanks to interactive learning tools. While practical learning is done face-to-face in the classroom, issues relating to theoretical learning can be handled outside of the classroom. Due to the availability of many e-learning tools, the prevalence of temporal independence implies that the learner can conduct learning even in their room. All these activities have resulted in reducing our reliance on structures. Let students “convert their knowledge into real-life experiences through practical project-based learning activities in the classroom” when they can learn the theoretical portions of their studies independently in a digital environment. Personalized learning is defined as a circumstance where

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students receive individualized instruction using different digital resources that are tailored to their needs, talents, and other requirements. Students that study slowly will be able to accelerate their learning thanks to individualized instruction. Another benefit of personalized learning is that it can help learners who struggle feel supported as they face their obstacles. With the use of software and technology, teachers can easily keep track of their student’s progress and understand how they tend to approach different courses, enabling them to identify the disciplines in which their pupils are strongest and weakest. The traditional educational system uses the same model for all students, which is different from flexible learning. Learning in the Fourth Industrial Revolution is adaptable, allowing for the application of several models and pathways to achieve the same objective. From education 1.0 through education 3.0, students were given access to the same material using the same teaching methods. Although several effective and efficient strategies were used in education 3.0, flexibility was not achieved. Every student is advised to follow a flexible global education approach in “Education 4.0.” Education 4.0 also includes project-based learning, where students are expected to use their knowledge on actual projects rather than responding to questions on an exam. One of the learning areas in the project learning class is called Maker, where students “transform into self-sufficient people by using the person’s talents in a fun way in numerous areas, especially technology.” Through play, maker culture hopes to prepare students. The skills that students can gain in the project-based learning domains depicted in the picture below include cooperation, teamwork, problem-solving, and even time management (Fig. 8.2). As was discussed before, learning through projects can assist students in the development of skills such as problem-solving, collaboration and teamwork, time management, and a solution-oriented mindset. Because disciplines like mathematics are becoming increasingly important in people’s daily lives, Education 4.0 also includes instruction on how to evaluate data. This is one more component of Education 4.0. The existence of robots that will perform a greater portion of the mathematical calculations necessitates the ability of humans to draw conclusions based on the data that has been realized. Students and graduates participating in Education 4.0 should have the ability to organize, manage, develop, gather, process, and understand data. Pupils should be able to identify patterns in data and draw conclusions and suggestions based on the information provided. Another critical component of the education system of

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Fig. 8.2 Project learning outcomes (Source Author’s analysis)

the Fourth Industrial Revolution is the elimination of standardized testing in favour of ongoing evaluation and evaluation of progress. Students are expected to reply to questions and answer them during an examination if they are participating in either the traditional or the modern kind of learning. After the test, the pupils will forget everything they have memorized, which leads many academics to believe that this method of education and evaluation is only useful for the short term. Instead of relying on evaluation tests, education uses the 4.0 scale instead. This is one of the most important components. The entire term, as opposed to just one test, will be taken into consideration for the evaluation. For students to put their knowledge into practice, they need to come up with a variety of ongoing activities. According to one illustration provided by Costley (2021), “children learning to code can design a calculator or a game that they can utilize in their daily lives, rather than merely memorizing theoretical information.” By the completion of these tasks, the theoretical information will be translated into a practical experience that can be retained in longterm memory. Students will be involved in the creation of the curricula as

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part of Education 4.0, which is another essential component of the new curriculum. It is extremely crucial to involve the students in the process and consider their suggestions to tailor the content to the interests of the students. This procedure will help to ensure that the subject matter included in the curricula is pertinent to the goals that the students hope to achieve in their lives and not just to pass the test. Another significant facet of Education 4.0 was its orientation towards providing guidance. Because of this aspect’s central importance, educators will increasingly take on the job of serving as mentors rather than as just knowledge disseminators. “Teachers are not going to be replaced by technology,” Munozovepi (2019) stated quite plainly; nonetheless, “technology in the hands of exceptional instructors will be a transformational phenomenon.” Mhlanga (2022a, 2022b) further claimed that the importance of digital transformation in the education sector is because it promotes “accessibility, cooperation, communication, value diversity, active and social learning, self-direction, content engagement, project learning, and global exposure.” According to Kehdinga and Fomunyam (2019), the evolution of science, technology, engineering, and mathematics (STEM) has been compelled by the development of new technologies, machines, scientific investigations, and inventions. Moreover, Sahin (2018) highlighted that school systems all around the world are embracing the new available technologies and that STEM education is one of the new methods that are being implemented in education systems. Even in the United States, people are becoming more open to new ways of teaching. In 2013, the former President of the United States of America, Barack Obama, said that “one of the things that I have been focused on as president is how we create an all-hands-on-deck approach to science, technology, engineering, and mathematics.” This shows that even in the United States, people are becoming more open to new ways of teaching. We need to make this a top priority to train many new teachers in these subject areas and to ensure that as a nation we are giving these topics the respect that they are due (Fomunyam, 2019). The second line of reasoning put out by academics is that more emphasis should be placed on STEM education to cultivate students who are equipped with the knowledge and abilities necessary to meet the challenges of the twenty-first century. According to Fomunyam (2019), some of the skills that need to be developed include “collaboration, communication skills, creativity, problem-solving, perseverance; information literacy;

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media literacy; global awareness; self-direction; social skills; literacy skills; civic literacy; social responsibility; innovation skills; critical thinking; technical skills; and digital literacy.” These are just some of the skills that need to be developed.

Digital Transformation in Education Digital transformation refers to the process of utilizing various forms of technology to improve teaching and learning results in all areas of the educational system. In the field of education, “digital transformation” refers to the process of enhancing a company’s basic business processes to meet the expectations of its customers more successfully by using data and technology. In the field of education, a target customer could be a student, faculty member, staff member, or graduate, and both students and teachers stand to gain from the implementation of digital technology in educational institutions. Allowing students to access through a mobile app or web application is one example of how digital transformation may improve the overall student experience. Another example is providing students with a diverse set of options for online education. Using technology to monitor the academic progress of students, enforcing intervention procedures, and offering online class organization capabilities are all examples of this. This also covers the utilization of digital resources, online platforms, and instructional software to improve the overall quality of the educational experience for students as well as for teachers. Some examples of educational technology include online education, virtual classrooms, electronic books, and educational apps (Balyer & Öz, 2018; Mhlanga, 2021, 2022a, 2022b). Higher education is currently going through a period of tremendous upheaval as a direct result of the integration of technology into the instructional process. The widespread adoption of digital technologies in higher education is having a profound impact not just on how students’ study but also on how instructors instruct. This shift is being brought about by the mandate to provide children with a learning environment that is more engaging, customized, and effective in preparing them for life in the digital age (Balyer & Öz, 2018; Bilyalova et al., 2020; Mikheev et al., 2021; Mhlanga, 2023). Because of the advantages brought about by the digital transformation of higher education, students now can access educational resources whenever and wherever they choose. Because of this, a greater number of students who

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are unable to attend traditional schools made of brick and mortar because they dwell in rural areas, have impairments, or work full-time jobs can now do so using online education. Once again, tools and instructional software give educators the ability to personalize the educational experience for each student, catering it to the specific needs and interests of that learner (Kagoya, 2020; Vivi Friedgut, 2021). It has been established that the use of personalized instruction in the classroom leads to increased levels of motivation and engagement among the students. The utilization of online learning environments and virtual classrooms offers several benefits, one of which is the facilitation of real-time collaboration and communication between students, as well as between students and their lecturers. It is possible to improve the overall quality of the educational experience by fostering a feeling of community and encouraging students to work together. As a result of the proliferation of digital tools and software for education, educators are now able to track the development of their students in real time and provide instant feedback. Because of this, students are better able to recognize both their strengths and their weaknesses, and they can focus their efforts on improving the areas in which they require assistance. The other advantage of the digital revolution in higher education is that it has lessened the need for physical classrooms, textbooks, and other resources, which has resulted in a fall in the cost of education. This has helped improve access to education for people from all walks of life, and it has made it easier for students to enrol in higher education programmes.

Key Areas of Digital Transformation in Education The term “digital transformation in education” refers to the process of incorporating digital technology into every facet of the educational system to improve both instruction and student retention. “Online learning, learning management systems (LMS), Personalized learning, Collaborative learning, Gamification, Artificial intelligence (AI) and machine learning (ML), Virtual and augmented reality (VR/AR), Blended learning, EdTech innovation, and Digital credentialing” are some of the key areas of digital transformation in education. Increased student engagement, greater learning outcomes, and more efficient and effective educational processes are all possible results of the core areas of digital transformation in education that are indicated in Fig. 8.3.

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Fig. 8.3 Key areas of digital transformation in education

The Main Focuses of Digital Transformation in Education Are Shown in Fig. 8.3 Online learning is the creation and delivery of courses, programmes, and degrees that students can access at any time and from any location. The usage of software platforms that support course management, administration, tracking, reporting, and the delivery of educational content is referred to as learning management systems (LMS). Technology is used to design personalized learning experiences that are tailored to each student’s particular needs, interests, and talents. The use of technology to facilitate group work and project-based learning that promotes teamwork, communication, and problem-solving abilities is another crucial component of collaborative learning. Gamification and collaborative learning enhance students’ learning experiences. Gamification refers to the incorporation of game-like features into the learning process to engage and inspire students, such as points, badges, leaderboards, and simulations. The use of artificial intelligence (AI) and machine learning (ML) algorithms to enhance student evaluation, deliver real-time feedback, personalize learning, and increase student engagement and motivation also falls under these categories. Although using VR/AR technology to create immersive learning experiences and imitate real-world situations is known

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as virtual and augmented reality (VR/AR). Blended learning combines online and conventional classroom-based instruction to produce a flexible and individualized learning environment. The creation and use of cutting-edge educational technology to promote teaching and learning are known as “EdTech innovations.” On the other side, digital credentialing refers to the use of digital technologies for managing, verifying, and sharing academic and professional achievements.

Financial Technology, Digital Transformation, and Quality Education in the Fourth Industrial Revolution Using creative financial solutions, FinTech is transforming the educational industry by enabling access to high-quality education. Students can now use loans, scholarships, and other forms of financial aid to pay for their education, enabling them to pursue their academic objectives free from the burden of financial limitations. FinTech is also enhancing how education is delivered by giving pupils individualized learning opportunities through tech-enabled tools and platforms. FinTech is assisting in the promotion of social and economic prosperity by lowering the price and boosting the accessibility of education.

FinTech and Student Loan-Based Ed FinTech FinTech is also working wonders around paying for college. FinTech businesses provide payment methods that make it simple for parents and students to cover the cost of tuition, books, and other educational expenses. This makes paying for school easier and simpler by getting rid of the burden of conventional payment methods. To assist students in covering the cost of their education, FinTech companies are providing cutting-edge financial solutions like student loans, scholarships, and bursaries. This has widened access to education and lessened the financial strain on both students and their families (Glovory Tech, 2021). Particularly, to prevent fraud, some platforms have teamed up with academic institutions to have the money sent directly to the universities. The loan can be returned in a set amount of time, typically two to three years, in monthly instalments. These apps have fraud detection built in, in contrast to a typical bank, which requires a drawn-out procedure of

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data verifications. It confirms the applicant’s assertions about where they reside and where they attend school using GPS data. Marsh (2017) asserts that as FinTech technologies proliferate around the world, our way of life is evolving. Because of advancements in financial technology, activities like buying a morning coffee by waving your watch, converting foreign currencies online, and sending money to someone on the other side of the globe have all become straightforward and commonplace. FinTech innovations are also emerging in student loan systems, such as peer-to-peer lending and loan refinancing. FinTech firms are providing cutting-edge student loan alternatives and financial planning tools through student financing, which enables students to efficiently budget for their education and manage their student loans. This makes it possible for students to continue their education without being concerned about the cost and to concentrate on their academics. Panos and Wilson (2020) claim that at an unmatched rate, FinTech is changing the financial services sector. Divergent opinions exist regarding how FinTech will likely affect social welfare, personal financial planning, and well-being. Financial education and informed financial advice are suitable policy interventions that improve financial and general well-being in an era of escalating student debt, growing digital financial inclusion, and threats resulting from instances of online financial fraud. Erlangga and Krisnawati (2020) looked at the impact of FinTech payments on student financial management behaviour in a different study. FinTech payments, according to Erlangga and Krisnawati (2020), had a favourable impact on students’ financial management practices in the Bandung Raya Region. A FinTech start-up called SoFi provides services for job progression and student debt refinancing. It offers resources for financial planning, support with job searches, and professional development programmes to students, assisting them in making decisions about their education and careers. To offer more inexpensive alternatives to pay off student debt, a group of Stanford Business School graduates started SoFi in 2011 as Social Finance. It was the first FinTech company with a U.S. basis to receive a $1 billion fundraising round in 2015. Another FinTech business, Upstart, evaluates a student’s creditworthiness for student loans using artificial intelligence. It gives students, especially those who do not have a credit history or who are unable to get a loan from conventional sources, a more equitable and accessible lending choice. Another FinTech business that provides a simple and safe platform for paying for college is Flywire. With Flywire, parents and students can pay for education in their local currency without

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having to deal with the fees and conversion costs that come with more convenient payment options. In a sense, Flywire is a simple payment solution that saves time and money for institutions and their payers while streamlining the payment process for overseas payers. We are modernizing the payment system to make it simpler, quicker, more transparent, and less expensive for payers to send money abroad.

FinTech and Education Management Allovue is a pioneer software platform that enables school districts to design and plan budgets as well as monitor expenditures to make decisions. According to Glovory Tech (2021), ed-FinTech is a web app for education allocation management, and Allovue is a pioneer software platform. This category was initially established by Allovue. It does this through a tool it offers called Balance, which assists regional governments in more effectively monitoring the distribution of their education budgets. Balance is a platform that allows regional schools’ financial data systems to be connected to school-level data like attendance and state test scores. This allows for tracking of both the budget and spending at the school level. The decision-makers would have a higher chance of noticing potential improvements and loopholes if they had access to the data. Yet, the utilization of this technology makes it possible for the choice to be made in a timeframe very close to that of real-time. Jess Gartner, the founder of Allovue, feels that her platform could assist educators in making more informed decisions by bridging the gaps between education, finance, and technology. It is important to keep in mind that strict adherence to the budget is obligatory to guarantee that the student will receive the highest possible level of education. Gartner, who has a considerable background in education as a teacher, intends to enhance student outcomes by ensuring that money gets to the appropriate channels and needs. Gartner has a significant background in education as a teacher (Glovory Tech, 2021). According to Payal Jain’s research (2022), FinTech apps are helping students fill the knowledge gap in personal finance by providing students with tailored tools to learn about the topic. It would be a beneficial addition to the current curriculum for financial education in schools to have access to a FinTech app that could educate children with an introductory crash course on everything from saving, investing, debt, and student loans to the principles of personal finance.

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By providing students with resources to learn on their own, financial technology apps relieve some of the stress that teachers face.

FinTech and Online Learning E-learning platforms have been built by FinTech companies, and these platforms offer online courses and programmes that are designed to meet the requirements of students from all over the world. This not only gives students access to education of a high level but also paves the way for them to learn from the very best in their respective industries, opening a world of options for them to do so. One such company is Funngro, which offers a prepaid card in addition to an app-based platform for managing children’s finances. It provides young people with an appbased platform for managing their finances, which not only assists them in managing their finances but also gives them a platform from which they can earn their own money. Apps such as Funngro make learning simple and enjoyable, which encourages greater participation from children in their education regarding money management. The stress that is placed on parents and children because of difficult financial issues is being alleviated by the efforts of several FinTech companies, who are increasingly using technology in their efforts. The paradigm is being progressively shifted because of the use of technology, which makes the process of planning more pleasurable and straightforward. Not only can financial technology applications for children instruct children about important financial concepts such as saving, investing, and compound interest rates, but these programmes also assist children to keep track of their money and expenses by defining boundaries and goals for them (Jain, 2022). Kids are more likely to be interested in learning about personal finance if they use these applications since they make learning simple and exciting. It is never too late to begin teaching your children the significance of saving and investing their money, and they will be grateful to you for doing so in the future. Encouragement of children’s participation in financial education programmes at an early age will give them the tools they need to build a secure financial future (Jain, 2022). When children understand the concept, they may have the ability to influence their families by providing them with knowledge on the benefits of saving money and encouraging them to take the steps necessary to effectively manage their finances. Because of this, encouraging children to become financially literate and

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boosting their level of financial awareness may prove to be highly advantageous. What we are seeing is that FinTech can transform the academic sector by offering groundbreaking solutions that enhance the educational experience and make it more accessible to everyone. This is what we mean when we say that FinTech has the potential to transform the education sector.

Personalized Learning The educational experience of pupils is being made more individualized by financial technology businesses using artificial intelligence and machine learning algorithms. Because of this, they can receive individualized content and feedback that is tailored to their learning speed and capabilities. The traditional method of instructing a class follows a sequential process in which the instructor first presents new information, after which the students practice the new ability using worksheets, and finally the instructor administers a test to determine whether the students have understood the material. The lesson will proceed regardless of the percentage of pupils who have mastered the content. Students who are not keeping up with their classmates are left behind, while those who are progressing more quickly become disinterested in the material. One objective of a personalized learning plan is to allow students the opportunity to investigate topics that are of particular interest to them in the manner which is most conducive to their learning. An increasing number of teachers are realizing that each pupil in a classroom possesses a oneof-a-kind combination of skills and interests. Students can express their natural curiosity and produce their best work via the use of tools and tactics that highlight their skills rather than illuminate any disabilities they may have. This is made possible through personalized learning. When each student is allowed to shine in his or her special way, the atmosphere of your classroom will become more positive, and kids will be inspired to keep pushing themselves to perform better on each project that comes after the one they just completed. Knewton is one example of a personalized learning platform that is powered by artificial intelligence and offers students individualized learning content as well as feedback on their progress. It does this by utilizing data and analytics to identify each student’s learning pace and abilities and then adjusting the content following this knowledge. The result is a learning experience that is more efficient and effective.

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Career Advancement Students can improve their employability by taking advantage of the professional development courses and certification programmes that are being made available by organizations in the FinTech industry. Students are therefore able to maintain a competitive advantage and become better prepared to meet the demands of the modern-day workforce because of this. FinTech plays a significant role in the delivery of quality education by delivering cutting-edge technologies that make education more available, individualized, and easy. The education industry is transforming because of the creative solutions offered by FinTech companies. These solutions enhance the overall quality of education and make it more available to individuals. The following are a few examples that illustrate how powerful financial technology may be in the delivery of high-quality education. One example of an online learning network that offers more than 4,000 courses from prestigious educational institutions and universities is called Coursera. Students do not need to be physically present in a classroom to obtain a high-quality education using Coursera, which allows students to study from any location in the world.

Chapter Summary The emergence of financial technology during the Fourth Industrial Revolution has transformed the educational landscape. This has created new possibilities for providing top-notch education to students everywhere in the world, regardless of where they are. Teaching and learning are now more accessible, efficient, and successful because of the incorporation of financial technology. But there are also important issues that need to be resolved, especially in rural areas where there are few people with reading and technology abilities. Notwithstanding these obstacles, financial technology in education has huge potential benefits. To overcome these obstacles and make sure that students have the skills and knowledge they need to succeed in the digital era, educational institutions and policymakers must collaborate. Financial technology can be a driving factor for promoting high-quality education and making sure that students are ready for the challenges of the twenty-first century with sustained efforts and collaboration.

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CHAPTER 9

Artificial Intelligence and Machine Learning in Making Transport, Safer, Cleaner, More Reliable, and Efficient in Emerging Markets

Introduction How we engage with the world around us is already being profoundly altered by the introduction of artificial intelligence, also known as AI. AI, a potent collection of technologies that can assist humans in resolving problems that arise in their daily lives, has major applications in several sectors (Abduljabbar et al., 2019; Lopez Conde & Twinn, 2019; Mhlanga, 2021a, 2021b). One of these fields is transportation, where applications of artificial intelligence are already disrupting in the manner that we move both people and things. AI is generating chances to make transportation safer, more dependable, more efficient, and cleaner. One example of this is the optimization of sailing routes to decrease emissions. Another example is the scanning of traffic patterns to reduce road accidents. Although the challenges posed by the technology need to be managed effectively, there are multiple applications of AI in both advanced economies and emerging markets that demonstrate the contributions these developing technologies can make to economies. These applications illustrate how these technologies can make economies as they evolve. According to Abduljabbar et al. (2019), the rapid rate of breakthroughs in Artificial Intelligence (AI) is creating chances that have never been seen before to improve the overall performance of a variety

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1_9

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of industries and businesses, including the transportation industry. The advancements made possible by AI include the introduction of very complex computational algorithms that are modelled after the way the human brain performs its functions. The application of artificial intelligence in the sphere of transportation is aimed at overcoming the issues posed by an increase in the demand for travel, CO2 emissions, concerns regarding safety, and degradation of the environment (Abduljabbar et al., 2019). It is now more conceivable to handle these concerns in a manner that is both more efficient and effective due to the availability of a vast amount of quantitative and qualitative data as well as AI in this digital age. When the system and its users’ behaviour are too complicated to model and predict travel patterns, this presents a difficulty for those who work in the transportation industry. Thus, artificial intelligence is a good fit for transportation systems to tackle the issues posed by an increasing travel demand, concerns regarding safety, emissions of greenhouse gases, and degradation of the environment (Abduljabbar et al., 2019). These issues are brought about by the consistent expansion of traffic in rural and urban areas because of the increasing number of residents, particularly in developing countries. According to Lopez Conde and Twinn (2019), AI has already started to make a significant impact on the global economy, and it is expected that this trend will continue in the foreseeable future. The development of artificial intelligence might add almost $13 trillion to the production of the global economy by the year 2030. This figure includes the transportation industry, where AI is anticipated to generate significant upheaval. The numerous advantages that AI can provide to the transportation industry, including enhanced efficiency, decreased costs, and improved safety for pedestrians and drivers, are driving the rapid expansion of this sector. According to Gangwani and Gangwani (2021), the tremendous population growth around the world has led to an increase in the use of vehicles and other forms of transportation, which in turn has led to an increase in traffic congestion and accidents on the roads. As a result, there is a demand for intelligent transportation systems in the country that can provide safe and reliable transportation while also maintaining environmental conditions such as pollution levels, CO2 emissions, and energy consumption. Olugbade et al. (2022) investigated the most pressing issues and possible solutions for lowering the number of accidents that occur on the roads, as well as the role that artificial intelligence and machine learning

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play in the operation of road transportation systems. According to Olugbade et al. (2022), the most accident-prone mode of transportation is road transport, which results in a high number of casualties and injuries. In addition to this, it contends with a myriad of interminable challenges, one of which is the recurrent destruction of both life and property in the event of an accident. To address these issues, the appropriate steps need to be done, such as the establishment of an automatic event detection system that makes use of artificial intelligence and machine learning (Olugbade et al., 2022). According to the findings of Olugbade et al. (2022), ensuring the safety of road transportation systems requires optimizing routes, forecasting cargo volumes, performing predictive maintenance on fleets, keeping track of vehicles in real time, and managing traffic. Considering the context, the purpose of the present investigation was to evaluate the role that AI and machine learning can play in making transportation in emerging markets safer, cleaner, more reliable, and more efficient.

AI Models in the Transport Sector There are so many AI models that can be useful in the transport sector. Some of the models or algorithms are outlined in Figure 9.1.

Fig. 9.1 Artificial neural networks

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The neural networks that are present in the human brain have served as a model for the creation of artificial neural networks. They make judgments based on prior experience as well as data points with changing weights, and they can manage complex issues because they operate with enormous volumes of data while recognizing nonlinear relationships (Abduljabbar et al., 2019). Gathering data from a GPS, an accelerometre, and a magnetometre, more advanced Global Positioning Systems (GPS) use artificial neural networks to detect the mode of transportation that is being used. This is accomplished by using the data from the three sensors. This is comparable to how people “sense” distance by taking into account several different data points. In addition, models based on artificial neural networks can be utilized in the field of public transportation to assist with the prediction of the arrival times of buses at stop places (Abduljabbar et al., 2019; Gurmu & Fan, 2014).

Artificial Immune System This algorithm draws its motivation from human biology, more specifically from how human bodies react to pathogens known as antigens. The Artificial Immune System simulates the behaviour of human immune systems regarding the extraction of features, recognition of patterns, learning, and storage of memories. Pattern identification, anomaly detection, clustering, optimization, planning, and scheduling are all areas that can benefit from the application of artificial immune systems. Engineers have developed a real-time regulatory support system with the assistance of AIS to assist public transport networks if the network experiences a disturbance (Abduljabbar et al., 2019; Masmoudi et al., 2012).

Fuzzy Logic Model A model for evaluating the risk that is based on fuzzy set theory and fuzzy logic, and that considers the opinions of experts, facts from limited experience, and inference rules. Fuzzy logic, which is based on the way humans make decisions, works by assigning data with numeric values somewhere between 0 and 1 to represent uncertainty. It is used to manage the concept of partial truth, in which the truth value might fluctuate between being true and being false (Chattaraj & Panda, 2010; Ohayon, 1999). Due to the ambiguity and vagueness of the problems, fuzzy logic

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has the potential to be used in modelling traffic and transportation planning issues. It also has implications for traffic control, since it can signal time at a four-way intersection and predict how long vehicles should wait at a stop sign (Abduljabbar et al., 2019; Chattaraj & Panda, 2010).

Ant Colony Optimizer The Ant Colony Optimizer is a piece of software that uses algorithms to simulate the behaviour of ant colonies. This includes how ants select their paths based on their desire to take the shortest route possible as well as pheromones that relay the experience that other ants have had with each path. Ants use this method to determine the shortest path between two places to maximize their efficiency. This problem is also known as the Traveling Salesman Problem in the field of computer science. In this scenario, a salesperson is tasked with travelling to X different towns before making his way back to the initial location through the route that incurs the lowest possible expense (Lopez Conde & Twinn, 2019). The Ant Colony Optimizer can be used to improve the routes that public transportation buses take, as well as the routes that ride-sharing companies take that pick up multiple customers at once, such as Via or Uber Pool.

Bee Colony Optimization This algorithm, much to Ant Colony Optimizer, uses the collective foraging activities of honeybees as an example of coordinated teamwork, coordination, and tight communication. Honeybees migrate from flower to flower in search of food. The movements of the bees inside the hive provide scientists with valuable information that helps them improve the movements of robots (Kaur & Goyal, 2011). The Bee Colony Optimization method can be utilized to improve travel routes, hence reducing travel times as well as the number of waits, delays, and pollution caused by air and noise.

A Window of Opportunity for the Use of Artificial Intelligence in Transportation There are so many opportunities that arise from the use of artificial intelligence and Machine learning in the transport sector. Figure 9.2 outlines the opportunities.

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Opportunities

Opportunities Road Safety

Efficiency

Reliability and predictability

Reduction in Emmissions

Fig. 9.2 Opportunities for the use of artificial intelligence in Transportation

Figure 9.2 outlines some of the opportunities for the application of artificial intelligence in the transport sector. AI can help solve a variety of problems in transport related to safety, reliability, and predictability, as well as efficiency and sustainability when applied appropriately in emerging markets. A major concern for public health is ensuring the safety of motorists as well as pedestrians on the roads. According to Lopez Conde and Twinn (2019), the number of deaths that were caused by road traffic accidents reached 1.35 million in 2016, which is an increase from the 1.25 million deaths that were caused by road traffic accidents in 2013. However, the majority of these deaths occurred in low-income countries. Human error is a significant contributor to the high death toll. For example, in the European Union, a human error such as speeding, distracted driving, and drunk driving is a factor in more than 90 per cent of accidents that occur on roads. This is even though inadequate infrastructure, in particular, poor roads and vehicles without modern safety equipment, plays a role in the high death toll (Lopez Conde & Twinn, 2019; Mhlanga, 2020, 2021a, 2021b). It is estimated that autonomous vehicles could reduce the number of people killed in traffic accidents by up to 90 per cent by the year 2050 in some developed countries.

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Tesla’s first attempt at an autonomous vehicle reduced accident rates by 40 per cent when self-driving technologies were activated, demonstrating the potential of this technology (Lopez Conde & Twinn, 2019; Mhlanga, 2020, 2021a, 2021b). Yet, although developing countries are not yet prepared for the widespread deployment of driverless vehicles, some people anticipate that one out of every four cars will be driverless by the year 2030. (Lopez Conde & Twinn, 2019). The second issue is that the increased dependability and predictability of the system brought about by the implementation of AI and machine learning in the transportation sector is a positive development. To facilitate the movement of both people and commodities, transportation must maintain a consistent level of performance and maintain the capacity to accurately predict arrival and departure times. When it comes to public transportation, for example, the provision of information that is both timely and accurate regarding transit journey times can enhance ridership and the pleasure of transit users (Lopez Conde & Twinn, 2019). Urban mobility solution providers in South Africa, such as Uber, Lyft, and Bold, use AI in multiple ways to provide reliable pickup and drop-off times for their routes. Such innovations can be harnessed to improve the quality of public transport solutions globally, particularly in emerging economies. One more time, the implementation of artificial intelligence in the transportation industry may lead to some degree of increased productivity. Developing nations typically have low Logistics Performance Index (LPI) rankings because their logistics expenses as a percentage of their GDP are typically greater. This is in part due to a lack of efficiency brought on by inadequate infrastructure and bad customs procedures (Mhlanga, 2021a, 2021b). Despite this, logistics expenses in developing countries typically account for between 6 and 8 per cent of GDP. However, in some developing countries, these costs might vary from 15 to 25 per cent. AI can help optimize movements to maximize efficiency. The field of elogistics, in which Internet-related technologies are applied to the supply and demand chain, also integrates AI in several ways, such as matching shippers with delivery service providers. AI can help optimize movements to maximize efficiency. The other major concern is the state of the environment. Globally, the transportation industry is responsible for 23 per cent of all energy-related CO2 emissions, and if effective mitigation strategies are not put into place, these emissions may more than quadruple by the year 2050. The use of artificial intelligence technology optimizes

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routes to decrease the number of unproductive journeys taken both on land and at sea and so improves fuel efficiency and reduces emissions of greenhouse gases (GHG). One application of artificial intelligence that is favourable to the environment is truck platooning, which is a method that wirelessly connects numerous trucks to a lead truck and enables them to drive substantially closer to each other while being safe. This results in increased fuel efficiency (Lopez Conde & Twinn, 2019).

The Application of Artificial Intelligence and Machine Learning in Making Transport, Safer, Cleaner, More Reliable, and Efficient in Emerging Markets Both artificial intelligence (AI) and machine learning (ML) are distinct types of data analysis that have made substantial contributions in recent years to a variety of businesses all over the world, especially in developing economies. The term “emerging markets” refers to countries that are undergoing considerable economic and political development and are distinguished by the rapid growth of both their economies and populations. These nations struggle with a variety of issues, including a deficiency in core infrastructure, inadequate budget, and ineffective resource distribution, to name a few. The application of artificial intelligence and machine learning has the potential to make the transportation sector in developing countries safer, cleaner, more reliable, and more productive. This is because of the potential benefits of these technologies.

Utilizing AI for Planning, Designing, and Controlling the Structures of Transportation Networks The application of artificial intelligence (AI) in the planning, design, and control of transportation network architecture in emerging economies has the potential to usher in a new era of innovation (Abduljabbar et al., 2019). Both AI and ML have the potential to play a significant part around traffic management and prediction. AI can be utilized to forecast traffic patterns, improve traffic flow, and lessen congestion. Algorithms powered by artificial intelligence can assess past traffic data, current weather conditions, and real-time traffic information to deliver real-time

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traffic updates and suggest alternative routes (Chen & Zhang, 2022). This has the potential to be a game-changer in many cities in developing nations where traffic congestion is a major issue. Intelligent Transportation Systems (ITS) that are powered by AI can be used to monitor and control traffic, hence increasing overall road safety and decreasing the likelihood of people getting into accidents. The flow of traffic can also be optimized, congestion can be reduced, and travel times can get better with the help of these technologies. Once again, artificial intelligence may be utilized to design and control autonomous cars, which will make transportation significantly safer, more efficient, and more reliable. AI algorithms can be used to process data from sensors, cameras, and other sources to manage the movement of vehicles, thereby lowering traffic congestion and minimizing the likelihood of collisions. The effectiveness of AI in areas like upkeep and repair is an additional vital component. Monitoring the infrastructure of transportation and determining when it will require upkeep and repairs can both be accomplished with the help of AI. Technology has the potential to assist reduce the cost of maintenance, limit disruptions to transportation networks, and increase safety. The planning of public transportation can benefit greatly from the use of artificial intelligence. AI has the potential to improve the effectiveness of public transportation systems by streamlining bus and rail timetables, cutting down on wait times, and making it simpler for passengers to navigate different areas of the city. Applying AI to this mix has the potential to work miracles in terms of optimizing costs. It is possible to apply AI algorithms to optimize the cost of transportation infrastructure, thereby lowering the cost of creating and maintaining transportation networks while simultaneously enhancing the overall efficiency of transportation systems. To summarize, artificial intelligence can significantly improve the planning, design, and control of transportation network structures in developing markets. This is especially true of emerging countries. AI has the potential to contribute to making transportation safer, more efficient, and more reliable. It can do this by improving the flow of traffic, giving realtime traffic updates, and lowering the risk of accidents. There are a lot of studies that were contacted in this area proving the role of artificial intelligence and machine learning in Planning, Designing, and Controlling the Structures of Transportation Networks. Some examples of these studies include Abduljabbar et al. (2019), Chen and Zhang (2022), Gangwani and Gangwani (2021), and a lot of other similar studies.

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The Detection of Incidents The early detection of incidents within transportation networks is an essential component of effective traffic management and safe driving on public roads. Artificial intelligence (AI) has the potential to play a big part in this domain by delivering information and analysis in real time, hence facilitating faster incident identification and resolution timelines. To assist traffic management in reducing congestion, numerous attempts have been made to determine the time and location of an occurrence, as well as the severity of the problem (Abduljabbar et al., 2019). These approaches comprise a wide variety of methodologies, including but not limited to manual reporting, automated algorithms, and neural networks. The detection of events and the cost-effectiveness of manual reports that are generated by people can be delayed, respectively. On the other hand, AI algorithms can measure the features of the flow both before and after the occurrence by using data collected from sensors along the route. Statistical methods, such as the California Algorithm, were initially utilized in the process of putting in place algorithms for event detection (Abduljabbar et al., 2019). On the other hand, it can be challenging to implement an algorithm on arterial roads due to the presence of street parking and traffic signals. As a result of this motivation, algorithms for neural network techniques have been developed. Real-time monitoring is one application of artificial intelligence that has potential around incident detection in transportation networks. Real-time monitoring of the transportation network can be accomplished using AI algorithms that can process data from a variety of sources including cameras, sensors, and GPS. The appropriate authorities can receive up-to-the-minute information about incidents that can be detected by AI, such as traffic jams, road closures, and accidents. AI can make use of previous traffic data, weather trends, and other data sources to predict incidents and take preventative measures to lessen the impact of those incidents. For instance, AI can anticipate traffic congestion in a certain region and recommend alternate routes to take to shorten the amount of time spent waiting and increase overall safety. Again, artificial intelligence systems can use visual and video data to identify items in real time, such as vehicles and traffic signs. This information can be put to use to improve road safety in a variety of ways, for as by monitoring for stopped vehicles or accidents in traffic and reporting them to the appropriate authorities. AI is

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also capable of analyzing patterns of traffic flow and identifying variations in the flow that may suggest an event. These changes may include sudden slowdowns or deviations from regular patterns. This information can be put to use to identify events promptly and to notify the appropriate authorities. It is also possible to integrate information from multiple modes of transportation using AI, such as roads, public transportation systems, and bike lanes so that a comprehensive picture of the transportation network can be obtained. This type of integration is referred to as multimodal integration. This can assist in the detection of incidents that may influence several modes of transportation, hence offering a more accurate and comprehensive view of the transportation network. As was mentioned in the paragraphs that came before, the rapid growth of intelligent transport systems (ITS) has increased the necessity to provide more advanced approaches to predict traffic information. The success of ITS subsystems including advanced traveller information systems, advanced traffic management systems, advanced public transportation systems, and advanced commercial vehicle operations can be directly attributed to the methodologies described here. According to Abduljabbar et al., 2019, intelligent predictive systems are produced by utilizing historical data that is gathered from sensors that are attached to the roads. Following that, these data are fed into machine learning and artificial intelligence algorithms to make real-time, short-term, and long-term forecasts. In the past, research efforts concentrated on making short-term flow predictions using a straightforward feedforward neural network. In conclusion, artificial intelligence can significantly improve incident detection in transportation networks by offering real-time monitoring, predictive analysis, and multimodal integration. This might result in significant cost savings. AI has the potential to help improve road safety, lessen the effect of mishaps, and improve traffic management all at the same time by recognizing incidents in a timely and accurate manner.

System for the Management of Hazards and Emergencies The management of safety and emergencies within transportation networks is an essential component in guaranteeing the health and safety of passengers as well as the efficient operation of the transportation system. The use of artificial intelligence (AI) has the potential to significantly contribute to the enhancement of transportation network safety

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as well as emergency response management. People who are travelling in vehicles as well as pedestrians on the street are always at risk due to the high number of accidents that occur on the roads. Even with the most recent technological advancements in vehicles, the issue of pedestrian safety is a primary focus in the research and development of Intelligent Transportation Systems. In the context of smart transportation, artificial intelligence plays a significant role in the management of safety and emergencies (Gangwani & Gangwani, 2021). Using techniques from AI, a reliable and complete database of accident data is being developed. This database will be able to give the required information for analyzing trends and patterns in traffic accidents. Real-time monitoring of the transportation network can be accomplished using AI algorithms that can process data from a variety of sources including cameras, sensors, and GPS. The appropriate authorities can receive up-tothe-minute information about incidents that can be detected by AI, such as traffic jams, road closures, and accidents. After obtaining this information and conducting this analysis, it will then be possible to provide people with secure mobility. AI is also capable of providing rapid and effective advancements in traffic analysis, which can be helpful in times of emergency, provide people with safety, and safeguard their health. AI can also help provide individuals with an emergency management system that can assure safe and expedited transport from one location to another. As was said before, AI can utilize data obtained from sensors, cameras, and other sources to forecast when maintenance will be required and maximize the use of available resources. For instance, AI can forecast when a train will require maintenance, which enables the appropriate authorities to plan maintenance at a time that causes the least amount of disturbance to the transportation network. Algorithms powered by AI can analyse data received in real time from emergency services, which can help reduce response times and optimize the distribution of available resources. For instance, AI may analyse data gleaned from dispatch systems used by fire departments and ambulance services to prioritize where resources should be sent, thereby shortening response times while simultaneously enhancing public safety. A comprehensive perspective of occurrences, including the location, size, and impact of incidents, may be provided by AI by using data from cameras and sensors. This allows the necessary authorities to respond swiftly and efficiently. In conclusion, artificial intelligence has the potential to significantly improve safety and disaster management in

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transport systems by providing real-time tracking, predictive modelling, and optimization of emergency response. This could be accomplished through the implementation of several different technologies. AI can help make transportation networks safer, more dependable, and more efficient by leveraging real-time data and predictive analysis. This will reduce the effect of mishaps and improve the overall safety and well-being of passengers.

AI and Autonomous Vehicles Artificial intelligence (AI) and autonomous cars are two technologies that are intimately tied to one another and are influencing how we think about different modes of transportation. Autonomous vehicles are motor vehicles that can function without the assistance of a human operator. These vehicles rely on artificial intelligence (AI) algorithms and sensors to navigate roadways, make judgments, and avoid obstacles. It is anticipated that these cars will have a significant impact on the transportation industry in the next years, delivering benefits such as enhanced safety, higher efficiency, and a reduction in the amount of traffic congestion. However, there are enormous technical, legislative, and ethical issues that need to be addressed before fully autonomous vehicles can become a widespread reality. These challenges must be overcome before fully autonomous vehicles can become a widespread reality. The development of artificial intelligence has paved the way to produce self-driving automobiles, which are available to consumers who can make use of the newest technology (Gangwani & Gangwani, 2021). When moving from one area to another, autonomous vehicles, also known as self-driving cars, make use of several different cutting-edge technologies such as sensors, cameras, radar, and other forms of artificial intelligence. Because there is always some element of risk associated with these vehicles, people need to have a significant amount of faith in autonomous vehicles before they will be allowed on the road. Consequently, a combination of machine learning, deep learning, and artificial intelligence approaches is required to demonstrate the reliability and safety of such vehicles (Persaud et al., 2017). In addition, as said before, autonomous vehicles, often known as AVs, have the potential to be beneficial to emerging economies in several different ways. AVs have the potential to significantly reduce the number of accidents that are caused by human error, which will result in safer

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roads and fewer fatalities. This will improve the efficiency of transportation systems and reduce costs. AVs also have the potential to reduce the amount of time and fuel consumption associated with human-driven vehicles. Likewise, AVs have the potential to provide mobility solutions for groups of people who have restricted access to transportation, such as the elderly, the disabled, and communities with poor incomes. Communication with other cars and the optimization of their driving patterns might help autonomous vehicles (AVs) enhance traffic flow, which in turn reduces congestion and speeds up travel times. Automated vehicles have the potential to increase the adoption of electric vehicles and contribute to the reduction of carbon emissions, hence contributing to an overall improvement in the sustainability of transportation systems. Additionally, the development and implementation of AVs can be a driver of technological innovation, a boost to economic growth, and an increase in the global competitiveness of emerging economies.

Smart Parking Management The term “Smart Parking Management” refers to a system that makes use of technology to increase the efficiency with which parking spaces are used and lessen the challenges that are involved with the process of locating a parking spot. The Sensors are often involved in this process. When determining whether a parking spot is filled or vacant, intelligent parking systems make use of a variety of sensors, including cameras and pressure sensors. The data from the sensors are sent to a centralized system, which then collects and examines the data to improve the effectiveness of the management of the parking spaces. Mobile applications are also used for intelligent parking management. Mobile users can get information about available parking spaces and reserve places through applications on their mobile devices, which significantly cuts down on the amount of time and effort spent looking for a parking spot. Users of payment Systems can pay for parking through mobile applications or in-person payment systems. This eliminates the requirement for physically dispensed tickets and streamlines the payment procedure. Users of smart parking systems are provided with real-time information, such as the availability of parking spots and the estimated amount of time it will take to reach a parking destination. This contributes to an overall improvement in the parking experience.

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Cities can reap the benefits of Smart Parking Management by alleviating traffic congestion and cutting down on air pollution, enhancing parking enforcement and revenue collection, and offering users a parking experience that is both more convenient and more effective. The development of intelligent parking systems has profited from the application of AI algorithms. The development of a smart parking management system is necessary for the expansion of smart cities. There are a lot of obstacles that need to be overcome to organize a parking system that is systematically organized so that people don’t have to waste their time looking for a parking space at universities, colleges, and public locations (Gangwani & Gangwani, 2021; Melnyk et al., 2019). Because of this, a well-structured system that can identify vacant parking spots and provide the driver with advanced warning is required. AI technology is used by a smart parking management system to detect available parking spots, notify the driver, and provide a status update regarding the availability of a parking place. The most popular form of artificial intelligence, known as the Genetic Algorithm (GA), is predicated on the idea of biological evolution (Gangwani & Gangwani, 2021). This AI technique has recently been used for the resolution of a variety of transportation optimization issues. In urban design concerns that call for the most effective utilization of space possible, GA can be utilized to develop intelligent parking management systems. Most smart cities have sensors installed in their parking lots to keep track of availability, which might be of assistance to vehicles looking for a spot. These days, because of the development of smart cities, multistory parking lots are fitted with digital sensors that can be used to their full potential and managed effectively. Drivers will be able to receive an online message indicating which level of parking is vacant, and they will then be guided to that level. This can help save time and energy, as well as cut down on emissions of carbon dioxide, by reducing the number of drivers who are hunting for parking spaces (Gangwani & Gangwani, 2021). It is now conceivable, thanks to the development of these technologies, to create a transportation system that is both effective and productive, and which will contribute to making travelling more secure, hassle-free, and rapid for the public (Gangwani & Gangwani, 2021).

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Limitations Risks and Challenges of AI and ML in Making Transport Safe and Reliable Artificial intelligence has the potential to increase production and efficiency, but it also has the potential to have substantial socioeconomic repercussions that need to be handled, particularly in emerging nations. One of the major repercussions is a lack of data, which is discussed further down in this article. It may be difficult to train AI algorithms in the transportation industry in many emerging nations since there may not be enough data available to train the algorithms. This makes it difficult to adopt AI-based solutions. The other problems involve limitations on the available funding and the existing infrastructure. Constrained infrastructure in emerging economies, such as a lack of access to highspeed Internet, can make it difficult to implement AI technology in the transportation industry, particularly if the economy is still developing. Likewise, many developing countries do not have the financial resources to invest in the development and deployment of artificial intelligence technologies in the transportation industry. This is a problem because these technologies are becoming increasingly important. The other issue that needs immediate attention is the Regulatory Issues. The deployment of AI technologies in the transportation sector in emerging economies may be hindered by a lack of clear laws and standards, which may be a factor. This may be a barrier to its implementation. Sometimes a significant problem is that there is a shortage of technical expertise. There is a potential scarcity of technical professionals in emerging economies who possess the requisite abilities to develop and deploy artificial intelligence technology in the transportation sector. In other contexts, there is pushback against the process of change. In some instances, the stakeholders in the transportation sector of emerging economies may display resistance to the adoption of new technology, including AI. This may be the case in certain instances. Another problem that plagues developing markets is the lack of developed infrastructure for public transportation. The transportation infrastructures in many developing economies are not as sophisticated as those in developed economies; as a result, it is more difficult to adopt AI-based solutions in these economies. According to a paper published by the Center for Global Policy Solutions, a rapid transition to AVs in the United States will almost certainly result in the loss of more than four million jobs. The roles of delivery and heavy truck drivers, bus drivers, taxi drivers, and chauffeurs would all

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fall under this category of jobs. It is expected that AI will speed up the move towards a service economy, so upending conventional models for economic development. This will hasten the process of job loss for people with low levels of education in numerous industries, including transportation. The potentially high cost of some AI systems, including hardware and software, is a major barrier that is preventing the growth and development of AI in the transportation business. This barrier has been one of the most significant factors. In addition, there may be limitations placed on the use of foreign currency as well as stringent regulations concerning the importation of computer-related goods. Infrastructure that is both poor and underdeveloped because their infrastructure is not prepared for implementation and is unable to provide maintenance and repairs, countries with low incomes and shaky political systems confront a significant obstacle when attempting to use AI-based transportation applications. One contributory factor to this challenge is the absence of dependable power sources and the preponderance of inadequate communication networks. It may be more difficult for nations to harness the power of artificial intelligence if they invest a relatively small fraction of their GDP in technological research and physical infrastructure. Over the past several years, there has been a rise in the demand for AI specialists in established countries as well as emerging markets (EMs), who have been expanding their investment in AI. It is commonly acknowledged that the most significant obstacle to the widespread use of AI in industrialized countries is a dearth of qualified AI expertise. Except for China, emerging markets are experiencing an even more severe shortage. It takes some time for a nation to successfully implement new technology, particularly complicated ones that have effects across the economy such as artificial intelligence. This indicates that it takes some time to establish a capital stock that is sufficiently substantial to have an aggregate effect, as well as the supplementary investments that are required to take full benefit of AI investments, such as access to competent individuals and business processes. It is impossible to anticipate the regulatory constraints that will be imposed on AI, particularly regarding the assignment of responsibility in situations in which robots, like humans, make mistakes. Research indicates that autonomous vehicles (AVs) could minimize the number of people killed in traffic accidents; nevertheless, it is not apparent who would eventually be held accountable if an AV caused an accident, hurt, or death. To reiterate, having strong privacy legislation is necessary to

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ask customers to opt-in and supply additional personal data for machine learning. These laws need to be weighed against the potential advantages of incorporating additional data into a communications network.

Summary This chapter is an assessment of how artificial intelligence (AI) and machine learning are helping to make transportation safer, cleaner, and more dependable so that cities may be more diverse and safer. Artificial intelligence, sometimes known as artificial intelligence, has already had a significant impact on how we interact with the world around us. Since AI is a potent collection of technologies that may help humans in resolving difficulties that they confront daily, it has significant applications in a range of industries. The number of vehicles on the road, the amount of cargo being transported, and the amount of pollution produced all rise because of inadequate infrastructure, growing populations, increased urbanization, and, in some locations, rising income. In the transportation sector, emerging markets usually encounter significant difficulties. Artificial intelligence can be used to come up with fresh solutions to these problems. By facilitating market access and enabling nations to provide better services to their underserved people, these initiatives open new markets and opportunities for private-sector investment.

References Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189. Chattaraj, U., & Panda, M. (2010). Some applications of fuzzy logic in transportation engineering. http://hdl.handle.net/2080/1179 Chen, G., & Zhang, J. (2022). Applying Artificial Intelligence and deep belief network to predict traffic congestion evacuation performance in smart cities. Applied Soft Computing, 121, 108692. Gangwani, D., & Gangwani, P. (2021). Applications of machine learning and artificial intelligence in intelligent transportation system: A review. Applications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML, 2020, 203–216. Gurmu, Z. K., & Fan, W. D. (2014). Artificial neural network travel time prediction model for buses using only GPS data. Journal of Public Transportation, 17 (2), 45–65.

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Kaur, A., & Goyal, S. (2011). A survey on the applications of bee colony optimization techniques. International Journal on Computer Science and Engineering, 3(8), 3037. Lopez Conde, M., & Twinn, I. (2019). How Artificial Intelligence is making transport safer, cleaner, more reliable and efficient in emerging markets. EMCompass;Note 75. International Finance Corporation, Washington, DC. © International Finance Corporation. https://openknowledge.worldbank. org/handle/10986/33387 Masmoudi, A., Elkosantini, S., Darmoul, S., & Chabchoub, H. (2012, June). An artificial immune system for public transport regulation. In: 9th international conference on modeling, optimization & simulation. Melnyk, P., Djahel, S., & Nait-Abdesselam, F. (2019, October). Towards a smart parking management system for smart cities. In: 2019 IEEE international smart cities conference (ISC2) (pp. 542–546). IEEE. Mhlanga, D. (2020). Artificial Intelligence (AI) and poverty reduction in the Fourth Industrial Revolution (4IR). Preprints, 2020090362,. https://doi.org/ 10.20944/preprints202009.0362.v1 Mhlanga, D. (2021a). Artificial intelligence in industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability, 13(11), 5788. Mhlanga, D. (2021b). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability, 13(11), 5788. Ohayon, M. M. (1999). Improving decision-making processes with the fuzzy logic approach in the epidemiology of sleep disorders. Journal of Psychosomatic Research, 47 (4), 297–311. Olugbade, S., Ojo, S., Imoize, A. L., Isabona, J., & Alaba, M. O. (2022). A review of artificial intelligence and machine learning for incident detectors in road transport systems. Mathematical and Computational Applications, 27 (5), 77. Persaud, P., Varde, A. S., & Robila, S. (2017, November). Enhancing autonomous vehicles with commonsense: Smart mobility in smart cities. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1008–1012). IEEE.

CHAPTER 10

FinTech and Climate-Related Challenges in the Fourth Industrial Revolution

Introduction Climate change and environmental issues are a concern for many central banks, financial regulators, and finance departments in growing and developing nations. One of the reasons for this concern is the fact that central banks and financial regulators in emerging and developing countries have a policy role that encompasses roughly 1.7 billion people, or 85% of the world’s unbanked population (Chenet, 2021; International Monetary Fund, 2019). Due to the negative effects of climate change, environmental disasters affect about 200 million people, causing billions of dollars in annual expenses to the global economy. Another serious problem is the fact that climate change is forcing thousands of people to relocate every day, ruining developing and emerging economies. One of the main obstacles to financial stability and poverty reduction is climate change, which is once more making many people financially excluded (Hansen, 2022; IMF, 2019; Puschmann et al., 2020). Numerous academics, including Shonkoff et al. (2011), Levy and Patz (2015), Gasper et al. (2011), Hope (2009), Leichenko and Silva (2014), and many more, hold that the poor and vulnerable, such as those residing in low-lying coastal areas, are disproportionately affected by climate change. These individuals are significantly impacted in the near term by severe climatic disasters such as floods, droughts, and storm surges. Several factors, including climate

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change, are expected to be contributing to an increase in poverty globally. By 2030, it is predicted that 132 million people would be living in poverty because of climate change (Jafino et al., 2020). The World Bank (2020) claims that declining agricultural production is the main cause of rising food prices in the poorest regions, such as SubSaharan Africa. Because they spend so much of their income on food, the poor often struggle to meet their necessities and fall into poverty because of an increase in food prices. For instance, 63% of household income in Malawi is spent on food and drink. So, even a small change in food costs has a big effect on Malawians. Making ensuring agricultural production can adjust to climate change is therefore essential (World Bank, 2020). According to the World Bank (2020), food prices, health shocks, due to the high prevalence of diarrheal diseases in the region, and natural disasters, due to the region’s high exposure to cyclones, floods, and other extreme weather events, are the three main causes of poverty in South Asia, specifically in Bangladesh and India. More effective natural-risk management has been a primary priority for the worldwide community in recent decades, according to the World Bank (2020), with significant advancements “in our ability to save lives thanks to weather projections and early warning systems.” Conversely, Jafino et al. (2020) suggested that “if the course towards the Sustainable Development Goals is maintained, the impact of climate change on poverty can be halved.” Continuing the trajectory of the Sustainable Development Goals will “result in a more inclusive economy with equal access to financial services, drinking water and sanitation, efficient energies, healthcare, and social welfare, as well as a more resilient and robust economy,” claim Jafino et al. in 2020. Despite having negative effects on climate change, Liu et al. (2021) assert that financial inclusion can aid in resolving issues related to the environment. Many believe that climate change will make it easier for people and enterprises to obtain more advantageous and affordable financing, increasing the availability of green technology investments. In this sense, inclusive financial systems benefit the environment by making it more accessible and affordable, as well as by promoting the adoption of ecofriendly practices to lessen the effects of climate change (Ullah et al., 2022). According to Liu et al. (2021), financial inclusion is essential for addressing climate change issues because many small businesses and smallholder farmers might not have the financial resources to participate in renewable energy. Financial limitations, a lack of support from

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the government, and finance options from banks have all been acknowledged as significant barriers to the adoption of solar household systems, according to Baulch et al. (2015) in Ho Chi Minh City, Vietnam. Some academics concur that access to capital may have detrimental effects on climate change. Jensen (1996), for instance, suggested that improved financial accessibility, on the other hand, helps and stimulates manufacturing and industrial activity, hence increasing CO2 emissions and exacerbating global warming. Consumers are more inclined to purchase energy-intensive consumer goods like automobiles, refrigerators, and air conditioners, which pose a serious environmental risk since they produce more greenhouse emissions, according to Frankel and Romer (1999). Inclusionary financial institutions stimulate economic growth, which increases demand for hazardous energy sources and, as a result, increases greenhouse gas emissions. Al Nawayseh (2020) asserted that one of the most crucial and valuable enablers in the development of socioeconomic competence is the financial sector’s role in facilitating financial transactions. Technology improvements, particularly in the field of information and communication technology (ICT), have altered the financial services sector in modern nations in recent decades, enabling businesses to provide their customers with more efficient and effective services. Several digital financial platforms have gained popularity in recent years, and they have shown to be helpful to low-income or disadvantaged members of society in general, particularly in developing countries. The most crucial aspect of various financial technology (FinTech) services above traditional forms of financing is that they provide both the financial sector and customers with the most practical and secure means of carrying out transactions. Alwi (2021) claims that the rising use of personal mobile devices like smartphones has made it possible for a wide range of FinTech services to reach even the most remote regions of developing nations. With the use of these services, consumers could now carry out routine financial transactions on their mobile devices, giving many consumer products more flexibility. The world is heading towards a cashless society because of numerous financial industry advances, the introduction of goods like credit and debit cards, as well as FinTech services, and the increased reliance of consumers on non-cash payment methods. Due to the availability of various technologies, virtual payments have grown in importance (Alwi, 2021). Electronic payment systems are gradually displacing conventional cash-based payment methods. According to the 2019 Global Payment Survey, non-cash transactions increased by 12%

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between 2016 and 2017, totalling $539 billion, the greatest amount in the previous two decades (Alwi, 2021). In 2016–2017, non-cash transactions grew by more than 32% in emerging economies like Asia. Because of advancements in the financial sector and the development of products like credit and debit cards as well as FinTech services, consumers are depending less and less on cash as a form of payment. The use of virtual payments has become necessary due to the accessibility of various technologies (Alwi, 2021). Electronic payment alternatives are gradually displacing conventional cash-based payment systems. The number of non-cash transactions increased by 12% between 2016 and 2017, reaching $539 billion, the greatest amount in the previous two decades, according to the 2019 Global Payment Study (Alwi, 2021). In 2016– 2017, non-cash transactions grew by about 32% in emerging economies like Asia. While Asia-Pacific (APAC) growth maintained around 7% during the same period, Central and Eastern Europe, the Middle East, and Africa (CEMEA) had growth of almost 19%. Financial institutions and technology firms are increasing their investment in FinTech technologies, according to Al Nawayseh (2020). In 2019, more than $40 billion is anticipated to be invested globally in FinTech. Al Nawayseh (2020) observed that despite growing FinTech investment, the maturation and adoption of these technologies among low-income earners continue to be a concern that necessitates quick action. Particularly in emerging nations, the skill of balancing the advantages and hazards of FinTech breakthroughs is crucial. The issue is made worse by the fact that, despite their best efforts, people in poor countries with little socioeconomic means do not have access to the crucial knowledge of financial products. The effect of financial technology (FinTech) on climate change is examined in this study. Although numerous studies have demonstrated the effects of financial development on CO2 emissions, including Zhang et al. (2011), Shahbaz et al. (2013), and Charfeddine and Kahia (2019), there are surprisingly few that assess Fintech’s contribution to climate change. While Pinshi (2021) focused on the potential that FinTech offered to people during the pandemic’s duration, Alwi (2021) emphasized discovering the elements that influence people’s decision to utilize mobile e-wallets after the outbreak. The most current developments in fintech, as well as the difficulties and potential advantages they may pose for financial inclusion and financial literacy, were evaluated by Morgan (2022). With a focus on mobile applications, Fu and Mishra investigated how the

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COVID-19 epidemic affected the uptake of digital finance and fintech (2021). Pinshi (2021) found that Fintech was one of the instruments that helped in realigning the global financial system during the Coviddisturbances and that it assisted the finance system to become resilient and react appropriately to the crisis by ensuring that the financial sector operated while the virus’s containment measures were followed. According to the empirical literature, a lot of the research was biased towards assessing financial inclusion and the causes of financial inclusion rather than looking at the impact of FinTech on climate change. To solve climate-related difficulties in the context of the Fourth Industrial Revolution, the current study explores the role of fintech for financial inclusion and concludes sustainable development goals.

The Fourth Industrial Revolution According to one definition, the Fourth Industrial Revolution is the “blurring of boundaries between the physical, digital, and biological worlds” (McGinnis, 2022; Mhlanga, 2020). A “combination of breakthroughs in artificial intelligence (AI), robots, the Internet of Things (IoT), 3D printing, genetic engineering, quantum computing, and other technologies” is how the Fourth Industrial Revolution is also referred to (McGinnis, 2022). The first, second, and Third Industrial Revolutions serve as the foundation for the Fourth Industrial Revolution, also referred to as Industry 4.0 or 4IR (Mhlanga, 2020, 2021, 2022). The First Industrial Revolution was sparked by the discovery of the steam engine in the eighteenth century, which allowed the industry to be mechanized for the first time and led to considerable social change and urbanization. Around the fourth millennium, when electricity and other technological developments made mass production feasible, the Second Industrial Revolution took place. The Third Industrial Revolution came to an end with the development of computer automation and digital technology. Manufacturing has therefore become more automated, upending sectors like banking, energy, and communications (McGinnis, 2022). The Fourth Industrial Revolution, according to Klaus Schwab, “founder and executive chairman of the World Economic Forum and author of the book The Fourth Industrial Revolution,” is a brand-new revolution. According to Schwab, there has never been a time of greater promise or potential hazard in human history since the changes are so fundamental (McGinnis,

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Fig. 10.1 Technologies driving change in the Fourth Industrial Revolution (Source Author’s Analysis)

2022). The following is a list of the technologies driving the Fourth Industrial Revolution. The technologies shown in Fig. 10.1 are examples of some of the ones that are propelling the progress of the Fourth Industrial Revolution. Examples of these technologies include artificial intelligence, virtual reality, augmented reality, biotechnology, 3D printing, blockchain, robots, and the Internet of Things.

Financial Inclusion Financial inclusion, according to the World Bank’s definition from 2022, is “the process where people and companies have access to usable and reasonably priced financial products and services that fit their needs, such as transactions, payments, savings, credit, and insurance.” They should be provided responsibly and sustainably, according to the World Bank

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(2022). The World Bank lists financial inclusion as a facilitator of 8 of the 17 Sustainable Development Goals (SDGs). These objectives are listed in the United Nations Capital Development Fund (2022) as “SDG 1: Eradicating Poverty; SDG 2: Ending Hunger, Achieving Food Security, and Promoting Sustainable Agriculture; SDG 3: Promoting Health and Well-Being; SDG 5: Achieving Gender Equality and Economic Empowerment of Women; SDG 8: Promoting Economic Growth and Jobs; SDG 9: Supporting Industry, Innovation, and Infrastructure; SDG 10: Reducing Inequality.” According to the World Bank (2022), financial inclusion is a crucial enabler for eliminating extreme poverty and boosting shared prosperity. Global financial inclusion has advanced significantly, according to the World Bank (2022), with 1.2 billion adults having access to an account between 2011 and 2017. Almost 69% of people in 2017 had a bank account, according to estimates. Additionally, it is said that more than 80 countries have implemented digital financial services, such as the use of mobile phones, and that “millions of previously underserved poor consumers are making the transition from cash-based transactions to formal banking services via cellular telephones or other digital technology” (Mhlanga, 2020; World Bank, 2022). According to the latest recent Findex data, nearly 1.7 billion people, or about one-third of all people, were still without banks in 2017 (World Bank, 2022). Women from low-income households in rural areas or who were unemployed made up about half of those without bank accounts (Mhlanga, 2020; World Bank, 2022).

Financial Technology (FinTech) Sometimes abbreviated to “FinTech,” as shown in previous chapters this term describes “all the technologies that are being deployed to enhance, digitize, or disrupt conventional financial services” (Walden, 2020). Anything which is used in the financial sector, including “programmes, algorithms, and apps for digital and mobile devices” (Philippon, 2016; Puschmann, 2017). The financial technology sector is not a new one, but its growth is unprecedented. Technology, such as credit cards and ATMs, has always been a part of the financial sector. Puschmann (2017) defines Fintech as the “merging of Financial and Technological processes.” “Initially introduced in the early 1990s by Citicorp’s chairman John Reed in the framework of a newly formed Smart Card Forum consortium, Speaking a language of cooperation across enterprises and

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industries,” writes Puschmann (2017). Puschmann (2017) defines fintech as an “umbrella word that comprises new financial solutions enabled by technology and start-up companies that sell those solutions, while it also includes traditional financial services providers such as banks and insurers.” According to Goldstein et al., “FinTech” is “the intersection of financial services and information technology” (2019). As previously said, Goldstein et al. (2019) noted that technological innovations have always affected the functioning of the financial industry. Again, the FinTech revolution is exceptional because it is bringing about change from outside the financial industry, with both small start-ups and large established technology companies attempting to disrupt incumbents through the introduction of new products and technologies and the introduction of a substantial new dose of competition (Goldstein et al., 2019).

What Is Climate Change? The United Nations has established a definition for “climate change,” which is the phrase used to describe long-term changes in temperature and weather patterns. Since the 1800s, burning fossil fuels like coal, oil, and gas has been the main cause of climate change, which is primarily attributable to human activity. These changes may have been brought about by natural processes, such as oscillations in the solar cycle, but since that time, human activity has been the main cause of climate change. By trapping the heat of the sun and raising temperatures, greenhouse gas emissions produced by burning fossil fuels act as a blanket over the planet. Two examples of greenhouse gas emissions that are influencing the state of the climate today are carbon dioxide and methane. They are created, for instance, when a car is being driven or a building is being heated with coal. When forests and other land are destroyed, carbon dioxide may also be released into the sky. A substantial source of methane released into the atmosphere is garbage landfills. Some of the major sources of greenhouse gas emissions include energy, industry, transportation, construction, agriculture, and land use. The effects of climate change may also have an impact on our ability to farm food, build homes and businesses, as well as our physical health. Some of us, such as those who reside in nations with smaller island populations or other developing nations, are already more vulnerable to the consequences of climate change than others. People are at risk of starving as a result of protracted droughts, and conditions like rising

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sea levels and the penetration of salt water have advanced to the point where entire settlements have been forced to evacuate. In the future, more people who consider themselves “climate refugees” are projected. By the year 2050, climate change may force 216 million people to relocate within their own countries, according to The World Bank (2022). As early as 2030, hotspots of internal migration could appear, and they might then expand and get worse. Crop production could be impacted by climate change, especially in regions of the world with the lowest levels of food security. On the other hand, human activities related to farming, forestry, and changes in land use account for around 25% of global greenhouse gas emissions. The agricultural sector is crucial to solving the climate change problem. It is possible to lessen emissions and increase resiliency, but doing so will necessitate enormous social, economic, and technological changes. Priorities for combating climate change also range significantly between nations and among various businesses. Due to the urgency and size of the issue, the participating nations must move quickly to exchange knowledge, adapt to the specifics of their circumstances, and exercise courage in their attempts to put policies in place that would lower emissions and enhance living standards.

Empirical Literature on the Role of Financial Inclusion The literature on financial inclusion is growing in popularity because of the many positive outcomes it has been shown to influence, including poverty reduction, financial system stability, business growth, mental health, and gender equality. Studies after studies have shown that increased access to financial services is a major factor in fostering economic expansion and development. Sethi and Acharya (2018), for instance, analysed the dynamic impact of financial inclusion on economic expansion in both developed and developing nations. The empirical results of the study showed a positive long-run connection between financial inclusion and economic growth in 31 countries. Based on the results of the panel data estimate test, we know that financial inclusion contributes to both economic growth and growth in the overall standard of living. According to Sethi and Acharya (2018), financial inclusion is a key factor in boosting economies. Long-term economic growth can be significantly impacted by policies that prioritize financial sector reforms and financial inclusion. Similar results were found by Huang et al. (2021),

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who studied how financial inclusion and trade openness affected the development of economies in 27 European nations, and by Sethi and Acharya (2018). The research by Huang et al. (2021) shows that “access, depth, efficiency, and overall development of financial institutions have a large beneficial impact on economic growth” in European countries. There were several factors recognized as contributors to economic expansion, including free commerce, abundant capital and labour, and high energy use. Furthermore, Huang et al. (2021) discovered that the impact of financial inclusion on economic growth was larger in low-income countries and new European member states than in high-income countries and old European member states. Financial inclusion, as represented by the spread of financial services across businesses, was found to have a positive effect on firm growth by several researchers, including Sethi and Acharya (2018) and Huang et al. (2021). As less consolidated and more competitive financial institutions such as banks are, the positive impact of financial inclusion on business growth is magnified, as stated by Chauvet and Jacolin (2017). When financial inclusion is high, competitive institutions are more likely to foster business growth, as found by Chauvet and Jacolin (2017). It was also noted that in settings with little financial deepening, company performance and size can benefit from financial inclusion and bank competition. Umar (2020) repeated a similar theme, but this time he centred on Islamic finance and its role in promoting corporate financial inclusion in Nigeria’s Kano State. The study found that businesses that are financially included have better record-keeping, lower risks of bed debt and cash-related risks, higher sales and growth, greater government and NGO support, and more job opportunities. Umar’s (2020) research shows that the widespread adoption of electronic payment methods like point-of-sale businesses increases financial inclusion, which in turn can aid in the maximization of societal wealth and the alleviation of poverty. The success of Nigeria’s monetary policy from 1980 to 2012 was studied by Mbutor and Uba (2013), who looked at the role of financial inclusion. Mbutor and Uba’s (2013) research found evidence for the hypothesis that broadening access to financial services will boost the efficiency of monetary policy. Mbutor and Uba (2013) provide additional evidence that broadening access to financial services can make aggregate demand more sensitive to changes in interest rates, which in turn helps to make monetary policy more effective. Similarly, Banna et al. (2021) looked into the possibility

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that widespread use of fintech-based financial inclusion could encourage financial institutions to assume a greater proportion of the risk associated with lending to borrowers. According to the study’s findings, increased financial inclusion enabled by fintech can help banks reduce their risktaking. According to Banna et al., there is a strong correlation between the use of fintech for financial inclusion and banks’ willingness to take risks in the post-industrial revolution 4.0 age (2021). Another study looked at how financial inclusion affected human development in Sub-Saharan Africa and was conducted by Matekenya et al. (2021). The research shows that financial inclusion positively affects human development. In line with the research of other academics, Matekenya et al. (2021) argue that having easy access to and making use of financial services is beneficial for new business creation, health and education spending, risk management, and even mitigating the effects of shocks. The result will be a positive impact on human progress. Matekenya et al. (2021) concluded that policymakers should take action to help minimize the costs of obtaining and using financial services and to increase knowledge of their availability. Financial inclusion can help alleviate poverty in addition to its positive benefits on the economy. Lal (2018) studied how access to financial services can help reduce poverty by making use of credit unions. According to the results of the research, financial inclusion via cooperative banks has a direct and substantial effect on poverty reduction. According to Lal (2018), an individual’s ability to gain access to a range of financial services, including credit, savings, and more, is a key factor in their ability to break out of poverty. Abor et al. (2018) looked at the possibility that mobile phone use fosters pro-poor growth by helping households allocate spending resources effectively, so allowing them to lift themselves out of poverty. Access to a variety of financial services was also investigated to see if it may help families build self-sufficient futures. The results demonstrated that the probability of poverty is lowered when there is widespread access to cell phones and financial services among households. There is evidence that higher rates of mobile phone ownership and financial inclusion lead to higher rates of household consumption of both food and non-food items per person. Abor et al. found that households in which a woman was the primary breadwinner benefited more from financial inclusion (2018). Financial inclusion was studied by Ouechtati (2020) to determine its effect on poverty and income disparity in developing nations. According to the results, there was an inverse correlation between

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financial inclusion and poverty alleviation. Access to credit and commercial bank deposit accounts were two examples of the kind of financial services that helped reduce poverty. Researchers Ouechtati (2020) found that expanding the availability of credit and money helped to decrease poverty and boost the quality of life for the poor. The results were consistent with those of Abor et al. (2018), who found that credit and a high rate of bank penetration increase the likelihood that low-income households will have access to financial services, which in turn helps to mitigate inequality. The global and Asian ramifications of financial inclusion were examined by Park and Mercado (2018). After talking about how things like the rule of law, GDP per capita, and demographics are influencing financial inclusion in Asia and elsewhere, we can move on to the next topic. Park and Mercado found that lower rates of poverty and income inequality were also associated with higher rates of financial inclusion (2018). Yet, there was no correlation between financial inclusion and economic inequality in developing Asia. Inoue (2019) also investigated how the growth of commercial banking affects India’s efforts to reduce poverty. Public sector banks, but not private-sector banks, showed a statistically significant negative relationship between financial inclusion and financial deepening and the poverty ratio. Most public sector banks had a significant impact from financial inclusion and financial deepening on poverty reduction, suggesting that it is critical to encourage the breadth and depth of public sector banks in India to have a synergistic influence on poverty reduction. Others have echoed Omar and Inaba (2020)’s sentiments, such authors Inoue (2019), Park and Mercado (2018), and even Ouechtati (2020). The effect of financial inclusion on poverty and income inequality in developing nations was studied by Omar and Inaba (2020). The findings show that financial inclusion helps alleviate poverty and income inequality in developing countries. Omar and Inaba’s (2020) research lends credence to the premise that increasing people’s access to formal financial markets can improve their quality of life. Once again, Koomson and Danquah (2021) investigated how financial inclusion affected energy poverty in Ghana. The results of the study demonstrated that a standard deviation decrease in energy poverty of 1.380–1.556 might result from a rise in financial inclusion. Koomson and Danquah (2021) found that homes in rural areas where a man is the primary breadwinner were more likely to be stable over time.

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The research indicates that energy poverty, especially among workers, can be alleviated by a greater margin if financial inclusion is enhanced. Indirectly, through the factors of poor home consumption and household net income, financial inclusion can help reduce energy poverty. According to the available evidence, financial inclusion is essential for combating poverty and promoting social justice. Ajefu et al. (2020), Aguila et al. (2016), and Gyasi et al. (2015) are just a few of the many research that highlights the importance of financial inclusion when it comes to addressing mental health (2019). For instance, Ajefu et al. (2020) studied how access to banking services affected the emotional well-being of Nigerian breadwinners. According to the results, financial inclusion significantly benefited mental health. One study found that “food consumption; remittances; and risk-coping mechanisms” were the channels via which financial inclusion affected mental health. This study’s results add to the growing body of evidence that having access to financial services can help alleviate depression. Aguila et al. (2016) examined the impact of financial inclusion on the mental health of older Hispanic minorities by focusing on account ownership. The results showed that Hispanics’ mental health was positively affected by having a bank account, but there was no evidence that it improved their physical health. Furthermore, those who have a hard time getting their hands-on traditional banking services benefited the most from this study’s findings in terms of mental health. It was found once again that Latinos with lower-thanmedian wealth and those living in less wealthy neighbourhoods benefited more psychologically from having a bank account. From 2004 to 2018, Immurana et al. (2021) examined the effect of financial inclusion on the health of populations in African countries. Using life expectancy and mortality rate as surrogates for population health, the study found that financial inclusion can play an important role in boosting health outcomes. Immurana et al. (2021) argue for more financial inclusion to better the health of Africa’s populace.

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The Role of FinTech for Financial Inclusion in Addressing Climate-Related Challenges in the Fourth Industrial Revolution To solve climate-related difficulties in the context of the Fourth Industrial Revolution, the current study explores the role of fintech for financial inclusion and concludes sustainable development goals. Although financial inclusion can help people become a little more resilient to extreme weather events, changing rainfall patterns, sea-level rise, or saltwater intrusion, a large body of research shows that poverty is worsened by climate change. Savings, credit, insurance, money transfers, and new digital distribution channels can help people cope with changing environmental situations. Digital financial services offer the ability to reach more unbanked people because most people have access to a cell phone, especially women, the poor, and people living in rural regions. During a natural disaster, mobile banking accounts enable the needy to receive money transfers and offer a prompt, efficient, and affordable method of aiding impacted populations.

Financial Services as a Strategy for Resilience Building Increased access to formal financial services, in the words of a thorough study, “may help the poor cope with income shocks, whether these are weather-related like drought or floods, health and well-being concerns, or other unforeseen barriers,” can help the poor deal with income shocks (Poverty Action, 2017). Everything depends on whether effective financial services and products can be developed to meet the unique needs of farmers and other people whose livelihoods are being affected by climate change, make them more affordable for the poor, and encourage the adoption of these services and products. Financial inclusion, according to Arner et al. (2020), “entails offering financial products and services to all members of society at a reasonable cost, and it helps people manage their financial commitments properly, reduces poverty, and supports overall economic growth.” According to Arner et al. (2020), financial inclusion also makes it easier for people to save, which helps them weather economic downturns and invest in their micro businesses, health, and education. Once again, financial inclusion boosts daily efficiency. Bills can be paid electronically without taking time off from work, and it

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enables people to socialize and diversify their financial risks through the financial system (Arner et al., 2020). A case in point provided by Arner et al. in 2020 was breadwinner insurance, which can assist people in preventing a relapse into poverty. Finally, financial inclusion encourages economic growth by increasing the financial resources that may be used to support real-world activities, particularly for individuals and small and medium-sized firms (SMEs).

Channels Through Which Financial Inclusion Can Help to Address Climate-Related Challenges Figure 10.2 presents some of the channels via which financial inclusion can help to address climate-related challenges. These channels include providing access to credit and insurance for the poor as well as providing access to savings accounts for those who are less fortunate. Some of how financial inclusion can help to address climate-related challenges are given in Fig. 10.2. These ways include providing access to credit and insurance, as well as providing the opportunity for the poor to save money.

Access to Insurance

Access to Saving by the Poor

Access and adoption of Clean Energy Acess to Credit

Fig. 10.2 Channels through which financial inclusion can help to address climate-related challenges (Source Author’s Analysis)

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The Poor’s Ability to Save Can Help Them Overcome the Problems Posed by Climate Change Many academics think that higher savings rates can help the poor manage their consumption after unforeseen shocks and bear the strain of slow price increases (Ganong et al., 2020; Meng, 2003). The consequences of climate change on well-being could be reduced by “4.5 to 7.6 percent in Guatemala, Mauritania, Angola, Peru, Gabon, Morocco, Zambia, Colombia, Kyrgyz Republic, Democratic Republic of Congo, Mongolia, Niger, and El Salvador, according to estimations. The best resilience comes from formal savings accounts with financial institutions rather than unofficial savings like cattle or residences because they enable the poor the opportunity to spread their risks, have access to credit, and hasten recovery and reconstruction.” Farmers in Malawi, for instance, boosted their investments in agricultural inputs by 13% and their output by 21% by using savings accounts. According to Karlan et al. (2014), saving is essential for reducing the effects of climate change. Karlan et al. (2014) argue that savings are yet another financial tool that can support investments in climate-resilient technology or help the poor manage their consumption during times of unforeseen setbacks. Also, formal savings accounts are a more advantageous method of money management than socking away cash in the form of cattle or other climate-vulnerable commodities. A variety of digital tools for financial inclusion can assist individuals in saving more money by assisting them in the process of allocating resources for specific purchases using labelling, in which a client labels finances as they set them aside for a clear goal, or commitment devices, in which a saver chooses to restrict access to his or her funds to save towards a goal. These tools can also help users focus investment on agriculture, which is particularly vulnerable to the effects of climate change. A “randomized intervention among Malawian farmers” was carried out by Brune et al. (2016) to encourage formal financial savings for agricultural inputs in Malawi. Contrary to those in the control group, farmers in the treatment group had the option of having their cash crop harvest income put straight into brand-new bank accounts in their names. Both the amount of money saved in the months before the following planting season and the amount of agricultural input used during that season rose because of the experiment. The study also found that the therapy had a favourable effect on household spending and the proceeds from following crop sales. In a different study, Stage and Thangavelu (2019) replicated and reanalysed

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data from a randomized controlled trial of a strategy created by Brune and associates to assist Malawian tobacco farmers with formal money savings. The results showed that giving farmers access to personal savings accounts increased their banking activity and enhanced the welfare of their households. These findings lead us to the conclusion that “greater savings rates can help the poor smooth consumption after unexpected shocks and bear the burden of steadily increasing costs when a calamity occurs.” Another study conducted by Bastian et al. (2018) supports this viewpoint. Women microentrepreneurs in Tanzania were urged to sign up for mobile savings accounts as part of an experiment with and without business training. The findings demonstrated that women were substantially more adept at conserving money using mobile wallets six months following the intervention and that the business training contributed to this development. The results also revealed that women are more likely to obtain microloans using the product’s supplementary mobile account than males. Thus, financial inclusion initiatives must be intensified, especially in regions susceptible to climate change disasters.

Climate Change and Credit Access If they have access to financing, low-income households may be able to defend themselves against the damaging effects of climate change (Calderone et al., 2019). According to Innovations for Product Action, while poor households may find it difficult to afford the high initial costs of low-carbon technology solutions as well as other investments that protect against the unforeseen and slow-but-steady effects of climate change, the availability of credit can eventually spread out these costs (2017). Examples of this from The Innovations for Product Action (2017) were offered. According to Innovations for Product Action (2017), lending to smallholder farmers enables them to invest in agricultural inputs that increase resilience, such as better seed varieties, irrigation, fertilizer, and pesticides. Additionally, it is thought that Farmers could reduce costs during the harvest and planting seasons by adjusting loan disbursement and repayment schedules to their seasonal cash flow, boosting agricultural yields and income while lowering their vulnerability to potential droughts, floods, and other climatic changes (Innovations for Product Action, 2017; Stage & Thangavelu, 2019). Additionally, extensive research has shown that climate-resistant inputs like hybrid seeds can increase smallholder farmers’ resistance to climate-related shocks.

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According to research, more loan availability encourages farmers to invest in technology that boosts crop productivity, such as better seeds, irrigation, fertilizer, and insecticides (Bryan et al., 2009; Le et al., 2020; Ojo et al., 2021). For example, researchers in Kenya found that asset-collateralized loans boosted the use of rainwater collection tanks, giving dairy farmers more reliable and convenient access to water while also improving their productivity (Innovations for Product Action, 2017; Jack et al., 2015, 2016). Another illustration is the Mumuadu Rural Bank in Ghana, which gave farmers who were chosen at random credit along with crop price protection. The loan stipulated that the bank would waive half of the loan balance and interest payments if crop prices during harvest fell below a predetermined amount. Farmers who obtained the loan and crop price protection spent significantly more on inputs, especially fertilizer, than those who did not (Karlan et al., 2011). In previous studies from Kenya, “researchers found that farmers’ fertilizer consumption increased by 14 percentage points on a 23-point baseline when they had the option to buy fertilizer when they received their harvest money.” These results resembled those of a 50% price subsidy. The availability of more finance during the lean season can help farming households allocate their labour more effectively, which will boost production and well-being (Innovations for Product Action, 2017). Another instance involves farmers in Mali who, after getting a cutting-edge loan product suited to their seasonal cash flow, observed a significant increase in farm investments and expenditures as well as fertilizer, insecticides, and herbicides (Beaman et al., 2014; Innovations for Product Action, 2017). Once more, Zambian farming communities that had access to finance that was provided at the start of the lean season and refunded after harvesting generated 5.6% more in average production than comparable households. They were also around 40% less likely to experience food insecurity during the lean season. Customers that experience natural disasters or other unanticipated events may benefit from credit alternatives that offer flexible payback schedules. While combining credit and insurance products may benefit customers who face climate-related risk, the results of research exploring this strategy are inconsistent (Fink et al., 2014). Research has shown that disadvantaged households with financial resources may be able to shield themselves from the negative consequences of climate change. This study demonstrates that poor households

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may be able to protect themselves from the harmful effects of climate change provided they have access to financial resources.

Insurance as a Tool for Dealing with Climate Change Issues It is possible to protect low-income people from a variety of climaterelated risks by expanding their access to a variety of insurance policies. These risks include prolonged droughts, rising sea levels, the transmission of diseases, and an increase in pests that threaten crops as well as the spread of diseases. “Parametric insurance or weather index insurance for farmers, as well as microinsurance for those without standard insurance, provide a buffer against extreme weather occurrences and volatility,” states an article published in 2017 by Innovations for Product Action. The purchase of insurance instils confidence in smallholder farmers, allowing them to pursue investment opportunities and make production decisions that result in increased agricultural output. Farmers in Ghana were encouraged to make larger investments because of the availability of rainfall index insurance, which ultimately resulted in higher returns (Innovations for Product Action, 2017). According to research, farmers who are offered subsidized insurance are more willing to invest in expanding agricultural productivity. Furthermore, under certain conditions, insurance products may be a more effective financial instrument for supporting growth than cash or credit (Sibiko & Qaim, 2020; Vigani & Kathage, 2019). Small-scale farmers, for example, are particularly sensitive to the risk that relates to their investment. If they do not have access to appropriate insurance solutions, they will make decisions to lower risk, which will result in a decrease in profitability. The provision of capital in the form of credit or cash might make it easier to make investments; however, the fact that the returns on such investments cannot be guaranteed may expose investors to a higher level of risk. In addition, they are unable to offer the same level of protection against the dangers that are associated with the weather as insurance can. One of the reasons why the poor are at the greatest risk is because they lack the resources to deal with the challenges that climate change brings to their health and livelihoods. This is one of the reasons why the poor are at the greatest risk. Access to “formal financial institutions, such as insurance, savings, and loans can help the poor smooth consumption when they encounter unanticipated setbacks, according to

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rigorous evidence.” (Access to “formal financial institutions,” such as insurance, savings, and loans can help the poor smooth consumption when they encounter unanticipated setbacks.) As a result, financial services are becoming an instrument that can be used to enhance resilience in the face of shocks associated with climate change. The provision of financial services is a means of boosting the availability, cost, and rate of adoption of environmentally friendly technology that reduces the adverse effects of greenhouse gas emissions. It’s possible that low-income people could have access to especially customized financial services that would allow them to participate in environmentally friendly habits at a lesser cost, lowering their overall impact on the environment. In the following paragraphs, we will provide a more in-depth description of how financial inclusion might assist in the effort to solve issues relating to climate change.

Financial Services as a Means of Increasing Clean Accessibility and Adoption of Clean Technology It is well known that financial inclusion encourages the use of renewable energy sources, which benefits the environment (Feng et al., 2022). Feng et al. (2022) examined the effects of financial inclusion on the utilization of renewable energy sources and environmental quality in China. The results showed that the usage of renewable energy in China was positively impacted over the long term by an increase in the number of “ATMs and general insurance.” According to the findings, financial inclusion encourages the use of renewable energy sources and reduces CO2 emissions in China. Redirecting resources towards environmentally friendly production and consumption is therefore essential. The development of sound environmental practices that lower greenhouse gas emissions and may enhance personal well-being can be encouraged by fintech companies, who can also help the poor make fair investments in other, greener technologies. There is a market for these novel solutions, according to recent research, but further study is required to ascertain the best ways to develop and market products that encourage the adoption of cleaner technologies and better environmental practices (Innovations for Product Action, 2017). One way that financial products might help in the adoption of the best environmental practices is through payments for ecosystem services, in which people or enterprises are reimbursed for providing ecologically beneficial services. In Uganda, Jayachandran et al.

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(2016) discovered that giving landowners incentives to leave trees alone resulted in less tree cover loss in the villages receiving the incentives. The initiative was an efficient and effective way to cut carbon dioxide emissions, and it significantly decreased deforestation in the villages it was intended to help. There is evidence that farmers may be persuaded to engage in environmentally friendly operations if given the right incentives, but more creative thinking regarding how to include such incentive programmes into traditional financial goods and services is required. Green loans, for instance, provide clients with financing to purchase environmentally friendly products like solar panels, better-insulated homes, and organic seeds and fertilizers, according to Innovations for Product Action (2017). However, since they are new, these loans need to be carefully examined to ascertain whether they can promote the adoption of environmentally friendly behaviours and reduce the population’s carbon footprint (Innovations for Product Action, 2017). A sustainable energy technology that could be used in financial services to benefit customers, service providers, and the environment is solar energy microgrids. These grids are made to be user-friendly and affordable, and they can increase the resilience of underserved populations to power outages brought on by natural disasters while simultaneously lowering carbon emissions by burning fewer fossil fuels (Innovations for Product Action, 2017). Consumers can access green technologies like solar microgrids and other alternative energy sources by using “pay-as-you-go (PAYGO) services.” A customer can obtain a small loan to fund the purchase of electricity from an off-grid solar panel and then repay it over time through convenient mobile money accounts. The devices might be switched off as a reminder if a customer forgets to make a payment. These platforms are useful for customers because they can buy as much electricity as they need, but they are also less risky for providers because they can stop providing service if payment is not made. An observational study found that when this technology is available, consumers in South Africa benefit from the flexibility that prepaid power metres provide. Preliminary results from an IPA study conducted in Kenya show that giving Nairobi shops PAYGO solar lamps reduced their consumption of kerosene (Adwek et al., 2020; Ndiritu & Engola, 2020). This discussion has led to the conclusion that, if implemented effectively, financial inclusion can be a tool for combating climate change and achieving Goal 13 objectives.

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Conclusion/Recommendations In recent years, the need for access to financial resources has grown significantly, especially in the face of challenges related to climate change and disaster risks. This has led to the development of various financial instruments, including microfinance, insurance, and cash transfers. Additionally, there has been a rise in the number of development partners who advocate for the use of these tools to address such challenges. To evaluate the effectiveness of financial inclusion in addressing climate-related challenges, a study was conducted, which demonstrated that it could help build the resilience of households, individuals, and businesses. Financial inclusion can assist in providing critical support to victims of climate change and those who must manage new environmental realities, such as changes in rainfall patterns, rising sea levels, and salter water intrusion. The study’s findings highlight the importance of promoting financial inclusion as a means of addressing the risks associated with climate change and achieving sustainable development goals. Governments, civil society, and development partners should prioritize this channel to ensure that vulnerable communities have access to essential financial services such as insurance, savings, credit, money transfers, and digital delivery channels. Overall, it is critical to recognize the role that financial inclusion can play in addressing the challenges of climate change. Its ability to support communities during and after natural disasters and to help them adapt to new environmental realities demonstrates the importance of promoting financial inclusion as a crucial tool for building resilience and promoting sustainable development.

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CHAPTER 11

Artificial Intelligence and Machine Learning in the Power Sector

Introduction While clean, affordable, and reliable energy is necessary for growth, the application of artificial intelligence (AI) in the power sector is increasingly expanding to emerging nations, where it may have a significant impact (Makala & Bakovic, 2020b). When appropriately constructed, AI systems can be especially helpful in automating repetitive and organized operations, freeing up humans to address tomorrow’s power concerns. A basic barrier to progress, the lack of energy access, which affects one billion people, especially in Sub-Saharan Africa and South Asia, influences livelihoods, gender equality, health, and education. It also has an adverse effect on the reduction of poverty. One of the Sustainable Development Goals (SDGs) is universal access to affordable, reliable, and sustainable modern energy, but it will remain merely a goal unless cutting-edge solutions and contemporary technologies can get past the numerous energy-related challenges that afflict emerging markets, from a lack of adequate power generation to subpar transmission and distribution infrastructure to affordability and environmental concerns. Moreover, complicated problems for power generation, transmission, distribution, and consumption in all countries are brought about by the diversification and decentralization of energy production, the introduction of new technologies, and shifting demand patterns.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1_11

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Artificial intelligence (AI) can reduce energy waste, minimize energy costs, and facilitate and speed up the deployment of clean renewable energy sources in power systems around the world. Power system planning, management, and control can all be enhanced by AI. As a result, the development of AI technology is strongly related to our ability to produce the cheap and clean energy needed for growth. To overcome the difficulties involved in integrating sophisticated artificial intelligence technologies into smart energy systems and grids, a comprehensive grasp of computational, economic, and social issues is necessary. Despite the post-industrial society and its effects, the pursuit of workable solutions for global advancement has drawn academia, industry, and society into the mission to achieve sustainable development (Erban & Lytras, 2020). The search for workable solutions to the global development dilemma involves academia, business, and society. Global difficulties for the energy sector include increased consumption and efficiency worries, shifting trends in supply and demand, and a lack of the analytics needed for effective management (Mhlanga, 2023). In nations with developing markets or so-called emerging markets, the severity of these problems is amplified. Unauthorized “connections to the power grid” are prevalent, which suggests that a sizable portion of electricity is neither metred nor paid for. Because of the losses and increased CO2 emissions that occur, efficiency issues are a particularly pressing problem. As consumers receive energy at no cost, they are less motivated to utilize it appropriately (Makala & Bakovic, 2020a). The use of “artificial intelligence and other associated technologies that enable connectivity between smart grids, smart meters, and Internet of Things devices” in the electricity sector has already started in many industrialized countries. These innovations could boost the use of renewable energy sources while simultaneously enhancing power management, efficiency, and transparency (Makala & Bakovic, 2020a). According to Ghoddusi et al. (2019), the use of ML is creating brand-new prospects for groundbreaking research in the fields of energy economics and finance. The growing body of research on the applications of ML in the fields of energy economics and finance was thoroughly examined by Ghoddusi et al. in 2019. They discovered that ML had numerous possible uses. Applications in many fields were discovered by Ghoddusi et al. inquiry’s (2019), including the evaluation of macro and energy trends, demand forecasting, risk management, trading strategy development, and data processing. Three different energy sources crude oil, natural gas,

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and electricity have prices that can be predicted. Moreover, Chen et al. (2020) hypothesized that machine learning is drastically changing several sciences, including physics and chemistry as well as other disciplines. According to Chen et al. (2020), AI and ML can expedite the creation of new materials by establishing material linkages, understanding the chemistry of materials, and understanding material properties. ML is presently being investigated as a brand-new strategy to utilize its capacity to carry out difficult jobs by itself. AI is also being utilized to facilitate the creation of tangible ties. The work Chen et al. (2020) provided demonstrated how ML may be used to analyse a range of energy materials. Rechargeable alkaline batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors were some of these materials. According to Liu et al. (2021), advances in AI and ML will enable data-driven materials research to alter scientific advancements and give rise to new paradigms for the creation of energy materials. These changes are anticipated because of recent technological advancements. As a result, data-driven materials science has a better chance than ever of having a meaningful impact on the results of research. Liu and colleagues (2021) claim that applying ML technology would greatly ease the design and development of advanced energy materials as well as improve the discovery and use of these materials. Recent advancements in data-driven materials engineering further support this claim. Energy “plays a strategic role in the economic and social development of countries,” according to Nabavi et al. (2020: 1). The world’s energy demand has been rising steadily over the last few decades, and trying to predict it is one of the main issues in many nations. According to Nabavi et al. (2020), the residential and commercial sectors are thought to make up about 34.7% of the world’s total energy usage. To supply energy sources and create sustainable energy plans, Nabavi et al. (2020: 1) claimed that governments should anticipate energy demand in these areas. Using both renewable and nonrenewable energy potentials to create a secure and environmentally sustainable energy system is one example of these strategies. According to Nabavi et al. (2020), modelling energy use in the residential and commercial sectors enables researchers to identify the critical economic, social, and technological factors that eventually result in a secure level of energy supply. This argument is based on the discovery that modelling energy consumption in the household and commercial sectors

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is possible by identifying the significant economic, social, and technological components. The framework used to simulate how much energy is consumed by residential and commercial constructions addressed this. In their work from 2020, Nabavi et al. used three different machine learning techniques to predict Iran’s household and commercial energy needs. Some of the techniques that were incorporated into these approaches included “multiple linear regression, logarithmic multiple linear regression methods, and nonlinear autoregressive with exogenous input artificial neural networks.” This study proved successful in foreseeing Iran’s energy needs. According to Xu et al. (2019), accurate predictions of corporate failure in the Chinese energy sector act as a motivator for sustainable investment in the energy industry as well as ongoing development in state power generation. These findings were brought up in the Chinese energy industry. China’s energy sector served as the discussion’s setting during the talk. Hence, Xu et al. (2019) suggested a new integrated model (NIM) for business failure forecasting in the Chinese energy sector by simultaneously considering textual data and numerical data. After laying these foundations, Xu et al. (2019) think that AI and ML will be helpful in the energy industry, especially in emerging markets where energy is produced and consumed. So, the goal of this study is to assess the potential contributions that AI and ML could make to the energy sector, especially in terms of improving energy generation in developing regions. The study’s goal is to investigate the issues that have developed as a result of load shedding and significant electricity shortages in emerging economies.

Machine Learning (ML) Machine learning is a “subfield of AI” that “involves” the development and application of algorithms for data-driven prediction, classification, and optimization systems. The three main subfields that make up “machine learning” are “supervised learning,” “unsupervised learning,” and “reinforcement learning.” Developing algorithms for prediction or classification in the presence of labelled data is known as supervised learning in the domain of machine learning. The inputs (predictors) of these algorithms must be transferred to an output (reaction) (Chen et al., 2020; Mhlanga, 2021a, 2022). When the output is categorical, classification is

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the issue that needs to be resolved; however, when the output is continuous, prediction is the issue that needs to be resolved. Algorithms that can be used for supervised learning include linear and nonlinear regression, random forests, neural networks, and decision trees. Unsupervised learning includes the development of patterns and trends in data that have not been labelled. Instead of attempting to predict the outcome in this case, the objective is to use clustering algorithms and other similar techniques to find common elements in the data (Ghoddusi et al., 2019; Mhlanga, 2021b). They consist of the following analysis of the principal components. Reinforcement learning comprises the act of creating and deploying learning agents in a setting where they can maximize their potential rewards. Energy resource planning is required at the national level, and in South Africa, this is done through the Integrated Resource Plan (IRP), where energy supply and demand predictions are created. By doing this, it is possible to guarantee that there will always be an energy supply to meet the needs of the economy in a range of probable future circumstances. The IRP has thus far been developed using time series approaches and other traditional forest management techniques. The use of ML techniques for scenario analysis and forecasting may help the IRP models perform better. For instance, using Recurrent Neural Networks for demand forecasting and Monte-Carlo methods for sensitivity analysis could both increase the validity of scenario analysis and the precision of predictions. Both methods are currently being investigated and improved. The use of machine learning in energy systems, both in terms of energy production and consumption, holds out a lot of promise. The optimization of energy-producing systems like wind and hydro can be enhanced using ML algorithms. Energy production systems (stations, machinery, and power lines) can also be maintained utilizing predictive maintenance systems, which use condition monitoring frequently carried out using machine learning and the Internet of Things (IoT). The most significant aspect of consumption is energy efficiency. Machine learning is effective at reducing consumption through supervised learning algorithms like neural networks and other approaches. A cooling system would make a good example of this. One should be aware of things like the setting in which it functions, the function it serves, the traits of the people in charge, the activities going on in the room where it is located, and whether it is winter or summer right now. ML performs incredibly well in this situation because the device can accept a wide variety of input values and learn from the data it

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receives, an engineer doesn’t need to regularly alter it. Because there are millions of air conditioners sold and installed each year, optimizing how each one is utilized has the potential to make a significant difference. The right temperatures and humidity levels must be maintained by the heating, ventilation, and air conditioning (HVAC) systems of a structure. According to studies, heating, ventilation, and air conditioning (HVAC) use 10% of the total power used globally and account for more than half of the energy consumed in a building. We have great potential to achieve our sustainability goals by reducing our energy consumption and carbon dioxide emissions through the optimization of HVAC systems. The exploration and drilling of fossil fuel energy sources have been used for both artificial intelligence and machine learning. For instance, the Massachusetts Institute of Technology (MIT) and Exxon Mobil worked together to create self-learning submersible robots that will explore the ocean’s surface in search of possible locations for drilling for oil and natural gas. When paired with machine learning, particularly reinforcement learning, these robots’ ability to collect data about the ocean floor will allow them to learn from their mistakes as they conduct exploration. Despite this, South Africa’s use of smart grids and the development of AI and ML systems and algorithms for grid management may prove to be a key area of application. Electric energy providers and customers may connect in both ways thanks to smart grids. Power grids that use sensors, metres, and other warning devices to collect and display data to users are known as smart grids. These power grids harness the power of machine learning, artificial intelligence, and the Internet of Things. This makes it possible for users to keep an eye on and reduce their energy usage. Intelligent grids are another name for smart grids. Smart grids will assist producers in controlling consumption and avoiding unauthorized power connections, two important issues in developing economies. Producers can monitor consumption and prevent unauthorized power connections with the use of smart grids.

AI Towards a Smart Power Sector With the introduction of technologies like AI-managed smart grids, the future of the power industry seems bright. These are electrical networks that enable utility and consumer interaction in both directions. Smart grids have an information layer incorporated in them that enables communication between its many parts so they can react more quickly to

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changes in energy consumption or emergency circumstances. This information layer enables data gathering, storing, and analysis thanks to the widespread installation of smart metres and sensors (Baloko & Tonci, 2020). Synchrophasors, also known as phasor measuring units (PMUs), are another crucial component of the contemporary smart grid. They make it possible to measure and align data in real time from many remote sites on the grid, which results in a current, accurate, and integrated view of the complete power system and improves grid management. These smart grid components, when combined with robust data analytics, have enhanced the dependability, security, and effectiveness of energy transmission and distribution networks (Baloko & Tonci, 2020; Pinte et al., 2015). AI methods like machine learning are best suited for their analysis and usage due to the enormous amount and varied structures of such data. Many uses for this data analysis include defect detection, preventive maintenance, power quality monitoring, and forecasting of renewable energy sources. Smart metring has become increasingly common thanks to advancements in information and communications technology (ICT), cloud computing, big data analytics, and artificial intelligence (Mhlanga, 2023). Smart metres and other advanced sensor technology are widely used, which has resulted in massive volumes of data that are generated quickly. To handle this data, new methods for storage, transfer, and analysis are needed. As an example, one million smart metres put in a smart grid would produce over 35 billion records at a sampling rate of four times per hour (Baloko & Tonci, 2020). Although the use of smart grids in developing economies lags behind that in advanced economies, several emerging nations, including those at various stages of development such as Brazil, China, Gulf Cooperation Council (GCC) countries, Malaysia, South Africa, Thailand, and Vietnam, have taken steps to adopt them (Baloko & Tonci, 2020; Mhlanga, 2023). It would be practically hard to detect patterns and anomalies across very big datasets on the power demand and supply sides without the aid of deep learning techniques, a subset of machine learning, and this has led to enhanced systems, quicker problem-solving, and greater performance (Mhlanga, 2023). Leading the way in the use of AI in the electricity sector are developed economies. For instance, DeepMind, a Google company, has been employing machine learning algorithms to forecast electricity output for 700 megawatts of wind power in the central United States using neural networks trained on historical data from wind turbines and weather forecasts (Baloko &

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Tonci, 2020). Deep learning algorithms may also learn on their own, and when used to analyse trends in energy data, they do so through trial and error. For instance, Agder Energi and the University of Agder in Norway collaborated to create an algorithm to maximize water use in hydropower facilities (Baloko & Tonci, 2020). Although it may seem like there is an unlimited supply of energy in water, there is a finite amount that can be utilized to generate hydroelectricity, therefore it must be managed wisely. In Canada, Sentient Energy was chosen in 2017 to serve power and natural gas utilities. Sentient Energy is a major provider of advanced grid monitoring and analytics solutions to electric utilities. Issues with hydroelectricity production forecasts can also be resolved with AI. Most nations have trustworthy hydrology data that has been gathered over 40 years, and in some cases, longer, which makes it easier to estimate hydrology using tried-and-true stochastic dual dynamic programming tools. The mathematical models used to operate power generation today are about 30 years old and are typically incompatible with the present hydropower industry realities. Among the many obstacles to maximizing production and profit are the factors’ growing unpredictability, such as future precipitation amounts or prices.

The General Access to Electricity According to a report that was published in November 2019 by the International Energy Agency (IEA), there are approximately 860 million people around the world who do not have access to electricity. Additionally, there are approximately three billion people who cook and heat their homes by using open fires and simple stoves that are fueled by kerosene, biomass, or coal. More than four million individuals lose their lives prematurely due to illnesses related to air pollution in the home. It is for these reasons that the provision of energy is more than just the delivery of power; it is essential to the health and safety of human beings. The following Fig. 11.1 provides an overview of the availability of electrical power around the world. The percentage of the whole population that has access to some form of electrical power is depicted in Fig. 11.1. When it comes to defining what it means to “have access to power,” the term that is employed in international statistics sets the bar extremely low. It is characterized as having an electricity source that can provide

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very rudimentary illumination, charging a phone, or powering a radio for a total of four hours every day (Our World in Data, 2020). Access to electricity is depicted across the world in Figure 11.1. In the year 1990, approximately 71% of the world’s population had access to electricity; in the year 2016, this number had climbed to 87%. On a worldwide scale, the percentage of people who have access to electricity has been continuously increasing over the past few decades (Our World in Data, 2020). In 2016, this indicates that 13% of the world’s population did not have access to electrical power. Electricity is an essential component for the reduction of poverty, expansion of economic opportunities, and advancement of living standards. According to statistics provided by the World Bank, the percentage of worldwide electrification reached 88.9% in the year 2017. Although the proportion of energy derived from renewable sources such as hydroelectric sources increased from 16.6% in 2010 to 17.5% in 2016, these types of sources of power have not yet achieved widespread adoption, which is problematic in terms of sustainability. This is owing, in part, to the fact that renewable energy sources, due to their intermittent nature and difficulty in terms of realtime planning, pose a unique challenge to the power grid (Baloko & Tonci, 2020).

Fig. 11.1 Global access to electricity (Source Our World in Data [2020])

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Because of their speed, robustness, and relative insensitivity to noisy or missing data, AI technologies can help improve the planning, operation, and control of the power system, which is necessary to address this issue. By doing so, AI can make it easier to incorporate renewable energy sources into existing power grids, so enabling the creation of hybrid, lowcarbon energy systems. So, the transition to renewable sources of energy can occur at a considerably quicker rate if artificial intelligence is utilized. Artificial intelligence is now being investigated as a possible answer to the problem of increasing the use of renewable energy. Along with variable electrical loads like electric cars and buses, energy storage (batteries), and decentralized renewable power like rooftop solar PV systems, the increasing development of intermittent wind and solar generation will require either a more stable grid or a smart grid. A smart grid can acquire knowledge and adjust based on the load as well as the quantity of variable renewable energy that is coming into the grid.

The Role of AI and ML for Energy Production and Consumption The utilization of AI and ML in power-generating optimization can be of great assistance to both endeavours. The implementation of artificial intelligence and machine learning in the energy industry in Arica can be beneficial, as was just seen in Fig. 11.2. Some of the potential solutions include predictive maintenance, the exploration of new energy sources, grid management, the application of machine learning to the challenge of excessive energy consumption, and the enhancement of energy efficiency in residential and commercial buildings.

Predictive Maintenance of Turbines and Optimize Energy Consumption The phrase “predictive maintenance” refers to a technique that makes use of several tools and methods for data analysis to spot anomalies in the way that equipment and processes work as well as potential defects in those components and fix problems before they lead to catastrophic breakdowns. IBM created this technique in the 1980s. Predictive maintenance can help the situation because a portion of the problems affecting

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Reducing the overall energy footprint by maximizing efficiency.

Performing predictive maintenance on turbines

AI and ML to Correctly Determine Energy Demand.

AI and ML for Energy Production and Consumption

Energy efficiency in homes

The Ability to Accurately Predict Energy Prices

Grid Management

Fig. 11.2 AI and ML for energy production

the South African power company Eskom is caused by infrastructure failures brought on by ageing infrastructure. This is because some of the issues Eskom has been experiencing are a direct outcome of those issues. Stetco et al. (2019) have recently researched “ML models that have been employed to wind turbine status monitoring (for example, blade defect identification or generator temperature monitoring).” These numerous models are categorized using conventional machine learning techniques, such as data sources, feature extraction and selection, model selection (classification, regression), model validation, and decision-making. They discovered that most models rely on simulated data. Just about a third of the remaining approaches involve classification, and most of them rely heavily on regression. The most often used technologies are “decision trees, neural networks, and support vector machines.” Nearly 2.8 million

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sensor data from 31 Taiwanese wind turbines installed between 2015 and 2017 were analysed by Hsu et al. (2020) to identify wind turbine defects and estimate the amount of maintenance that will be required. The study investigates and forecasts the maintenance needs for wind turbines using historical data on wind turbines that were collected in Chang Hua Coastal Industrial Park in Taiwan. Thirty-one wind turbines in total amassed a total of 2,815,104 observations between the years 2015 and 2017. They achieved a success rate of over 92% in anticipating abnormalities in wind turbines using two different machine learning techniques known as decision trees and random forest classifications. They concentrated primarily on using the maintenance checklist insights offered by the practitioners when assessing the sensor data from wind turbines. They conducted research to identify the root causes of wind turbine problems, separated data on abnormal and typical wind turbine operation states, and created prediction models by fusing data analytics with their practical experience. The results give Taipower and other wind turbine operators accurate cues for identifying issues with wind turbines and predicting how much maintenance will be needed in the future. For quite some time, people’s thoughts have been focused on energy use, both at home and at work. Yet, we have only ever been able to get a broad idea of how much energy is being consumed, without being able to pinpoint which appliances or devices use the most without performing a sizable number of calculations by hand. The growth of the Internet of Things devices and smart metres has upended all of that. Disaggregation, also known as nonintrusive appliance load monitoring (NIALM), is a technique that uses machine learning to examine energy usage on a device-by-device basis. It is easy to identify which home appliances have the greatest monthly operating costs using this technique. This method is also known as nonintrusive appliance load monitoring (NIALM). Consumers that make use of this will be better able to modify their consumption patterns, allowing them to reduce their energy use and save money. Consumers can choose to use pricey appliances less frequently or swap them out for more energy-efficient ones.

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Management of the Grid and the Ability to Accurately Predict Energy Prices In this era of industrialization, data analytics is a field that is growing more and more significant. One sector that has advanced significantly in embracing data analytics techniques is the electric industry. The deployed smart metres and other sensors enable the collection of a sizable amount of data within the smart grid. Without the aid of big data analytics, it would be impossible to process such a large amount of different data. The electrical transmission and distribution network needs big data analytics and machine learning algorithms since they are necessary for data collecting, storage, analysis, prediction for data forecasting, and system maintenance. In addition to ensuring that energy is supplied in the most effective way possible, at the most reasonable price, with the best possible quality, and at the lowest cost possible, these measures can also help to improve customer service and social welfare (Dhupia et al., 2020). Smart grids can help businesses monitor usage and lessen the number of illicit power connections, both of which are major issues in South Africa when it comes to manufacturing. The second issue is that as the use of home power-generating technologies like solar or wind power grows simpler and more accessible, people and companies are increasingly producing their electricity. Power generation systems enable people to generate, consume, and store their energy. Depending on where in the world they live, they could even be able to sell any extra electricity to the local power company. Machine learning can be used to determine the best times to produce, store, or sell this energy. In an ideal scenario, people would use or store energy when costs were low and then resell it to the system when costs rose. Machine learning models can be used to examine historical data, usage patterns, and weather forecasts to produce hourly forecasts that are considerably more accurate. This information can be used by people who own or operate energy generation equipment to assist them in making strategic decisions about how to use their energy. The Adaptive Neural Fuzzy Inference System (ANFIS), which has been put in place to estimate the short-term wind patterns required to generate electrical power, is one example of this. As a result, energy producers can produce the most energy possible and then sell it back into the system when prices are at their greatest. With all of this knowledge, it is incredibly beneficial for organizations and institutions to invest in artificial intelligence and

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machine learning to assure grid management and the ability to predict energy costs with accuracy.

AI and ML to Correctly Determine Energy Demand and Energy Efficiency in Homes There have already been some severe concerns raised about potential supply issues, the depletion of energy resources, bad effects on the environment, ozone layer depletion, global warming, climate change, etc. due to the rapidly growing demand for energy around the world. Between 20 and 40% of the world’s total energy consumption is currently used by structures, which is higher than any other significant industry, including manufacturing and transportation. Both residential and commercial buildings are included in this (Cao et al., 2016). Because of the growth in the world’s population, rising demands for comfort and building services, and an increase in the amount of time spent within buildings, it is anticipated that the rising trend in energy demand will last for a while. As a result, boosting the level of energy efficiency that can be accomplished in buildings is now a top priority for energy policy on all three levels regional, national, and worldwide. The increase in energy consumption among building services, which accounts for 50% of all building consumption and 20% of all consumption in the United States, is especially evident (Cao et al., 2016). The efficient use of energy in homes is the other important issue with AI and ML. As they improve comfort and quality of life, smart home systems have experienced a stratospheric rise in popularity in recent years. The electrical industry has been attracted by significant advancements in the Internet of Things, which has emerged as one of the most crucial applications for smart home technology and has become one of its most significant uses. Smart lighting is one of the Internet of Things-based platforms that may be found in smart homes. Smart lighting is a word that is frequently used to describe lighting systems that offer higher levels of functionality, such as remote dimming or on/off control, to improve user comfort while using less energy. The goal of this study is to determine how employing smart LED bulbs in smart lighting systems affects those systems’ energy efficiency. The amount of electricity needed by the smart LED bulb to produce each unique colour is then calculated and noted. The clever LED bulb can produce a variety of colours, each of

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which calls for a different amount of electricity. The energy-saving capacities of halogen, CFL, LED, and smart LED are additionally examined, along with three case studies. A smart LED bulb only appears to use the least amount of energy when it is remotely dimmed and controlled (Ayan & Turkay, 2020). The efficient use of energy in commercial buildings is a crucial component of AI and ML. According to Robinson et al., buildings are responsible for the consumption of 40% of all the energy consumed in the United States (2017). The distribution of energy intensities requires a thorough understanding on the part of city planners. This is because aspects of urban forms, such as density and floor-area ratios (FAR), affect the amount of energy used by buildings. The Commercial Buildings Energy Consumption Survey (CBECS) data were used to train machine learning models in this study’s novel approach for estimating the energy consumption of commercial buildings based on a small set of physical attributes. A sizable amount of the country’s overall energy consumption is attributable to the building sector, which has led to several environmental issues that endanger human existence. To lower overall energy use and save money, it is becoming more and more common to predict a building’s energy needs. Also, the development of buildings with lower energy requirements will help lower the overall energy consumption of newly constructed structures. The technology that is widely accepted as being the most successful for achieving the outcomes needed in prediction tasks is machine learning (ML) (Olu-Ajayi et al., 2022). An additional crucial topic is the investigation of potential energy sources. Although the seas make up 71% of the planet’s surface, little is known about their subsurface composition. On the other side, advancements in marine robots and artificial intelligence may soon make the ocean floor less mysterious. The knowledge gap between space and the oceans is growing as more and more spacecraft venture outside of our solar system. This conversation has made it necessary for South Africa to utilize the potential that AI and ML provide. Yet, this does necessitate a considerable change in public and private-sector policy, as well as the creation of successful P3s. It is crucial for any utility company to precisely predict the energy needs of their consumers because there is currently no practical option for the storage of significant amounts of energy in bulk. This calls for the delivery and usage of energy almost soon after it is produced. With the use of ML and AI, these forecasts’ accuracy could be increased. By examining past data on energy use, reviewing weather forecasts, and considering the kinds of

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businesses and buildings open on a given day, it is possible to predict the quantity of energy that will be used on any given day. An example of this is how a hot summer day during the workweek leads to higher energy usage since commercial establishments must run their air conditioning systems at full capacity. Rolling blackouts in the summer can be brought on by air conditioners, but they can be avoided if they are detected early enough using weather forecasts and historical data. Machine learning searches for intricate patterns in the many variables that affect demand, such as the day of the week, the hour, the predicted wind and solar radiation, important sporting events, historical demand, mean demand, air temperature, moisture and pressure, and the direction of the wind when attempting to explain shifts in demand. Because machine learning can find more complex patterns, its predictions are more accurate than those generated by humans. This suggests that it’s possible to increase efficiency and reduce costs when buying energy without having to make any particularly expensive changes. According to Erban and Lytras (2020), renewable energy systems may not be dependable if they lack adequate storage capacity in light of upcoming changes in market complexity, demand fluctuations, virtual customers, and other issues. Recent advancements show that even in the lack of thorough long-term meteorological data, their optimization can be provided by AI.

Recommendations for Emerging Markets to Maximize AI and ML in Energy Figure 11.3 highlights the importance of effective and efficient public– private partnerships, government-sponsored investments in artificial intelligence and machine learning in the energy sector, the development of accountable and robust AI methods, the development of trustworthy metrics to assess the performance of the AI model, and an undisclosed Machine learning (ML) and artificial intelligence (AI) are being used more and more in public and governmental settings, particularly in the electrical and energy sectors. Yet, due to the requirements of dependability, accountability, and explainability, it is risky to directly apply AI-based technology to power systems. This is because society cannot afford the expenditures involved with cascade failures and massive blackouts.

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Recommendations For Emerging Markets to Maximize AI and ML in Energy

Emerging Markets AL and ML in Energy Sector

Comprehending the measurements of the electricity system via the lens of physics and using AI algorithms to anticipate demand

Fig. 11.3 Proposals for AI and ML to be effective for emerging markets

Looking to the Future The energy sector in both emerging markets and advanced economies continues to face numerous challenges in terms of efficiency, transparency, affordability, and the integration of renewable energy sources in power systems, even though AI holds considerable potential to improve power generation, transmission, distribution, and consumption. Secondly, while AI firms have a strong foundation in math and computer science, they sometimes lack the experience required to comprehend the details of power systems. And emerging markets are more severely affected by this issue. There is a need to educate the AI business more thoroughly on the facets of the power sector, even though there are numerous and varied potential uses of AI in the power sector. For instance, cloud-based apps are common and essential to AI solutions, but their deployment in the electricity sector is subject to regulatory limitations. Yet, this is altering as the advantages of cloud-based AI applications become increasingly clear. Second, the use of cellular technologies in many emerging markets, especially in low-income nations, restricts the potential of AI in rural and other underserved areas. Smart metres require an ongoing data connection, so a lack of dependable connectivity is a major barrier in places with patchy or

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restricted cellular network coverage. Finally, the power grid has become a target for hackers because of its digital transition. In 2015, Ukraine had the first successful attack of this kind, which left thousands without electricity. Cyberattacks that are successful against vital infrastructure can cause as much harm as a natural disaster. Due in part to the fact that investments in smart metring and automated control have grown to constitute close to 10% of worldwide grid investments, or $30 billion per year devoted to digital infrastructure, the growing threat from hacking has become frequent and a source of serious concern. Fourth, it will be difficult to integrate many data sources and guarantee representativeness given the diversity of the data. A lack of data for machine learning models to learn from could lead to further problems. It could be challenging to contextualize and transfer knowledge from two tasks that are identical to one another. Inaccurate data may also be a problem for these models. Reinforcement learning is being used to overcome some of these issues. Fifth, AI-based models pose a security risk because most of their users are unaware of how they were created and view them as essentially opaque black boxes. Yet because current models are far from flawless, it’s important to put safety measures in place when integrating them into energy networks. When paired with improved analytics, sensors, robotics, and IoT devices, AI can be used to automate routine work so that people can concentrate on more complex problems. Sixth, investments in smart grids have lagged behind investments in smart metres due to an imbalance in priorities. Figure 11.3 shows that Smart metres have attracted a lot of interest. Smart metres are tools for consumer decision-making. Consumers can choose when to switch on or off their electricity and can alter their use patterns, such as during peak hours. As opposed to traditional grids, smart grids focus less on the consumer and more on making quick adjustments to ensure that electricity flows as efficiently as possible, for example, in the event of a disruption caused by a faulty line or imbalances brought on by variable renewable energy penetration. Ultimately, the electricity sector will need to address issues like governance, transparency, security, safety, privacy, employment, and economic repercussions, much like other industries that are progressively employing AI technology. AI will undoubtedly be crucial in lowering distribution losses in developing countries and assisting with reliability and maintenance difficulties. Moreover, AI will support the grid’s integration of intermittent renewable energy sources and will grant distributed

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energy resources and microgrids operational autonomy. Since the private sector is mostly responsible for driving AI innovation, may have a significant impact on bringing AI to the electricity sectors of emerging nations.

Conclusion and Recommendations The energy industry is facing an array of challenges worldwide, including increasing consumption, the need for greater efficiency, and changing patterns of supply and demand. In emerging market nations, these issues are more pronounced. A major challenge is the widespread use of illegal connections to the electrical grid, which results in a significant amount of unaccounted-for energy that goes unused or is wasted. When energy is free, there is little incentive for users to employ energy-efficient practices, leading to unnecessary waste and higher CO2 emissions. This is where industrialized countries’ energy industries have made significant strides in using artificial intelligence and related technologies to manage and streamline the data exchange and coordination between smart grids, smart metres, and Internet of Things devices. The use of AI and machine learning (ML) technologies in smart grids and smart metres has already shown great potential in improving energy management, efficiency, and transparency while also promoting the use of renewable energy sources. In this study, the primary focus was on the challenges faced by emerging nations that lack access to electricity and experience frequent power outages. The study aimed to assess how AI and ML technologies could contribute to the expansion of energy generation in these regions. The discussion centred on potential ways to help developing nations leverage AI and ML to improve their energy sectors. Overall, the use of AI and ML technologies in the energy sector has the potential to address many of the challenges faced by developing nations. These technologies can facilitate better management of energy resources, increase efficiency, and improve transparency. With further research and investment, emerging nations can leverage the benefits of these technologies to meet their energy needs while also reducing the negative impact of energy consumption on the environment.

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Nabavi, S. A., Aslani, A., Zaidan, M. A., Zandi, M., Mohammadi, S., & Hossein Motlagh, N. (2020). Machine learning modelling for energy consumption of residential and commercial sectors. Energies, 13(19), 5171. Olu-Ajayi, R., Alaka, H., Sulaimon, I., Sunmola, F., & Ajayi, S. (2022). Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. Journal of Building Engineering, 45, 103406. Our World in Data. (2020). What share of people have access to electricity? Available online: https://ourworldindata.org/energy-access. Pinte, B., Quinlan, M., & Reinhard, K. (2015, February). Low voltage micro-phasor measurement unit (µPMU). In 2015 IEEE Power and Energy Conference at Illinois (PECI) (pp. 1–4). IEEE. Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M. A., & Pendyala, R. M. (2017). Machine learning approaches for estimating commercial building energy consumption. Applied Energy, 208, 889–904. Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J., & Nenadic, G. (2019). Machine learning methods for wind turbine condition monitoring: A review. Renewable Energy, 133, 620–635. Xu, W., Pan, Y., Chen, W., & Fu, H. (2019). Forecasting corporate failure in the Chinese energy sector: A novel integrated model of deep learning and support vector machine. Energies, 12(12), 2251.

CHAPTER 12

Block Chain for Digital Financial Inclusion Towards Reduced Inequalities

Introduction Since its inception, blockchain technologies have demonstrated significant promise for institutionalizing remittances and expanding access to financial services (Rella, 2019). The potential of blockchain technology to streamline and maybe even replace the infrastructure that underpins international payments and remittances, such as correspondent banking, is thought to have been investigated by both regulators and practitioners. This is done so that transactions can be recorded in a decentralized ledger and validated using blockchain technology. Governmental agreements known as “Nostro-Vostro accounts,” or correspondent banking connections, permit banks to operate in countries where they do not have a physical presence. Correspondent banking is the term used commonly to describe these connections (Rella, 2019). Rella asserts that blockchain technology has been found to encourage rather than hinder the formalization of remittances (2019). To incorporate these applications, the current infrastructure, operations, business strategies, and regulatory systems are currently being modified (Schuetz & Venkatesh, 2020). The second key point is that blockchain technologies represent the most recent version of the technology that ushers in frictionless capitalism, as opposed to the advent of fundamentally new monetary systems. Because blockchain

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1_12

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technology is the most recent development to herald the arrival of frictionless capitalism, it is imperative to make this distinction (Rella, 2019; Schuetz & Venkatesh, 2020). According to research, regulators “developed financial firms, and non-governmental institutions are progressively looking to blockchain technologies as possibly beneficial tools for the institutionalization of value transfers that were historically informal, such as remittances,” as well as for the “financial inclusion” of the unbanked and “underserved” (Abdulhakeem & Hu, 2021; Rella, 2019). Many new companies and more seasoned ones are striving towards the same objective of promoting greater financial inclusion by creating more accessible methods of crossborder payments and international money transfers (Abdulhakeem & Hu, 2021; Rella, 2019). In recent years, the word “blockchain” has gained worldwide significance. The experts concur that this online platform, which will rule the Internet in the twenty-first century, holds immense promise for enterprises all around the world. Blockchain technology, in the opinion of Abdulhakeem and Hu (2021), has the most potential to enhance people’s lives in the ensuing decades. Three instances of how blockchain technology is being applied to many industries include “cryptocurrencies, online payments, and remittances.” Additionally, it discovers uses in the verification of educational content, smart contracts, voting, the healthcare sector, and the Internet of Things (Abdulhakeem & Hu, 2021). Blockchain technology allows for the tracking of physical objects as well as intellectual property rights and a wide range of other things. A sort of distributed ledger technology (DLT) known as blockchain is “a system that enables assets to be moved securely without the need for an intermediary” in addition to acting as a digital ledger (Abdulhakeem & Hu, 2021). Blockchain is described as “a system that facilitates value exchange through collaboration, cryptography, and some smart coding.” With a blockchain network, anything may be tokenized, stored, and exchanged, including money, art, and music. According to experts, blockchain technology, like the Internet, makes it easier to move information. Blockchain is a technology that facilitates more straightforward value exchange (Abdulhakeem & Hu, 2021). By integrating numerous independent systems, academics think that “blockchain technology has the potential to improve efficiency.” Banks and other intermediaries each have their systems, and each entity is completely in control of where and how

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its data is captured, saved, and handled, for instance, in the present financial systems that are in use in various nations. Several countries now use these systems. These systems cannot properly integrate or communicate with one another due to the nature of the systems. This is so that each institution can decide for itself which servers to use, where to put them, and how to set up its security policies. As a result, it is inefficient, costly, and time-consuming to move value across these constrained and centralized systems (Ohnesorge, 2018). Academics assert once more that the creation of Bitcoin in 2008/2009 made it feasible for people and businesses to send and receive money truly peerto-peer across national and international borders without the need for a dependable central body like a bank. It is said that just a few individuals realized the possibilities of technology in its early stages. Blockchains are now, however, sometimes referred to as the “internet of trust.” This phrase, according to Ohnesorge (2018), describes the global potential of blockchain technology, which extends beyond payment systems and permits people who do not trust one another to directly trade (digitally representable) products and services. According to Ohnesorge, this phrase refers to blockchain technology’s potential for use across many industries. The variety of blockchain technology available today, including cryptocurrencies in all of their forms, is astounding. Information technology start-ups and established businesses are always attempting to reduce the time, money, and effort needed to send Bitcoins on a worldwide scale. They are expanding transaction capacity and offering services beyond payments at the same time. It should not be surprising that “the world of international payments has been one of the earliest and most promising applications of blockchain technology.” Initially, blockchain technologies were created to control financial transactions in the decentralized network that underpins Bitcoin (Dwyer, 2017; Janssen et al., 2020). Commercial blockchain technology implementations are currently mired in a cycle of co-opetition, depoliticization of their design, and escalating competition. Blockchain technology is on the rise outside of conventional finance and frequently in opposition to it. Blockchain and distributed ledger technologies make promises about the immutable and transparent recording of transactions as well as quick clearing and settlement. These benefits are made available via decentralized ledgers. The business usage of blockchain technology is causing unclear dynamics in the financial services sector. Conflicts over interoperability and confinement, disintermediation and reintermediation, disruption, and rent extraction are all present in these

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processes (Dwyer, 2017; Janssen et al., 2020). According to Schuetz and Venkatesh (2020), blockchain technology can address most of the issues preventing remote Indian villages from being linked to national and international supply chains, which is necessary for the development of rural India’s economy. The success of financial inclusion programmes in India, according to Schuetz and Venkatesh (2020), is dependent on the widespread adoption of blockchain technologies, thus it’s critical to understand local customs and expectations around the adoption of new technologies. Rural Indians’ inability to access these supply networks is mostly a result of financial marginalization. On top of this foundation, the current study aims to comment on the most significant lessons and benefits for sustainable development and examine the impact that blockchain technology has had on the financial inclusion of the excluded people.

What Is Block Chain Technology? Blockchains are a sort of digital record that cannot be edited without leaving a blatant trail of what has been changed, according to Yaga et al. (2019). Due to the dispersed deployment of these digital ledgers, there is frequently no single repository or governing body, like a bank, company, or government. This is because a centralized repository and authority are not required. Blockchains allow a group of people to track transactions among themselves using a shared ledger at their most fundamental level. It is hard to change a transaction once it has been recorded on a blockchain due to the way the network was intended to function. To put it another way, a Blockchain is a decentralized, immutable ledger that facilitates the logging of transactions and the management of assets within a commercial network. Anything that can be physically touched, such as a home, car, cash, or plot of land, is considered an asset. Intellectual property, patents, copyrights, and trademarks are examples of intangible assets that can also be assets. Everything of value may be tracked and exchanged via a blockchain network, which reduces the related risks and costs for all parties involved (Saberi et al., 2019; Yaga et al., 2019). The launch of the Bitcoin network in 2009 signalled the beginning of the broadly accepted public understanding of blockchain technology, according to Yaga et al. (2019). It is assumed that the transfer of digital information that represents digital payment occurs across a distributed network while utilizing Bitcoin and other theoretically related systems. The first of several cryptocurrencies that are currently in use was Bitcoin, and there are many

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others now. Users of Bitcoin may digitally sign papers and grant other users access to them thanks to blockchain technology. All network participants can independently confirm the veracity of the transactions because this transmission is publicly recorded in the Blockchain data. The use of cryptographic methods and the fact that each participant in the Bitcoin blockchain is responsible for maintaining and managing their copy of the ledger both contribute to the blockchain’s resilience in the face of attempts to modify the ledger. The development of blockchain technology has enabled the creation of several cryptocurrency systems, notably Bitcoin and Ethereum1. Blockchain technologies are so frequently thought to be exclusive to Bitcoin or even cryptocurrency applications in general. This is not the case, though. Even though the technology is currently used in a wider range of applications and is being investigated in a wide range of various industries, this perception still exists (Pilkington, 2016; Saberi et al., 2019; Yaga et al., 2019). Businesses depend on the information, and the speed at which they receive and act upon that information determines how successful they are. As a result, “blockchain technology is ideal for the dissemination of data since it offers details that are instantly shared and open and that are preserved on an immutable ledger that can only be retrieved by network participants who have been approved to do so.” Keeping track of orders, payments, accounts, production, and many other things is possible with a blockchain network. You can see every aspect of a transaction from start to finish because everyone on the network has access to the same version of the truth. You gain more selfassurance as a result, along with new possibilities and benefits. Several electronic payment methods like cash and NetCash existed before Bitcoin, but none of them gained the kind of mass adoption that Bitcoin did. Simply put, the first of many blockchain-based applications that might be developed was Bitcoin. The use of a blockchain allowed for the decentralized deployment of Bitcoin. This suggested that electronic money was not controlled by a single user and that there was no single point of failure. The fundamental benefit of Bitcoin was its ability to permit direct transactions between users without the need for a trustworthy third party, which increased its appeal. Those who can add new blocks to the ledger and keep copies of it are known as miners. This feature also permitted the distribution of fresh money in a predetermined manner to those who were successful in doing so (Pilkington, 2016; Saberi et al., 2019; Yaga et al., 2019; Zheng et al., 2017). Using a blockchain and

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general agreement corrections, a self-policing mechanism was created to make sure that only legal acts and blocks were added to the blockchain. Because of the automated payment of the miners under this strategy, distributed system administration may be carried out without the need for coordination (Saberi et al., 2019; Zheng et al., 2017). Bitcoin can offer pseudo-anonymity since accounting entries can be made without the recognition or authorization processes that are usually required by Know Your Customer (KYC) regulations because all digital currencies are available to the public. Furthermore, because every Bitcoin transaction is auditable, this feature makes it possible for Bitcoin to offer a form of pseudo-anonymity (Dutta et al., 2020; Saberi et al., 2019; Zheng et al., 2017). Until the advent of blockchain technology, trust-building operations were frequently carried out via trusted middlemen. In a situation where users couldn’t be identified, this was crucial. But the demand for a situation where users couldn’t be identified gave rise to the necessity for trust-building initiatives. Because Bitcoin users cannot be easily identified, it was necessary to put procedures in place that would make it simpler for the community to develop trust. The four fundamental characteristics of blockchain technology, which are illustrated in Fig. 12.1, enable the essential degree of trust required among blockchain network users. These qualities do away with the requirement for reliable middlemen. Figure 12.1 is showing the Fundamental Components of a Blockchain. The blockchain can function independently of a central authority. By scaling up the number of nodes that make up a blockchain network, it is possible to increase the network’s resistance to attacks by bad actors. It becomes more challenging for malicious actors to interfere with a blockchain’s consensual mechanism as the number of nodes increases. The system uses a ledger, and to provide a comprehensive history of all transactions, it uses an append-only ledger. Unlike conventional databases, a blockchain does not permit changes to its transactions or values. All users on the network can access an immutable record of all transactions thanks to distributed ledger technology. All transactions only need to be recorded once thanks to the shared ledger, eliminating the normal effort duplication associated with more traditional corporate networks. Blockchains that are cryptographically secure ensure the data’s integrity and make it feasible to confirm that the ledger’s records haven’t been altered. Owing to this functionality, once a transaction has been recorded in the distributed network, it cannot be changed by any user. A secure

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Fig. 12.1 The fundamental components of a blockchain

blockchain has been built to prevent unwanted access. Both deals can be seen after a new transaction has been entered to correct an error in the transaction record. Many parties simultaneously have access to the ledger. By doing this, it is made sure that everyone using the blockchain network has access to the same information. By recording a set of rules that may be automatically implemented on the blockchain, smart contracts offer a technique to speed up transactions. Smart contracts are the name given to these regulations. A smart contract can be used for many different things, including, among many other things, specifying the terms for the payment of travel insurance and the conditions for the transfer of corporate bonds. This trust may enable quicker and cheaper transaction delivery by allowing individuals and companies to deal directly. These qualities permit some degree of confidence among couples who are unfamiliar with one another. This trust may enable direct interactions between individuals and organizations. “Permissionless blockchain networks” are blockchain networks that do not impose any restrictions on who can create accounts or take

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part in transactions. These characteristics enable parties who have never met previously to establish some degree of confidence. These features support the already-present trust between users of the permissioned blockchain network, which more rigorously restricts access than other blockchain networks. “A blockchain is a data structure that holds transactional records while assuring security, transparency, and decentralization,” claim Abdulhakeem and Hu (2021). With the aid of open-source software, this “technology” permits decentralized management of transaction data across a global network of computers. Cryptocurrencies like Bitcoin and Ethereum use blockchains. A consensus process that is independent of any one authority must be followed before any changes to the software that runs on a blockchain can be made. This system abides by the idea of transparency because all transactions are recorded on a public ledger that is maintained online and available to the public. Figure 12.2 is showing the types of blockchain networks. There are numerous methods for constructing a blockchain network. These may be made public, kept private, authorized by the government, or the result of a team effort. Public blockchains are blockchain networks that are accessible to everyone, like Bitcoin. The need for a sizable amount of computing power, the lack of transactional privacy, and insufficient degrees of security are some potential drawbacks. These are crucial factors to remember in the context of blockchain applications in organizations. Private blockchain networks are peer-to-peer distributed computer networks, just like public blockchain networks. Yet, “the network’s governance is under the control of a single body, which is also in charge of implementing a consensus mechanism and keeping track of the shared ledger.” This could greatly increase participants’ levels of trust and confidence, depending on the situation. Additionally, a private blockchain might be run inside the boundaries of a company’s firewall or even hosted locally. Networks on private blockchains with permits are frequently created by companies that also build private blockchains. It is crucial to remember that even open blockchain networks may assign privileges to their nodes. As a direct result of this, limitations are placed on the kinds of transactions that can occur and the users who can utilize the network. Before taking part, participants must have either an invitation or authorization. Blockchain-based consortiums—The responsibility for keeping a blockchain current might be divided among several businesses. Who is permitted to view the data or submit transactions is decided by these preselected companies. They choose who has access to the data as well. The

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Consortium blockchain networks

Fig. 12.2 Types of blockchain networks

ideal option for a business to employ is a blockchain that is controlled by a consortium when all parties involved in a transaction need to have authorization and must take turns being in charge of the blockchain.

Digital Financial Inclusion In line with the World Bank digital financial inclusion refers to the availability of a range of formal financial services that satisfy customer needs and are ethically provided at a cost that is affordable for customers and profitable for service providers. Using effective and affordable digital tools is necessary to provide services to those who are currently underserved or unreached. The three main components of any type of digital financial service are a digital transactional platform, retail agents, and the usage of a device—most frequently a mobile phone—by both customers and retail agents to complete transactions via the platform. A platform for digital transactions serves as the foundation for any digital financial service. If a customer uses a digital transactional platform, they will be able to send and receive payments and transfers using a device, as well as keep value electronically with a bank or nonbank that is allowed to do so. The user will also have the choice of keeping money in an authorized nonbank electronic storage account. Consumers who interact with retail agents who are furnished with digital devices that are connected to the communication system to transmit and receive transaction details have the option of turning cash into electronically stored value and back into cash. Information about transactions can be sent and received by retail agents. Retail agents can accomplish this goal by exchanging information about transactions. Depending on the regulations in force at the time and the details

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of their contract with the big financial institution, agents may also be required to perform other duties. The customer device may be a physical object, such as a credit card, that links to a digital object, such as a POS terminal, or it may be a digital object, such as a mobile phone, that serves as a method of transmitting data and information. The customer’s system might also combine analogue and digital features.

Industry 4.0 The Fourth Industrial Revolution, also known as “Industry 4.0,” represents a significant shift in how people interact with one another, go about their everyday lives, and go to work (Lasi et al., 2014; Mhlanga, 2020; Sony & Naik, 2019). A new chapter in the history of mankind has been added by the incredible technological breakthroughs, which are on par with those of the First, Second, and Third Industrial Revolutions. Because of technological improvements, the physical, digital, and biological worlds are becoming more intertwined, which has the potential to lead to both great promise and great hazard (Ghobakhloo, 2020; Lasi et al., 2014). Because of the speed, scope, and depth of this shift, we are being pushed to reevaluate not only the mechanisms that enable nations to prosper but also how businesses create value and even the fundamental foundation of what it means to be a human. Leaders, politicians, and individuals from all socioeconomic groups and countries can harness convergent technologies to build an inclusive and people-centred future during the Fourth Industrial Revolution. This opportunity is open to all nations and entails more than just a technological revolution. In other words, people are just as important as technology in the Fourth Industrial Revolution (Ghobakhloo, 2020; Lasi et al., 2014). The real opportunity lies in thinking outside the box of technology and figuring out how to enable the greatest number of people to positively influence their communities, families, and workplaces. Finding ways to make technology available to as many people as possible can help with this.

Sustainable Development The growth that meets current needs without compromising the capacity of future generations to meet their own needs is referred to as “sustainable development.” This broad definition of sustainable development covers a wide range of topics. The devastation of the environment and

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the growing socioeconomic divides are unintended consequences of the quest for economic expansion. A more thorough growth plan is required to implement the concepts of sustainable development. This plan should advance the three fundamental tenets of social inclusion, environmental sustainability, and economic prosperity (Parris & Kates, 2003; Rogers et al., 2012). The start of the industrial revolution sparked a tremendous economic boom as well as several technological breakthroughs, including the invention of electricity. In many parts of the world, coal has historically been a cost-effective energy source, but doing so entails costs for society and the environment. The burning of coal releases harmful greenhouse gases, which have a significant role in the accelerated rate of climate change. A nonrenewable resource is coal. A more sustainable alternative would be to implement technology that is more energy-efficient and to diversify our energy sources (Jabareen, 2008; Sachs, 2015). Alternative energy sources don’t harm the environment or people’s health. An example of this kind of energy source includes biomass, wind, and solar energy. The Brundtland Report, also known as Our Common Future, was released in response to the need for a more sustainable approach to development. This document is also frequently referred to as the Brundtland Report. Renewable energy innovations may potentially create new business opportunities (Brundtland, 1987; Sachs, 2015). The United Nations Committee on Environment and Development’s report from 1987 served as the first to propose the idea of sustainable development and its guiding principles.

Empirical Literature Review Blockchain technology’s key benefit is its ability to solve trust problems decentralized, without the involvement of a central authority or third party. Since its inception, blockchain technologies have demonstrated enormous potential for the formalization of remittances and the expansion of financial inclusion. A growing number of studies have also demonstrated the significance of blockchain technology for financial inclusion across a range of scenarios. Chen and Bellavitis (2019) examines the advantages of using decentralized finance, as well as the various business models now in use, and evaluates any potential difficulties and constraints. Blockchain technology, according to Chen and Bellavitis (2019), can reduce transaction costs, facilitate distributed trust, and make decentralized platforms simpler to use, hence paving the way

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for new business models. Blockchain technology paves the way for the establishment of more decentralized, creative, interoperable, borderless, and transparent financial services in the financial sector. Blockchain technology, according to Chen and Bellavitis (2019), can reduce transaction costs, facilitate distributed trust, and make decentralized platforms simpler to use, hence paving the way for new business models. According to Chen and Bellavitis (2019), decentralized financial services can reduce transaction costs, increase the population’s access to financial services, allow open access, foster permissionless innovation, and create new revenue opportunities for innovators. According to Chen and Bellavitis, decentralized finance, a relatively young field of financial technology, has the potential to change the structure of modern finance and create a new environment for innovation and entrepreneurship (2019). This exemplifies how decentralization can act as a springboard for the development of original business ideas. By providing the first studies of Malaysian and Shariah-compliant crowdfunding, Muneeza et al. (2018) assessed the value of crowdfunding for financial inclusion. They looked at how blockchain technology might affect the expansion of crowdfunding as well as the value of crowdsourcing for financial inclusion. According to Muneeza et al.’s research from 2018, the emergence of novel digital financial technologies, like blockchain and crowdfunding, creates new potential for reaching communities and groups who are economically disadvantaged. According to Muneeza et al.’s research from 2018, platform operators may be able to solve some of their present challenges by leveraging blockchain technology. Crowdfunding is an effective technique for expanding people’s access to financial services. In a different study, Schuetz and Venkatesh (2020) argued that for isolated villages in rural India to experience economic development, those communities needed to be connected to local and global supply chains. Due to high rates of financial exclusion, rural Indians are unable to join these supply networks. Schuetz and Venkatesh conducted a literature review on financial inclusion, adoption, and blockchain technology in India in 2020. Geographic access, high cost, subpar banking products, and financial illiteracy are the four predictions they base their research on for how to address the issue of financial exclusion. Additionally, blockchain technology has the potential to overcome the majority of these obstacles, claim Schuetz and Venkatesh (2020). To advance the development of such an understanding, Schuetz and Venkatesh (2020) set a research agenda on the causes, patterns, and impacts of adoption.

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This is essential if blockchain technology is to become the cornerstone of initiatives to advance financial inclusion. According to Mavilia and Pisani (2020), the first use of blockchain technology was as a means to sustain Bitcoins, the most well-known and divisive cryptocurrency in the world. But in a short period, it has established a reputation as a disruptive technology that has the power to both transform and establish new sectors. Mavilia and Pisani (2020) provided an overview of the core characteristics and capabilities of blockchain technology. The authors then focused on potential applications that could be used for developing nations. According to Mavilia and Pisani (2020), the empirical analysis highlights the shortcomings of the continent’s current financial system and forms the basis for the discussion of potential blockchain solutions to reduce the level of financial exclusion that exists today and promote sustainable development for African countries. The empirical analysis demonstrated the inadequacies in the continent’s current financial system, and the authors’ prior claim was supported by these considerations. Centralized institutions have historically offered financial services, claim Danho and Habte (2019). As a result, financial systems are now governed by several central parties. Nonetheless, some contend that the concentration of power has widened the wealth gap. Yet more recently, as blockchain technology has developed, conventional ideas about democratization and transparency have evolved. Blockchain has been investigated as a technology that has the potential to make a difference in the effort to reduce poverty levels because it has been highlighted as a critical step that must be made to increase financial inclusion. Danho and Habte’s (2019) study examines how blockchain technology can broaden access to financial services in Africa. According to Danho and Habte’s research (2019), blockchain is viewed as beneficial for mobile financial services partly because it can cut costs by eliminating middlemen, automating procedures, and fostering decentralized trust. Additionally, Danho and Habte (2019) discovered that the current usability of blockchain is significantly harmed by the lack of uniform protocols and terminology. As a result, blockchain cannot yet have a significant effect on the expansion of financial inclusion. According to a study by di Prisco and Strangio, blockchain technology may be able to assist underdeveloped nations with their limited access to traditional financial services (2021). A new financial ecosystem targeted to the needs of those with low socioeconomic status was to be created by the African blockchain company Wala. di Prisco and Strangio (2021)

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researched the past of Wala. Wala was created to aid persons with low socioeconomic status in resolving their issues. Despite its early success, di Prisco and Strangio (2021) assert that Wala was forced to exit the market in 2019. This provides a unique case study to assess the challenges standing in the way of poor countries utilizing blockchain technology to its fullest capacity. di Prisco and Strangio (2021) point out the necessity for quick action to solve the issue of the digital divide and to stimulate the use of new digital technologies in place of the preexisting informal channels because there is a mismatch between BCT capabilities and low SES requirements. According to Norta et al. (2019), getting credit and sending money across borders are still difficult, time-consuming, and expensive operations. According to Norta et al. (2019), the current methods for transferring money have several limitations, such as lengthy lines, exchange rate losses, counterparty risks, bureaucracy, and a lot of paperwork. Furthermore, Norta et al. (2019) argued that one of the most important steps in eradicating global poverty and boosting local economies is providing sustainable financial services to the target population. According to Norta et al., an estimated two billion adults lack bank accounts and have either no access to or very little access to financial services (2019). To make it simpler to access financial services, the Everex programme leverages blockchain technology for cross-border transfer, online payment, currency exchange, and microlending. This is achieved without the inherent volatility present in cryptocurrencies that do not employ stablecoins. Again, Norta et al. (2019) pointed out that the Everex wallet enables a bridge from fiat currency to cryptocurrency, making it easier to access cryptocurrencies. This makes it possible for our users to buy and sell tokens quickly without going to an exchange. Despite the presence of this invention, the traditional banking system has fallen short of the benchmarks set by other technological advancements. Despite the internet’s growth opening up a whole new world of potential in life, including banking, Abdulhakeem and Hu (2021) claim that the current financial system has fallen short of these aspirations. According to a study by Abdulhakeem and Hu, while almost everyone in the modern world has access to the Internet, not everyone has a bank account (2021). Although the Internet has made it possible to transfer information across the world in a matter of milliseconds, time and money are still required when it comes to financial assets. The World Bank Group estimates that 1.7 billion people worldwide still lack any access to banks. Furthermore,

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Abdulhakeem and Hu (2021) proposed that recent developments in technology, particularly blockchain technology, have generated a growing trend towards decentralization in the financial industry. Satoshi Nakamoto’s game-changing invention, the Bitcoin Blockchain, was the first to facilitate peer-to-peer transactions without the use of intermediaries or centralized systems. Another blockchain, Ethereum, was developed six years later by Abdulhakeem and Hu. Ethereum has served as the cornerstone of potential decentralized financial systems. Furthermore, since their inception, blockchain technologies have shown to have great potential for the formalization of remittances and the expansion of financial inclusion, according to Rella (2019). Regulators and practitioners have recently looked into how blockchain technology can displace the correspondent banking infrastructure that underpins cross-border payments and remittances, according to Rella (2019). Rella (2019) argues that blockchain technologies are not an example of fundamentally new monetary systems; rather, they are the most recent in a series of technological advancements that herald the arrival of frictionless capitalism.

Results and Discussion It is argued that 31% of all adults or 1.7 billion people according to the “World Bank estimate” do not have a bank account. This percentage “reaches as high as 61 per cent in some emerging countries, and women are at an even larger disadvantage, constituting 55 per cent of the unbanked population.” Bank accounts and credit cards are examples of digital technologies. Automated teller machines (ATMs) are frequently taken for granted; despite the crucial role they serve in ensuring that those who are excluded can access formal financial services. There are few simple alternatives for the “1.7 billion people who the traditional banking system has neglected, many of whom are already experiencing economic hardship, to send and receive money, accumulate savings, obtain credit, or obtain insurance.” It’s crucial to have access to alternative financial services since emergencies can be disastrous if you don’t have a financial safety net. Yet, blockchain technology has unexpectedly great potential for expanding access to the financial system globally. Technology is “global, open-sourced,” and accessible to anyone with Internet access, including people of various nationalities, ethnicities, races, genders, and socioeconomic classes, according to Chapiro (2021). Many people “associate

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blockchain technology with cryptocurrencies like Bitcoin and Dogecoin and possibly with greed, illegal activity, or environmental carnage, but at its core, the technology is nothing more than a decentralized method of organizing transactions in a database, or ledger, in such a way that multiple untrusted parties can agree on the state of those transactions without the need for a middleman.” Each transaction that is recorded is open, accessible, and encrypted, as was previously described. This is because blockchain enables financial transactions to be carried out in a way that is more safe, affordable, and efficient than the conventional options, changing the role that banks, governments, and businesses play (Chapiro, 2021; Pilkington, 2016; Saberi et al., 2019; Yaga et al., 2019; Zheng et al., 2017).

Strategies for Broadening Access to Financial Services Using Blockchain Blockchain technology has the potential to have a variety of effects on digital financial inclusion. These potential outcomes are represented visually in Fig. 12.3.

Utilize the Potential of Blockchain Technology in Financial Transactions

Block chain technology as a tool for boosting financial savings

The Application of Blockchain Technology to the Provision of Credit

Utilization of Block Chain Technology in the Process of Providing Insurance

Fig. 12.3 Strategies for broadening access to financial services using blockchain

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Blockchain technology has the potential to have a variety of effects on digital financial inclusion. Figure 12.3 gives a visual representation of these potential outcomes, which include utilizing blockchain technology’s potential in financial transactions, using blockchain technology to increase financial savings, using blockchain technology to provide credit, and using blockchain technology to provide insurance.

Utilize the Potential of Blockchain Technology in Financial Transactions A distributed ledger and faster, cheaper, and more secure payment processing options are provided by blockchain technology, in addition to the potential for increased participant trust. Blockchain is currently employed in a variety of businesses, including payments, after first serving as a platform for virtual currency. By utilizing encrypted distributed ledgers that provide trustworthy real-time transaction verification, blockchain technology enables international payment processing services and other types of transactions. As a result, intermediaries like clearing houses and correspondent banks are no longer required. Blockchain technology was initially developed to support the digital currency known as Bitcoin, but later research has suggested that it may also have a wide range of other uses. Our payment system needs to be updated because transactions can frequently take days to complete, there are extra fees, and there is inadequate security. This is one of the key causes of customers’ reluctance to hold or move their money through payment methods. In addition, a sizeable percentage of the population does not have access to safe banking and payment methods. Blockchain technology has the potential to have a significant impact on this industry. It is possible to give people the chance they deserve and address a significant number of the problems that have long dogged this industry. The cost of money transfers can be high; for transactions under $10, Western Union may impose a fee of up to 35%. Although those with greater wealth may be able to afford it, for those who reside in rural locations, this is a substantial sum of money. Access is another problem, in addition to the exorbitant prices. The 1.7 billion people without bank accounts not only find it more difficult to send and receive money, but also frequently lack the paperwork required to open an account, such as a passport, proof of income, dependable internet, and a smartphone. Mobile payments have made great strides in achieving this goal during

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the past ten years. One of the programmes that UNICEF has funded is called Leaf. No matter if the mobile device is a smartphone, anyone in Rwanda can use it to send and receive money directly from a mobile device. Neither a passport nor an internet connection is necessary. 5,871 users from the countries of Kenya, Uganda, and Rwanda had finished 97,819 transactions as of October 2021. $4.97 is the typical amount sent from one person to another, a sum for which conventional money transmitters would impose exorbitant costs. Many of the customers are Rwandans who were compelled to flee the DRC and are now able to get financial support from family members who reside outside. Some individuals use Leaf to purchase items like groceries and vegetables. Kotani Pay is yet another app that is accessible in Kenya. With this service, Kenyans may send and receive Bitcoin regardless of the device’s quality by just inputting a brief code on their phone, which then converts the cryptocurrency to Kenyan shillings. As of October 2021, 137,195 transactions had been finished by 2,598 users who were based in Kenya, Uganda, and Rwanda. The total amount of money traded by users as of October 2021 is over $400,000, with a $1 average transaction per user. “Both Leaf and Kotani Pay are employing blockchain technology to facilitate speedier and more secure money transfers,” claims Chapiro (2021). They provide users with the same benefits as mobile money does, plus a secure peer-to-peer transaction interface that runs at cheap prices (below 2% to cash out) and reduces transaction times from three to five seconds. Leaf and Kotani Pay both work on a global, decentralized platform, in contrast to mobile money, which was initially meant to be a domestic substitute. This would suggest that the currency exchange rate is the sole additional expense related to carrying out the company on a worldwide scale. Blockchain applications are an increasingly appealing technology to use for remittances, especially for the transfer of small amounts of money, due to instantaneous, affordable, and traceable transactions that can hold multiple currencies in multiple mobile networks both domestically and internationally. Blockchain-based applications can hold many currencies on various mobile networks. Applications built on the blockchain could hold different kinds of money.

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Blockchain Technology as a Tool for Boosting Financial Savings One of the most crucial determining variables has always been the cost of integrating new technologies. You can save money by using self-service technology, which can also cut down on the time and emotional labour needed. According to the definition, it refers to “the extent to which the client believes that employing a certain framework would minimize the amount of money spent on operating the service.” In the past, banks have served as a bridge between people who have a financial surplus of money and people who have a financial lack of money. By taking advantage of the “spread of the transaction,” which is the gap between the interest rates that banks charge borrowers and pay depositors, banks have been able to make money. The financial system has been successful in achieving this goal, but due to its complexity and high operating costs, they have only been able to do so by imposing admissions barriers, which has led to many individuals falling behind. Chapiro (2021) asserts that a sizable section of the world lacks access to savings accounts. “If villagers or farmers are fortunate enough to have any savings at all, it is frequently in the form of physical assets, such as animals,” this means. In an emergency, it may be difficult to dispose of these assets, and hoarding cash makes one more vulnerable to inflation. According to a study from 2022 by Ullah and his associates, blockchain technology can handle financial activities more effectively than the prior method. According to Ullah et al. (2022), past research results show that self-service and e-commerce technologies can lower transaction costs. These studies were used to support them. Modern, game-changing technology can lower distribution costs like those related to e-logistic services as well as transaction costs like those related to data encryption. It is generally accepted that a cost decrease will have a positive impact on perceived usefulness, perceived convenience of use, and perceived intention to use all of which can have a positive impact on financial inclusion. There are many platforms where blockchain is being used, and one of them is the fact that it is a helpful tool for low-income households in terms of cost-saving measures. For instance, the Argentine company Xcapit, which uses blockchain technology to provide a decentralized ledger, offers a different platform that makes it easier and less scary for people to save money and invest even if they do not have a bank account,

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credit, or financial knowledge. Opening an account is simple and open to everyone; all you need to do is download the app and turn on the wallet. According to Chapiro (2021), all a person needs to start investing is a cryptocurrency, which they may either acquire from Xcapit’s app partners or transfer from another platform. This is the only prerequisite. After passing a screening process that necessitates them to provide personal information, such as information as basic as their name and cell phone number, users choose the amount of money they want to invest and the level of risk they are ready to face. By answering a test that is built into the app, they are allowed to learn about their investor profile. Xcapit has created several financial products that offer various rates of return. Customers can withdraw their money at any time and may keep track of how their investments are doing using the app. The money is always accessible to them, even when it is being invested. Blockchain enables Xcapit to provide customers with a simple investment interface, one in which they can rely on the network’s integrity to protect the safety of their funds and have access to a more streamlined and affordable investment procedure. It “controlled about $3.6 million in total assets and had 3,590 users from Argentina, Mexico, Brazil, and Colombia with average investments of $1,500 apiece over the past two years.” Between January 2021 and October 19, 2021, active strategies used by it produced returns of up to 15.36% in Bitcoin units and “up to 8.83 per cent in USD” (Chapiro, 2021).

The Application of Blockchain Technology to the Provision of Credit Credit scores, which banking institutions have historically used to systematically determine eligibility and type, are crucial to the growth of the economy because they enable families to buy property and acquire credit facilities, give businesses access to capital, and help countries keep consumption levels stable during challenging economic times. More than one-third of the population, or the majority, do not have credit records, thus they do not have a backup plan in case of emergency. The International Finance Corporation estimates that the annual loan shortfall for micro, small, and medium-sized businesses in developing countries is $5.2 trillion. Decentralized credit scoring refers to the concept of establishing a borrower’s creditworthiness using either off-chain or on-chain data without the involvement of an intermediary. This study is performed on a

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blockchain, a distributed P2P computer network that acts as a decentralized database. Grassroots Economics, a blockchain-based project with a Kenyan base and support from UNICEF’s Innovation Fund, is one initiative that seeks to minimize the disparity in access to credit that exists in low-income communities. Villagers may not have access to a line of credit or a significant amount of money during a crisis, but they still have tangible goods and services, such as crops or clothing, or even the labour of a cook or teacher. The idea underlying the project developed by Grassroots Economics is helpful in these cases. The idea of Community Inclusion Currencies was developed by Grassroots Economics. With the help of this concept, the company will be able to create tokens that are backed by all the real goods and services provided in a specific neighbourhood. These goods and services include things like the town’s food and water supplies, as well as labour from carpenters and babysitters. The tokens can be used by Kenyan villages to establish a credit line that is secured by their assets and can be used in times of need. Without utilizing Kenyan shillings or a bank, people can monetize their assets and extend or receive credit by using Community Participation Currencies. Instead, users can receive credits for future production based on past trades equal to up to 10% of their prior annual earnings, allowing them to continue trading even when their liquidity is low. A blockchain records every transaction that occurs when people exchange tokens using feature phones. This ensures that users’ financial transactions with one another are completely secure. Individuals who have a history of responsible money management in the same neighbourhood are more likely to be approved for loans. According to Chapiro (2021). 58,400 Grassroots Economics users transacted tokens worth $3 million in 2020. More than 95% of clients were successful in using their CICs. The usual constraints of information gaps, traceability, transaction transparency, and credit issues in supply chain management are eliminated by blockchain technology. Its foundation of it is cutting-edge technology, including distributed ledgers, symmetric encryption, authorization, consensus processes, and smart contracts. This is consistent with Tan et al.’s (2020) assertion that blockchain technology has been heavily utilized in supply chain management. Moreover, Zou and Xue (2020) argued that while credit banks offer learning opportunities and orientations for developing abilities, it is challenging to monitor the results of learning due to their current centralized management structure. They proposed the idea that blockchain technology would be able to tackle

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the problem of credit management in credit bank systems. A connection is established between the block, the participants in the course, and the learning objectives thanks to blockchain technology. Due to its capacity to materialize decentralization of management, automation of transfer regarding learning credit and education, verifiable outcomes, and immutability, blockchain technology can “save management resources for credit banks and boost confidence.” By system design and testing, it is possible to implement the credit management of a credit bank system using the Hyperledger fabric technology. A blockchain-based credit rating system was also developed by Mao et al. (2018) to improve administration and oversight inside the food supply chain. By using smart contracts that are stored on the blockchain, the technology collects traders’ written credit evaluations. After that, a deep learning network called Long Short-Term Memory is used to thoroughly examine the assembled text. In conclusion, regulators base their management and monitoring on the outcomes of traders’ credit. Traders can be held accountable for their actions during the transaction and credit assessment process if blockchain technology is used. Authorities can collect data about traders that is more thorough, accurate, and reliable. The results of research done by Mao et al. (2018) show that when it comes to comprehending the credit evaluation text, employing Long Short-Term Memory outperforms traditional machine learning techniques like Support Vector Machine and Naive Bayes. The application’s general simplicity is aided by the system’s interface’s usability.

Utilization of Block Chain Technology in the Process of Providing Insurance Insurance policies frequently demand confirmation of identity, financial stability, and extra documentation, all of which may act as entry barriers. Even for those who have insurance, it is not always obvious who will pay the bills after a disaster or how expensive they will be. Blockchain technology will enable significant cost savings, cost reductions, transparency, faster payouts, and fraud prevention. Additionally, it will make it possible for data to be trusted and transparently shared in real time between various parties. The achievement of these benefits will result from the use of technology. Blockchain technology can be used to create new insurance business models, which could enhance markets and products. In a highly competitive market, consumers and businesses alike expect the

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best value for their money and the world’s best online shopping experience from the organizations they do business with. The introduction of blockchain technology presents the insurance sector with an opportunity for growth and constructive disruption. Although there aren’t as many blockchain projects concentrating on insurance as there are on other aspects of financial inclusion, the technology has the potential to be useful. The ETHERISC, which is backed by the Ethereum Foundation and ACRE Africa, is one instance of a group that has cooperated to develop a decentralized insurance policy to safeguard small farmers in Africa. The software allows farmers access to weather index insurance plans that are underpinned by self-executing contracts stored on a blockchain and known as smart contracts. If extreme weather affects the farmers’ crops, these contracts result in rewards. These smart contracts function like a straightforward if-then formula; for instance, if it rains 5 inches in 24 hours everywhere, the insured farmer will be paid for flood-related damages immediately after the contract. The agreements are linked to real-time weather information, which includes information on temperatures, rainfall, wind direction, sunshine hours, and even storms and hail. If a climate-related hazard materializes, the automated technique improves the insurer company’s operations, and the farmers are protected from the risk by fair and transparent recompense. Farmers would need to prove the harm, which is a procedure that can be time-consuming and may prevent them from swiftly recovering from the disaster if blockchain technology did not offer transparency and automation. 17,000 Kenyan farmers had gotten this insurance by the end of 2021, and they were utilizing the programme’s flexibility (Chapiro, 2021). Moreover, Singh et al. (2019) argued that utilizing blockchain technology is a practical way to deal with the issues related to conventional forms of auto insurance. According to Singh et al. (2019), the standard vehicle insurance procedures used by insurance companies rely on analyzing the history of the drivers’ behaviour to establish the appropriate premium amount to be paid by cars. But, telematics-based usage-based insurance (UBI), which is cutting-edge and effective, is a way to insure vehicles. Singh et al. (2019) asserted the following, as a contrast to the traditional approach, the UBI bases its premium calculations on drivers’ recent behaviour. When it comes to traditional techniques for motor vehicle insurance, there is a lack of transparency in the way claims are processed. In addition to delaying the processing of claims, this lack of transparency encourages

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several fraudulent behaviours. The application of blockchain technology may show to be an effective strategy for overcoming obstacles.

Blockchain Technology, Digital Financial Inclusion, Towards Sustainable Development Financial inclusion is a crucial component in the accomplishment of a number of the Sustainable Development Goals for 2030. This includes Sustainable Development Goal 1, which aims to put an end to poverty; Sustainable Development Goal 2, which focuses on preventing hunger and promoting sustainable agriculture; Sustainable Development Goal 3, which aims to improve health and well-being; and Sustainable Development Goal 5, which aims to achieve gender equality and economic empowerment for women. In addition to this, the Sustainable Development Goal aimed at combating climate change (SDG 6) and the Sustainable Development Goal aimed at enhancing implementation techniques (SDG 17) also emphasize financial inclusion. Financial inclusion can play a significant role in achieving these developmental goals and ensuring sustainable development on multiple fronts if it encourages the mobilization of savings for consumption and investment. This can be accomplished by promoting the saving of money for both consumption and investment. With the promotion of inclusive growth and the addition of $3.7 trillion to the GDP of emerging economies over the next decade, digital finance has the potential to be beneficial to billions of people. Moreover, financial inclusion has the potential to improve economic and financial stability, as well as domestic savings mobilization and the expansion of government revenue. Institutions such as the World Bank and the International Monetary Fund have promoted financial inclusion as a method for alleviating poverty in nations that are still in the process of building their economies. Low-income families can better protect themselves against unforeseen financial events, invest in their human capital by way of healthcare and education, and accumulate modest assets through the process of financial inclusion, which enables them to take advantage of promising investment opportunities in their respective economies. One of the more recent tactics that financial institutions have been implementing is known as “financial inclusion.” This is done to give appropriate information to potential customers who come from socioeconomic groups who have low levels of education and no access to financial resources. This strategy has as its goal the education of those with lower levels of

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understanding about financial tools that have the potential to improve their day-to-day lives. In summary, financial inclusion is an essential factor in the achievement of a wide variety of development objectives, and it has the potential to foster inclusive growth, improve economic and financial stability, and support overall economic growth as well as the accomplishment of general development goals.

Conclusion and Recommendation In conclusion, the utilization of blockchain technology presents a promising pathway towards achieving digital financial inclusion and reducing inequalities. The potential of blockchain technology to provide secure, transparent, and decentralized financial transactions is crucial in bridging the gap between the financially excluded and the mainstream financial system. This technology can significantly transform financial services by creating new opportunities for individuals, small businesses, and marginalized communities to access essential financial services, such as savings, credit, insurance, and investments. The adoption of blockchain technology by regulatory authorities, policymakers, and financial institutions is critical in ensuring that its potential is harnessed for the greater good. Governments in developing economies should prioritize investing in blockchain technology to provide essential financial services to their citizens and promote sustainable development. The lessons of sustainable development highlight the importance of addressing social and economic inequality, and blockchain technology provides a tool to achieve this goal. Blockchain technology can facilitate digital financial inclusion by enhancing trust and accountability, creating new investment opportunities, and promoting transparency in financial transactions. In conclusion, the utilization of blockchain technology is a crucial step towards achieving digital financial inclusion and reducing inequalities. The potential of blockchain technology to create a more inclusive financial system can contribute to the achievement of Sustainable Development Goals and promote sustainable development. The integration of blockchain technology into financial services presents a promising future for financial inclusion and reducing inequalities, and its adoption should be prioritized by regulatory authorities and financial institutions alike.

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CHAPTER 13

The Power of Smart Technologies for Global Partnership for Development

Introduction The Sustainable Development Goals (SDGs), also known as the Global Goals or Agenda 2030, were formed by the United Nations in September 2015. These goals were created with the intention of “putting an end to poverty, safeguarding the planet, and ensuring prosperity for all” (Eweje et al., 2021; Mhlanga, 2021). Multi-stakeholder partnerships (MSPs) between governments, businesses, civil society, financial institutions, donors, and academic sectors are increasingly being suggested as fundamental to achieving Sustainable Development Goals (SDGs). The creation of the SDGs has reenergized the focus on finding long-term solutions to resolve the “grand challenges facing the world” (Eweje et al., 2021). According to Clarke and MacDonald (2019), the presence and complexity of local sustainable development difficulties demand coordinated action from many actors in the commercial, public, and civil society sectors. This is the case since the challenges must be addressed simultaneously. According to Clarke and MacDonald (2019), big multistakeholder collaborations that create capacity by cultivating and using the unique perspectives and resources of partner organizations are becoming an increasingly attractive strategy for addressing problems of this nature. The significance of multistakeholder initiatives has been recognized in the United Nations Agenda 2030, and the United Nations has committed to

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achieving sustainable development goal 17 Global Partnership for Development, which will promote the successful implementation of sustainable development goals and “revitalize the global partnership for sustainable development.” In other words, the Sustainable Development Goals (SDGs) are a road map to solve the global wicked problems to create a world that is peaceful, prosperous, and environmentally sustainable. The SDGs have a global scope and are interconnected in nature, and their implementation requires a transformation in how the partnership is conceived and governed. Partnerships are groundbreaking forms of governance that can pool together multifaceted expertise and resources from the business sector, the government sector, and the civil society sector. Partnerships involve sharing knowledge, consolidating resources, and balancing the skills and knowledge of multiple sectors, which is expected to create synergies that result in unexpected positive and sustainable outcomes (Bäckstrand, 2006). The United Nations considers multi-stakeholder partnerships to be one of the most important mechanisms for “mobilizing and sharing knowledge, expertise, technologies, and financial resources to support the achievement of the sustainable development goals in all countries mobilizing and sharing knowledge, expertise, technologies, and financial resources to support the achievement of the sustainable development goals in all.” MSPs, or multi-stakeholder partnerships, are instrumental arrangements that serve as irreplaceable conveners, connectors, and catalysts in the pursuit of sustainable development. To put it another way, MSPs are instrumental arrangements that align the interests of various stakeholders to a unified agenda. In this chapter, we will evaluate the role that can be played by smart techniques to enhance that there are multistakeholder partnerships between government, business, civil society, and academic sectors as a critical and one of the best approaches to achieving the Sustainable Development Goals (SDGs). This argument will serve as the foundation for this chapter.

Sustainable Development Goals In 2015, the United Nations presented the Sustainable Development Goals (SDGs) to the world as a comprehensive plan to address the most significant global concerns of poverty, environmental degradation, and inequality. This revision of the concept of Sustainable Development

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Goals (SDGs) Recognizing that achievement in one area can have beneficial spillover effects in other areas, the 17 Sustainable Development Goals (SDGs) were created to be interrelated and interdependent on one another. The Sustainable Development Goals (SDGs) have as their overall objective the creation of a world that is sustainable on all fronts socially, economically, and environmentally. In addition to addressing other urgent problems on a global scale, such as gender equality, quality education, and access to clean water and sanitation, the Sustainable Development Goals (SDGs) were developed to put an end to poverty, hunger, and discrimination. These objectives are lofty and will call for considerable contributions from all facets of society, including individuals, governments, enterprises, and organizations representing civil society, among others. The idea that Sustainable Development Goals (SDGs) can only be achieved through the concerted efforts of all nations and every facet of society is one of the most compelling justifications for adopting them. Sustainable Development Goals (SDGs) cannot be achieved by any one nation or group working in isolation. They call for the participation of all nations, as well as cooperation and collaboration on a global scale, in addition to the pooling of monetary, technological, and informational resources. In addition, the Sustainable Development Goals were developed to be interconnected, which means that achievements made towards the achievement of one goal can have a positive impact on the achievement of other goals. For instance, expanding access to educational opportunities can assist in alleviating poverty and fostering increased economic growth, and safeguarding the environment can assist in promoting public health while simultaneously lowering the danger of natural catastrophes. The Sustainable Development Goals (SDGs) were developed as a universal call to action to assist in the formation of a world that is more sustainable, egalitarian, and prosperous for all people. The realization of these objectives calls for the concerted efforts of all nations and all parts of society, as well as the acknowledgement that advancement in one domain can have beneficial effects in other domains.

Sustainable Development Goal 17 One of the 17 global objectives that were approved by the United Nations in 2015 is referred to as Sustainable Development Goal 17, usually abbreviated as SDG 17. Its purpose is to invigorate global cooperation for sustainable development while also working to improve the methods of

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execution. To accomplish the remaining 16 Sustainable Development Goals, it is essential to ensure that all nations and other relevant stakeholders can collaborate productively (SDGs). The precise goals that are included in SDG 17 are as follows, Renewal of the global cooperation for sustainable development and improvement of the means of its implementation, to accomplish the Sustainable Development Goals (SDGs), this calls for more cooperation not only between nations but also across other parts of society. Raising the availability of technology and access to it, even in developing nations: This implies supplying developing countries with the tools and resources they require to achieve sustainable development. Improving North-South, South-South, and Triangular Regional and International Cooperation: This aspect of achieving the Sustainable Development Goals (SDGs) entails enhancing collaboration between developed and developing countries, as well as between other areas of the world. Assistance in the form of both financial and technical assistance is offered to developing nations to aid them in the process of formulating and putting into action policies and plans that promote sustainable development. This is part of an effort to support developing nations in formulating and putting into action policies and plans for sustainable development. Supplying developing countries with adequate and predictable means of implementation means supplying developing nations with the financial and technical resources they require to accomplish the Sustainable Development Goals (SDGs). Increasing the participation of all countries, but especially developing countries, in the decisions that affect their lives requires providing developing countries with a bigger voice and role in the decision-making processes that take place at the international level. Cooperation between all nations and all aspects of society is required to accomplish Sustainable Development Goal 17, which states that “all nations and all sectors of society.” It requires developed nations to offer the support and resources needed for emerging nations to accomplish the SDGs, and it demands developing nations to take responsibility for their development and work towards achieving the goals. Developed countries are required to provide the resources and assistance necessary for developing nations to achieve the SDGs. Sustainable Development Goal 17 is a very important objective since it ensures that people all over the world will be able to collaborate successfully to fulfil the other 16 SDGs. It will be feasible to establish a future that is more sustainable, egalitarian, and prosperous for everyone if we increase

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the means of implementation and revitalize the global partnership for sustainable development.

Technology and Partnership for Goals The United Nations has established a platform that acts as a central location for the formation of multi-stakeholder partnerships to advance sustainable development and the 17 Sustainable Development Goals (SDGs). This framework invites all interested parties, including Member States, civil society, local authorities, the private sector, the scientific and technological community, the academic community, and others, to voluntary basis register commitments or partnerships to drive the implementation of the 2030 Agenda and the SDGs. The SDGs are a set of 17 goals that aim to end extreme poverty, protect the planet from dangerous climate change, and ensure that all people enjoy economic, social, and cultural rights. This platform is managed by the Division for Sustainable Development Goals of the United Nations Department of Economic and Social Affairs. Its purpose is to collect registers from a variety of United Nations conferences and procedures about sustainable development. They include the United Nations Ocean Conferences in 2017 and 2022, the Society for International Development Conference in 2014, and the Rio+20 Conference in 2012. Access to multi-stakeholder action networks that are maintained by other UN institutions and actors is also provided by the platform. These networks help to mobilize alliances and pledges in support of Sustainable Development Goals (SDGs). The platform, which was designed with intelligence as well as inclusivity in mind, makes use of the potential of AI to facilitate improved connections between the United Nations and important parties working towards a future that is both sustainable and equitable. This is an interesting opportunity to make use of technology in the pursuit of Sustainable Development Goals (SDGs).

Inclusive by Design Online Physical gatherings have historically been a terrific method for the UN to bring people together around common interests and experiences, but COVID-19 and many other restricting constraints mean that they are no longer the best alternative. Here are some of the issues the UN has with traditional events and how we’re attempting to address them. Because attendance is expensive, inequity is ingrained in physical events. There

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may be travel involved, one must be able to take time off work, and there is frequently a participation fee. This implies that many people are excluded from the process from the start, including those who may have tremendous plans and dreams for resolving our shared sustainability problems. By removing geographic limitations from the equation, going online significantly levels the playing field. There is essentially no cap on the number of attendees for an online event. As a result, the cost of the event can be shared among a larger number of participants, lowering the per-participant cost. Costs are further decreased by not having to rent out expensive auditoriums or conference rooms. There is no escaping the reality that traditional festivities have a severely detrimental effect on the environment. Events with more sustainable designs generate less trash and emissions. The average daily CO2 emissions from transport alone are 150 kg per conference attendee. This implies that a two-day gathering of 1000 people would need to consume roughly 700 barrels of oil. The time restrictions that apply to a regular event do not apply to virtual events. Material may always be accessible. To access the shared information, you are not required to be in a certain location at a particular time. There are numerous ways to contact numerous stakeholders during an online meeting that is supported by technologies like artificial intelligence. You can add someone to your contact list to message them later, invite them on a video call, start a chat spontaneously, or meet in the audience to speak during a presentation. There are no time restrictions for networking because the event is live for a considerably longer period than a regular event, which means that the window for making connections is larger. Because of this, you can arrange a meeting for a long time following an event. According to Rokonuzzaman (2019), enhancing the ability of less-developed countries to create wealth in a globally connected, competitive economy while inflicting less damage on the environment is the key to accomplishing sustainable development goals. To address this fundamental problem, technology is crucial. For instance, if developing nations continue to increase output to satisfy rising consumption, pollution would have major detrimental consequences on sustainability. On the other hand, less-developed nations would lose trade competitiveness if they were unable to keep up with the technological advancement of more developed nations, which would cause them to lag in terms of generating new income and jobs. The developing

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global economic paradigm, which is being fueled by the rapid advancement of next-generation digital technologies and economic nationalism, should be taken into account while forming technology partnerships for sustainable development. The Fourth Industrial Revolution, which includes digital technologies such as artificial intelligence (AI), robotics, automation, and big data, is fast undermining the developing world’s edge in low-cost labour. The COVID-19 pandemic has forced UNICEF to consider fresh approaches to filling the final digital mile. As prior modes of outreach, such as community activities, are no longer available, formerly hard-to-reach communities have grown even more inaccessible. UNICEF country offices have used the Internet of Good Things (IoGT) to provide programmes remotely to overcome this difficulty. A web-based content management system provided by IoGT, a global provider of digital goods, offers access to content without data fees through collaborations with mobile network carriers and Free Basics by Facebook. IoGT can be adapted as a website to accommodate country offices’ demands and those of their programmes and to support external content from like-minded implementing partners or ministries. With low-end phone optimization, compatibility for regional languages, an accessible design with low-literacy consumers in mind, and little bandwidth usage, IoGT covers the final digital mile. The year 2020 will see the release of job aids, training materials for frontline workers, and important initiatives for risk communication and community involvement funded by IoGT. The platform expanded from 18 to 29 countries, more than doubling its annual reach to over 25 million site views. IoGT has been incorporated into all facets of UNICEF’s digital programmes. The IoGT sites at UNICEF country offices will be updated to version 2.0 in 2021. With the release of this new version, IoGT becomes a progressive web application that aims to offer offline access, scale content across websites quickly, and enhance user experience on mobile devices. A quiz module and the ability to track user performance across numerous surveys and quizzes are among the new features. In Zimbabwe, COVID-19 has had a significant influence on mothers and children. Many of the pandemic’s secondary impacts, including malnutrition, child marriage, physical, sexual, and gender-based violence, as well as limited access to medical care, are especially dangerous for adolescents and young children. Ability to communicate while maintaining a physical distance is one of the many difficulties COVID-19 brings.

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Zimbabwe has a high mobile phone penetration rate of over 90%; however, many people find it difficult to access the Internet since data is so expensive. UNICEF and Econet Wireless launched IoGT in June with a free virtual performance via Facebook Live, simulcast on a local radio station, to address disparities in access to data and close the digital divide. Through virtual interactions with musicians and presentations by well-known artists, the launch aimed to raise awareness of and increase traffic to IoGT. The three primary languages spoken in Zimbabwe will all be represented in the content distribution on IoGT. Popular areas include information on COVID-19, career advice, and All In, a section specifically for teenagers. The number of subscribers increased dramatically after the launch event in June, going from 195 at the time the epidemic was announced to 130,000. Revitalize the global partnership for sustainable development, according to Goal 17 of the Sustainable Development Goals. Immediate action is required to mobilize, refocus, and unleash the transformative power of trillions of dollars in private resources to achieve sustainable development goals. The demand for long-term investments, particularly foreign direct investment, is greatest in developing nations. They encompass information and communication technologies, infrastructure, and renewable energy. This will call for more effective and persuasive communication among the world’s people at the individual, organizational, and governmental levels than is currently the case. Due to the availability of smarter, less expensive technologies, which will considerably aid in the transmission of media material among a changeable global audience, coming from the local or limited regional level, artificial intelligence journalism will contribute to supporting this cooperation (Abdulzaher, 2019). Enhance knowledge sharing on mutually agreed-upon terms and North-South, South-South, and triangular regional and international cooperation on and access to science, technology, and innovation, including through improved coordination among current mechanisms, at the UN level, and through a global technology facilitation mechanism. To encourage the creation, transfer, dissemination, and diffusion of ecologically sound technologies into developing nations on advantageous conditions, including preferential and concessional terms as mutually, agreed. Improve the use of enabling technologies, particularly information and communications technology, and fully operationalize the technology bank and the framework for least developed nations to increase their capacity in science, technology, and innovation

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by 2017. Strong, more dynamic, 24/7 media operations that reach audiences across legal and physical borders are necessary to meet all these demands. The era of artificial intelligence in journalism will be characterized by that. Artificial intelligence journalism necessitates the availability of open data and the analysis of large data, which greatly improves the quality of global cooperation and hence the achievement of Goal 17 of the United Nations. To support national strategies to execute all the sustainable development goals, especially through North-South, South-South, and triangular cooperation, Goal 17 seeks to increase international support for doing so. Journalism powered by artificial intelligence will firmly encourage international collaboration. Everyone will be a development partner, therefore there won’t be any isolated nations or minorities that are cut off from the rest of the globe. Developing nations are also impacted by issues that affect the developed world, and vice versa is true for issues that have a detrimental impact on the inhabitants of emerging nations. In addition, multi-stakeholder partnerships will mobilize and share knowledge, expertise, technology, and financial resources to support the achievement of sustainable development goals in all countries, particularly developing countries. Lastly, AI journalism and the media technologies of the Fourth Industrial Revolution will enhance the global partnership for sustainable development. It is crucial to plan and implement low and zero-carbon electricity and transportation projects today that support long-term decarbonization and resilience to climate change impacts because infrastructure investments last for decades. For this purpose, President Biden announced the formation of the Global Partnership for Climate-Smart Infrastructure in April 2021. This partnership will link American businesses with significant renewable energy and transportation infrastructure initiatives in developing nations. Since 2021, USTDA has already provided funding for more than 50 initiatives that have the potential to open $65 billion in climate finance for energy and transportation projects that support ambitious climate targets in low- and middle-income nations. To address the demands of international partners for climate mitigation and adaptation, these initiatives are projected to generate $15 billion in U.S. exports of cutting-edge products and services. Using the USTDA’s project planning and partnership-building toolbox, the Global Partnership for Climate-Smart Infrastructure enables the deployment of American-made technologies and services to actualize climatesmart energy and transportation infrastructure in emerging economies.

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Together with reverse trade missions, industry conferences, and expert workshops, this also includes feasibility studies, technical support, and pilot projects. The Global Partnership for Climate-Smart Infrastructure, run by the U.S. Trade and Development Agency (USTDA), promotes the development of high-quality clean energy and transportation infrastructure in developing nations by easing the introduction of revolutionary U.S. products and services in a variety of fields, including advanced nuclear technology, methane abatement, energy efficiency, low- and zerocarbon hydrogen, waste-to-energy solutions, and carbon capture. The Partnership has concentrated on creating initiatives that advance intelligent transportation systems, electrification of surface transportation, clean mass transit, sustainable aviation, low-carbon freight, port modernization in line with climate objectives, and other priorities in the transportation sector. The Global Partnership for Climate-Smart Infrastructure, as President Biden has stated, “will create good-paying employment here in America by supporting the building of new, clean infrastructure in our partner countries.” These are the kinds of collaborations that will benefit us all. The American Clean Power Association, the Intelligent Transportation Society of America, the National Electrical Manufacturers Association, the Nuclear Energy Institute, the Solar Energy Industries Association, the United States Chamber of Commerce, the United States Nuclear Industry Council, and the U.S. Departments of Commerce, Energy, and Transportation are just a few of the organizations that USTDA collaborates with through the Global Partnership for ClimateSmart Infrastructure.

Smart Technologies and Their Contribution Towards Global Partnership for Development The Fourth Industrial Revolution The present phase of technological development, known as the Fourth Industrial Revolution (4IR), is defined by the convergence of physical, digital, and biological systems and is reshaping all facets of society and the global economy. The 4IR offers a chance to tackle some of the most important issues facing the globe and advance the pursuit of universal objectives. In this section, we will examine how the Fourth Industrial Revolution can support the global alliance for universal objectives and

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present illustrations of 4IR’s usefulness in this setting. The 4IR is distinguished by the spread of smart and linked systems, ranging from wearable technology to industrial control systems. These systems can be used to track and manage a variety of societal functions, such as trash disposal and energy use, helping to advance global objectives for sustainable development. The 4IR is also marked by Big Data Analytics’ exponential increase of data from a variety of sources, including sensors, social media, and satellite imaging. Big data analytics can be used to examine this data to learn more about many facets of society and to help realize international objectives like tracking and monitoring disease outbreaks. The other technology is augmented reality (AR), which allows users to interact with virtual items and information in real time by superimposing digital information over the real world. Through the provision of users with access to digital materials and the opportunity to study in novel and creative ways, augmented reality (AR) can be utilized to help the fulfilment of global goals relating to education and training. The advancement of artificial intelligence (AI), which has the potential to completely change how businesses function and help us reach our global objectives, is another feature of the 4IR. AI, for instance, can be used to automate procedures like data processing and monitoring, allowing businesses to function more effectively and efficiently. Once more, robotics is an important part of the 4IR and has the power to revolutionize a wide range of sectors, including manufacturing, agriculture, and the healthcare industry. Robots can be utilized to help the world accomplish its goals for health and well-being by giving everyone access to high-quality healthcare, wherever they may be. These are only a few instances of how the Fourth Industrial Revolution might support the global alliance for universal objectives. The 4IR offers a tremendous opportunity to tackle some of the most important problems facing the globe and advance the attainment of global objectives. To guarantee that 4IR technologies make a positive contribution to the global partnership for global goals, it is crucial to ensure their responsible use, which includes ensuring their accessibility and affordability. The Fourth Industrial Revolution has the potential to significantly contribute to securing international cooperation towards universal objectives. Organizations and governments can operate more productively, make better decisions, and allocate resources more effectively by integrating 4IR technology into the global partnership for global goals. A more sustainable, just, and wealthy world can be achieved through the proper use of 4IR technology.

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Artificial Intelligence (AI) The use of artificial intelligence (AI) has the potential to play a big part in the establishment of global partnerships for the achievement of global goals all over the world. There are many ways that AI can contribute to and improve global collaboration to accomplish global goals. AI algorithms can process enormous amounts of data derived from a variety of sources, which enables them to deliver useful insights and assist in recognizing trends and patterns. For instance, satellite imaging and machine learning algorithms can be employed to track and monitor environmental deterioration, such as deforestation, and offer information on the rate and magnitude of these changes. This can be done, for example, to monitor and track environmental degradation. After that, the information can be visualized with the use of tools like GIS software to assist in guiding decision-making and prioritizing efforts towards reaching global goals relating to the environment. When information is collected from numerous places, various stakeholders are then able to determine how they might combine their efforts to rectify the irregularities that have been identified. Using predictive modelling, AI can make significant contributions. Modelling and predicting the effect of various actions and policies on global goals can be accomplished with the help of AI. For instance, AI systems can be trained on data relating to energy consumption and greenhouse gas emissions to forecast the influence that alternative energy policies will have on these variables. This can be useful in assisting decision-makers in determining which projects are likely to be the most effective in accomplishing their goals and where resources should be spent. Again, this is possible because AI can automate many mundane and time-consuming jobs using automation, freeing up resources that can be directed towards the achievement of global goals. For instance, methods for machine learning can be used to automate the examination of big datasets relating to poverty, such as statistics on household income, to locate regions that have a disproportionately high concentration of people living in poverty. Following the collection and analysis of this data, priorities may be established for fighting poverty, and resources can be distributed accordingly. AI is also capable of doing effective monitoring and assessment, and it can provide real-time data and updates to aid with monitoring and analyzing progress towards global goals. For instance, AI systems could be

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trained on data about disease outbreaks to provide early warning signs in the event of a prospective outbreak. Following the collection and analysis of this data, appropriate and efficient measures can be taken to prevent further disease transmission and advance the cause of global health. AI algorithms can be used to support decision-making through a process known as decision support, which involves the provision of information and recommendations. For instance, AI algorithms can be trained on data relating to economic indicators such as unemployment rates to make suggestions on employment practices that are most likely to achieve the goal of full and productive employment as well as decent work for all. This can be accomplished by providing suggestions on employment practices that are most likely to achieve the goal of full and productive employment and meaningful work for all. These are only some of the ways that AI can contribute to the global cooperation to accomplish global goals. The use of artificial intelligence has the potential to play a key part in the establishment of global partnerships and the acceleration of progress towards the attainment of global goals. Yet, it is essential to keep in mind that AI is merely a tool and that to ensure that it makes a good contribution to the global partnership in achieving global goals, its usage must be conducted ethically and responsibly. In general, however, AI possesses the potential to usher in a new era of innovation in the implementation and monitoring of global partnerships for global goals. As a result of introducing AI into the global partnership for global goals, companies and governments will be able to work more efficiently, make more well-informed decisions, and more effectively allocate resources towards the accomplishment of the goals. The ethical use of AI has the potential to help bring about a world that is more environmentally friendly, egalitarian, and affluent for everyone. Machine Learning Machine Learning (ML) also can play a significant role in assuring worldwide collaboration towards the achievement of global goals in every region of the world. There are many ways that machine learning can lend assistance to and make improvements to global collaboration for achieving global goals. One of them is called predictive analytics, and it involves using machine learning to analyse large datasets to recognize patterns and trends. This enables organizations to make decisions that are more informed and to allocate resources more effectively to achieve

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global goals. For instance, machine learning can be applied to the analysis of satellite data to forecast the development of crop diseases. This paves the way for early intervention and contributes to the realization of global goals connected to food security (Mhlanga, 2021; Wang et al., 2022). ML algorithms can be used to construct decision support systems, which can then assist organizations in making informed decisions connected to global goals. Decision support systems can be developed using decision support systems. For instance, machine learning algorithms can be used to determine the most efficient methods for cutting greenhouse gas emissions, which can contribute to the realization of global targets relating to the mitigation of the effects of climate change (Hoosain et al., 2020; Vinuesa et al., 2020).). Customized Interventions are also a possibility thanks to machine learning. ML can be utilized to deliver individualized initiatives, trying to address the specific challenges and needs of various communities and assisting in the accomplishment of global goals related to health and wellbeing. Personalized interventions can be used to address the specific needs and difficulties of different communities. For instance, machine learning algorithms can be used in the creation of individualized plans for diet and exercise, which can assist individuals in achieving and sustaining a healthy lifestyle. This will make it feasible for several interventions to be carried out from a variety of places, which in turn will make it possible for a partnership to be formed. Monitoring and assessment are two more important areas in which ML can be of significant assistance. ML may be used to monitor and analyse progress towards global goals, offering realtime insights into the success of interventions and allowing organizations to alter their tactics as necessary. This can be accomplished through the usage of ML. For instance, machine learning algorithms might be used to keep track of the flow of monetary resources, thereby guaranteeing that these resources are put to good use and make a positive contribution to the accomplishment of global goals related to economic development (Bachmann et al., 2022; Ferreira et al., 2020; Truby, 2020). Detecting fraudulent activity is another possible application of machine learning. It is possible to employ machine learning algorithms to detect fraud, which can help businesses mitigate the danger of incurring financial losses and move the world closer to achieving global goals connected to responsible consumption and production. For instance, machine learning algorithms can be used to identify fraudulent activity in the supply chain of goods and services. This helps to ensure that commodities come from

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reputable vendors and contributes to the accomplishment of global goals connected to environmentally responsible development (Chandan et al., 2023; Sahoo et al., 2022). These are just some of the many ways that machine learning can contribute to global cooperation to accomplish global goals. The application of ML has the potential to play an important part in the establishment of global partnerships and the acceleration of progress towards the realization of global objectives. To ensure that it does, however, a positive contribution to the global partnership for global goals must be ensured by ensuring the responsible use of ML, which includes the preservation of privacy and the security of data. It is vital to stress that this responsibility must be ensured. Machine learning, on the other hand, can completely transform the process of putting the global partnership for global goals into action and keeping track of its progress. By implementing machine learning into the global partnership for global objectives, companies and governments will be able to work more efficiently, make more well-informed decisions, and more effectively allocate resources towards the accomplishment of the goals. The intelligent application of ML has the potential to contribute to the creation of a world that is more sustainable, equitable, and affluent for everyone. Internet of Things The Internet of Things (IoT) has the potential to play a big part in the realization of global partnerships for the achievement of global goals in every region of the world. The Internet of Things has the potential to bolster and strengthen global partnerships in pursuit of global goals. The gathering and examination of data are one of these methods. Devices connected to the Internet of Things can collect huge volumes of data from a variety of sources and provide useful insights on the current state of the world as well as the progress made towards achieving global goals. For instance, smart sensors can be used to monitor and track water usage, air quality, and trash generation. These sensors can then provide data that can be utilized to improve the management of these resources and help the achievement of global goals connected to sustainable development (Bachmann et al., 2022; Mondejar et al., 2021). The other concern is the use of predictive models. The influence that a variety of activities and policies will have on global goals can be modelled and predicted with the help of IoT devices. For instance, Internet of Things (IoT)enabled smart grids can be used to forecast energy demand and make

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adjustments to the generation of energy in real time. This can help meet global goals connected to climate change by lowering greenhouse gas emissions (Mhlanga, 2023a; Yar et al., 2021). Automation constitutes the second essential component. IoT devices can automate a wide variety of mundane and time-consuming operations, which frees up resources that may be directed towards achieving global objectives. IoT-enabled smart homes, for instance, can automate the control of energy use, which helps to cut down on energy waste while also contributing to the achievement of global targets linked to energy efficiency (Kee et al., 2022; Yar et al., 2021). The Internet of Things can be utilized in monitoring and assessment, much like it can be used in machine learning. By delivering data and updates in real time, IoT devices can be of assistance in the process of monitoring and evaluating progress made towards achieving global goals. IoT can be helpful in decision support. For instance, IoT-enabled health devices can monitor and track vital signs, which can provide information on the state of global health and help accomplish global health goals (Javaid et al., 2022; Yang et al., 2022). Devices connected to the Internet of Things can provide information and recommendations to aid in decision-making, which can contribute to the accomplishment of global goals. For instance, the Internet of Things (IoT)-enabled smart cities can provide real-time traffic information, which enables decision-makers to make well-informed choices on transportation policies that are in line with the overall global goals (Xu et al., 2023). Only a few instances of how the Internet of Things (IoT) might contribute to global cooperation in achieving global goals are presented here. The Internet of Things has the potential to play a significant role in guaranteeing global partnerships and driving forward progress towards the accomplishment of global goals. However, it is essential to keep in mind that for the Internet of Things (IoT) to make a constructive contribution to the global partnership in pursuit of global goals, it must be used in a responsible manner, which involves protecting users’ privacy and the integrity of their data. The Internet of Things has the potential to completely transform the process of putting into action and monitoring global collaboration for achieving global goals. By implementing IoT into the global partnership for global goals, companies and governments will be able to function more efficiently, make decisions that are based on more accurate information, and more effectively allocate

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resources towards the accomplishment of the goals. The intelligent application of Internet of Things technology has the potential to help create a world that is more sustainable, equitable, and affluent for everyone. Block Chain Technology The use of blockchain technology has the potential to play a key part in the formation of global partnerships that are directed towards the achievement of global goals. The global partnership to achieve global goals can be supported and improved by the technology of blockchain. The technology behind blockchain could prove valuable in maintaining trust and transparency. Blockchain technology offers a distributed and unchangeable record that can be used to keep tabs on the progress being made towards achieving global objectives. For instance, solutions based on blockchain technology can be used to monitor the flow of goods and services through supply chains. This helps to ensure that products and services are produced in a manner that is both environmentally and socially responsible, as well as contributes to the accomplishment of global goals related to sustainable development (de Villiers et al., 2021; Leng et al., 2020). The use of blockchain technology is significant for ensuring the integrity of data management. Data collection, storage, and sharing are essential components of tracking and assessing progress towards achieving global goals. Blockchain technology offers a way that is both safe and accurate for performing these three functions. For instance, solutions based on blockchain technology can be used to store and distribute health data, guaranteeing that this data is both secure and accurate while also contributing to the accomplishment of global health goals (Abbas et al., 2021; Akkaoui et al., 2020). Another essential aspect of decentralized decision-making is the use of blockchains. Blockchain technology makes it possible to make decisions in a decentralized manner, which enables stakeholders from all over the world to take part in the formulation, execution, and evaluation of global objectives. For instance, voting systems that are based on blockchains might be used to allow stakeholders to take part in decision-making processes relating to global goals. This would significantly increase the level of transparency as well as accountability. The other crucial aspect of expanding access to financial services is. The technology known as blockchain has the potential to expand access to financial services, which would contribute to the accomplishment of global goals connected to the reduction of poverty

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and the promotion of economic growth. For instance, financial systems that are based on blockchain technology might be utilized to give banking services to unbanked communities. This would allow these populations to participate in the global economy and contribute to the accomplishment of global goals (Lindgren, 2019; Mhlanga, 2023b). Traceability and verification are two other important areas in which Blockchain Technology excels. The distributed ledger technology known as blockchain enables users to track and verify the origin as well as the genuineness of products and services, which contributes to the accomplishment of global goals connected to responsible consumption and production. For instance, systems based on blockchain technology might be used to track the origin of food products, thereby ensuring that these goods come from ethical and environmentally responsible suppliers and helping to the accomplishment of global goals relating to food safety. These are just a few instances of how the technology behind blockchain can contribute to the global alliance that is working towards global goals. The use of blockchain technology has the potential to play a key part in the establishment of global partnerships and the acceleration of progress towards the realization of global goals. On the other hand, it is essential to keep in mind that for blockchain technology to make a constructive contribution to the global partnership in pursuit of global goals, it must be used in a responsible manner, which involves protecting users’ privacy and the integrity of their data. Nonetheless, despite all these drawbacks, blockchain technology can bring about a paradigm shift in the manner in which worldwide cooperation for global goals is carried out and monitored. By integrating blockchain technology into the global partnership for global goals, governments and organizations will be able to increase their productivity, improve the quality of the decisions they make, and more effectively direct their resources towards the accomplishment of the goals. The intelligent application of blockchain technology has the potential to contribute to the creation of a world that is more environmentally friendly, equitable, and wealthy for everyone. Augmented Reality (AR) Augmented Reality (AR) is a technology that projects digital information onto the actual world, allowing users to interact with virtual items and information in real time. AR works by superimposing digital data onto the real world. By bringing innovative and efficient solutions to

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some of the world’s most pressing concerns, augmented reality (AR) has the potential to play a big role in guaranteeing a worldwide partnership for global goals. This can be accomplished by ensuring that global goals are met. A good illustration of this is education and training. AR may be used to enhance the learning experience by giving students access to digital resources and enabling them to study in new and inventive ways. This can be accomplished by using AR to provide students with access to digital materials. AR can be used to generate virtual simulations of complex processes, such as chemical reactions or historical events, which can help students better understand these topics. For instance, AR can be used to create these simulations. This may assist in global collaboration to achieve global goals relating to education and training. The other industry that may be affected by AR is the healthcare industry. AR has the potential to completely revolutionize the healthcare business by giving doctors and other medical professionals access to a plethora of digital information that will improve their ability to accurately diagnose and treat their patients. For instance, augmented reality can be used to develop virtual simulations of medical operations. These simulations can then be utilized by medical practitioners to practice the procedures in a setting that is both safe and under control. This has the potential to support global collaboration for the achievement of global goals relating to health and well-being. Providing individuals with access to digital resources that encourage sustainable behaviour is one of the ways that augmented reality (AR) may be utilized to support the development of communities sustainably. AR can, for instance, be used to develop virtual simulations of environmental processes, such as the water cycle or the carbon cycle, which can assist individuals in gaining a deeper comprehension of the influence that their actions have on the surrounding environment. Global cooperation for the achievement of global goals connected to sustainable development may benefit from this. AR can also be used to help emergency response activities by providing first responders with real-time information about the position of assets and personnel out in the field. This information can include the status of both the assets and the individuals. For instance, augmented reality (AR) can be used to construct virtual simulations of crisis scenarios. These simulations can then be utilized by emergency responders to practice responding to these types of situations in a secure and managed-to-set. In this way, the global collaboration for achieving global goals connected to emergency response can be supported. As if that weren’t

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enough, augmented reality may also be used to help preserve cultural history by giving people access to digital resources that foster cultural knowledge. This, as if nothing else, is a pretty big deal. For instance, augmented reality (AR) can be utilized to construct virtual simulations of historical sites and monuments. This opens the door for individuals to gain knowledge regarding these historical sites and structures in a manner that is fresh and original. This can provide support for the global partnership to achieve global goals relating to the protection of cultural assets. Just a few instances of how AR might help global cooperation in the pursuit of global goals are presented here. The use of AR gives a tremendous potential to find solutions to some of the world’s most urgent problems and to accelerate progress towards the accomplishment of global goals. However, it is essential to keep in mind that for augmented reality to make a constructive contribution to the global partnership in pursuit of global goals, it must be used in a responsible manner, which involves ensuring that it can be accessed easily and at an affordable price. In conclusion, augmented reality possesses the potential to play an important role in assuring global partnerships for the achievement of global goals. By integrating augmented reality (AR) into the global partnership for global goals, governments and organizations will be able to increase their productivity, improve the quality of the decisions they make, and more effectively direct their resources towards accomplishing the goals. The proper application of augmented reality has the potential to help bring about a world that is more sustainable, equitable, and affluent for everyone. Advanced Robotics Advanced robotics, which includes a variety of technologies such as selfdriving robots, machine learning, and artificial intelligence, can play a substantial role in ensuring a global partnership for global goals. This is because advanced robotics encompasses a wide range of technologies. The field of robotics can contribute to the resolution of some of the most urgent issues facing the world today by offering novel and efficient solutions to the challenges that are being faced by our society. Advanced robotics has the potential to make a positive contribution to the global partnership for global goals and to demonstrate how advanced robotics can be successfully applied in this setting. The application of sophisticated robotics has the potential to enhance the delivery of healthcare services

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by automating mundane chores and freeing up medical practitioners to concentrate on more difficult responsibilities. For instance, robots can be utilized to assist with surgical procedures, enabling medical practitioners to carry out procedures with a higher level of accuracy and precision. This has the potential to support global collaboration for the achievement of global goals relating to health and well-being. Once more, advanced robotics can be of service in the fight against climate change. The monitoring and administration of natural resources can be made more efficient through the application of sophisticated robotics, which can then be used to contribute to the preservation of the natural world. Robots, for instance, have the potential to be utilized in the monitoring of wildlife populations, the tracking of the spread of exotic species, and the detection and prevention of illicit logging. The worldwide collaboration for achieving global goals relating to sustainable development and the protection of life on land and in the waters may benefit from this. Manufacturing and logistics are two fields that could benefit from advanced robotics. By automating regular processes and decreasing the need for manual work, advanced robots can be utilized to increase the efficiency and productivity of manufacturing and logistical operations. These improvements can be made possible by reducing the demand for human labour. For instance, robots can be used to sort and package products, which not only reduces the risk of damage but also increases the speed at which the operation can be completed and its level of precision. This can lend support to global collaboration to achieve global goals relating to the decrease of inequality and the growth of the economy. In addition, advanced robots can improve disaster response efforts by providing first responders with real-time information about the location and condition of assets and humans in the field. For instance, robots could be employed to explore dangerous situations for people who may have survived, thereby lowering the likelihood that human responders will be hurt. This can assist in global cooperation to achieve global goals connected to emergency response and the reduction of death and morbidity caused by natural catastrophes. Last but not least, advanced robotics can be employed to help in space exploration. This is because they make it possible to explore inaccessible and dangerous places. For instance, robots may be utilized to carry out geological surveys, look for telltale signs of life, and carry out maintenance on spacecraft. This may help the global collaboration achieve its aims, which include the exploration of space and the preservation of life on land and in the water. Just

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a few instances of how sophisticated robots might contribute to global collaboration in the pursuit of global goals are presented here. The development of more advanced robotics offers tremendous potential to find solutions to some of the most urgent problems facing the world and to accelerate progress towards the accomplishment of global goals. However, it is essential to keep in mind that for advanced robotics to make a constructive contribution to the global partnership in pursuit of global goals, it must be used responsibly. This involves not only making the technology accessible but also ensuring that it is affordable. The application of highly developed robotics has the potential to play a significant role in assuring worldwide partnerships for the achievement of global goals. By introducing sophisticated robotics into the global partnership for global goals, companies and governments will be able to work more efficiently, make decisions that are based on more accurate information, and more effectively allocate resources towards accomplishing the goals. The ethical application of advanced robotics has the potential to contribute to the realization of a world that is more environmentally friendly, equitable, and wealthy for everyone.

Chapter Summary In conclusion, the power of smart technologies for a global partnership for development is undeniable. With the emergence of digital innovations, we have the potential to address some of the world’s most pressing issues, including poverty, healthcare, education, and environmental sustainability. Smart technologies can enable collaboration and knowledge sharing among individuals and organizations, regardless of geographic location. They can help us work towards achieving the United Nations’ Sustainable Development Goals, fostering more inclusive and equitable societies. However, it is important to acknowledge that not everyone has access to these technologies and that the digital divide can exacerbate existing inequalities. We must ensure that the benefits of smart technologies are equitably distributed and that everyone can benefit from them. This will require a concerted effort from governments, non-profits, and the private sector to work together and invest in infrastructure, education, and training programmes. In summary, smart technologies have the potential to revolutionize the way we tackle global challenges and work towards a more sustainable and equitable world. It is up to us to ensure that we use them to their full potential, while also addressing the

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underlying issues of inequality and access. Through global partnership and collaboration, we can create a brighter future for all.

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Javaid, M., Haleem, A., Singh, R. P., Rab, S., Haq, M. I. U., & Raina, A. (2022). Internet of Things in the global healthcare sector: Significance, applications, and barriers. International Journal of Intelligent Networks, 3, 165–175. Kee, K. K., Ting, H. Y., Lim, Y. S., Ting, J. T. W., Peter, M., Ibrahim, K., & Show, P. L. (2022). Feasibility of UTS smart home to support sustainable development goals of United Nations (UN SDGs): Water and energy conservation. Sustainability, 14(19), 12242. Leng, J., Ruan, G., Jiang, P., Xu, K., Liu, Q., Zhou, X., & Liu, C. (2020). Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey. Renewable and Sustainable Energy Reviews, 132, 110112. Lindgren, D. (2019). Space-based financial services and their potential for supporting displaced persons. In A. Froehlich (Ed.), Embedding space in African society: The United Nations Sustainable Development Goals 2030 supported by space applications (pp. 93–104). Springer. Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability, 13(11), 5788. Mhlanga, D. (2023a). Block chain technology for digital financial inclusion in the industry 4.0, towards sustainable development? Frontiers in Blockchain, 6, 1035405. https://doi.org/10.3389/fbloc.2023.1035405 Mhlanga, D. (2023b). Artificial intelligence and machine learning for energy consumption and production in emerging markets: A review. Energies, 16(2), 745. Mondejar, M. E., Avtar, R., Diaz, H. L. B., Dubey, R. K., Esteban, J., GómezMorales, A., Hallam, B., Mbungu, N. T., Okolo, C. C., Prasad, K. A., She, Q., & Garcia-Segura, S. (2021). Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet. Science of the Total Environment, 794, 148539. Rokonuzzaman, M. (2019). Smart technology partnership for sustainable development. https://thefinancialexpress.com.bd/views/smart-technology-partne rship-for-sustainable-development-1546614957 Sahoo, S., Kumar, S., Sivarajah, U., Lim, W. M., Westland, J. C., & Kumar, A. (2022). Blockchain for sustainable supply chain management: Trends and ways forward. Electronic Commerce Research, 1–56. Truby, J. (2020). Governing artificial intelligence to benefit the UN sustainable development goals. Sustainable Development, 28(4), 946–959. UNICEF. (2021). Harnessing the power of technology and digital innovation for children. https://www.unicef.org/media/100211/file/DIGITAL% 20UNICEF.pdf

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USTDA. (2021). Global partnership for climate-smart infrastructure. https:// ustda.gov/global-partnership-for-climate-smart-infrastructure/ Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11(1), 233. Wang, D., Saleh, N. B., Byro, A., Zepp, R., Sahle-Demessie, E., Luxton, T. P., Ho, K. T., Burgess, R. M., Flury, M., White, J. C., & Su, C. (2022). Nanoenabled pesticides for sustainable agriculture and global food security. Nature Nanotechnology, 17 (4), 347–360. Xu, H., Berres, A., Yoginath, S. B., Sorensen, H., Nugent, P. J., Severino, J., Tennille, S. A., Moore, A., Jones, W., & Sanyal, J. (2023). Smart mobility in the cloud: Enabling real-time situational awareness and cyber-physical control through a digital twin for traffic. IEEE Transactions on Intelligent Transportation Systems, 24(3). Yang, Y., Wang, H., Jiang, R., Guo, X., Cheng, J., & Chen, Y. (2022). A review of IoT-enabled mobile healthcare: Technologies, challenges, and future trends. IEEE Internet of Things Journal, 9(12), 9478–9502. Yar, H., Imran, A. S., Khan, Z. A., Sajjad, M., & Kastrati, Z. (2021). Towards smart home automation using IoT-enabled edge-computing paradigm. Sensors, 21(14), 4932.

CHAPTER 14

FinTech and Financial Inclusion: Application of AI to the Problem of Financial Exclusion What Are the Challenges

Introduction Inclusion in the financial system is now widely acknowledged as one of the most effective means of promoting economic development and combating poverty (Izquierdo & Tuesta, 2015; Mhlanga et al., 2020). The goal of obtaining universal financial access by the year 2020 provided support for the concept that financial inclusion can contribute to the achievement of economic growth and the alleviation of poverty. Within international institutions, politicians, central banks, financial institutions, and governments, concerns over financial inclusion are growing more pressing (Mhlanga, 2020a). It is a commonly held belief that private financial institutions, by virtue of their role in the distribution of various financial goods to the general populace, are major and crucial agents in the promotion of financial inclusion. A great number of FinTech companies are performing an essential function by helping to make digital financial resources accessible to individuals located at the base of the pyramid (Mhlanga & Denhere, 2021). Through the direct application of AI, these institutions are expanding their use of digital methods that have been there for years to improve access even for individuals who were previously served by more conventional financial institutions (Alameda, 2020; Peric, 2015). The traditional banking industry is undergoing major transformations because of the introduction of AI, which is changing both

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the paper and physical distribution of cash (Alameda, 2020; Mhlanga, 2020a). According to Biallas and O’neill (2020), the application of AI is contributing more to addressing the obstacles to the financial inclusion of low-income earners. These obstacles include the high cost of saving rural customers, assessing creditworthiness, and establishing customer identity. The application of AI is contributing more to addressing these issues because it is helping to address these issues more efficiently. When AI is successfully utilized in the financial sector, the majority of the barriers that prohibit people living in rural areas from having access to financial resources are removed. According to Biallas and O’neill’s research from 2020, fulfilling the goal of financial inclusion requires investments in infrastructure for the widespread deployment of artificial intelligence. In recent years, there has been a growth in the number of research projects that evaluate the impact that AI has had on many economic subfields. Yet, this literature is still in its infancy, particularly the study that evaluates the application of AI to minimize the problems of financial exclusion and the applications that make customers more vulnerable. According to the findings of a study that was conducted by Theodoridis and Gkikas (2019), applying AI to digital marketing is not as easy as collecting large amounts of data; rather, digital marketers need to have the necessary knowledge that will enable the technology to effectively target their customers. In their study on AI-enabled value co-creation, Paschen et al. (2020) place a strong emphasis on the shifting roles and resources that occur during the process. Yet, these research papers grossly underestimate the difficulties that come with putting AI to use in the financial industry, even though multiple studies have found that AI is extremely important for expanding access to financial services. It is yet unknown what obstacles will arise for financial institutions as a result of the deployment of AI in the process of providing financial services to persons who have been historically excluded from such services. As a result, a critical investigation of the technological and human issues connected with the adoption of AI solutions in the financial inclusion of the excluded is carried out in this chapter. This investigation is carried out utilizing a systematic review. This chapter will discuss the artificial intelligence (AI) innovation in the financial services industry, as well as the applications of AI in the financial sector, along with specific case studies. A case study of FarmDrive’s work in Kenya will be discussed, and then an empirical literature review and the methodology will be presented. Following the examination of the

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problems that are linked with the implementation of artificial intelligence (AI) solutions in the financial inclusion of the excluded, the conclusion and recommendation were presented.

Innovation in Artificial Intelligence for the Financial Sector According to Biallas and O’neill’s (2020) definition, artificial intelligence (AI) is the science and engineering that gives robots the ability to think for themselves. In other words, artificial intelligence (AI) can be defined as a collection of systems, methods, and technologies that can exhibit intelligent behaviour through the analysis of their surroundings and taking action with a degree of autonomy to achieve predetermined results. The term “artificial intelligence” (AI) refers to the collection of technology that enables machines to carry out their functions with increased levels of intelligence. Machines that are powered by AI are capable of imitating human capacities, including the ability to sense, comprehend, and act (Access Partnership, 2020). AI enables machines in certain ways to see the world in which they work, in some cases to think and learn, and to behave in a manner that is responsive to both the environment and the available conditions (Meunier, 2018). Because of the ever-increasing sophistication of AI applications, an increasing number of businesses can implement them into their workflows. According to Biallas and O’neill (2020), artificial intelligence (AI) has been existing as a field of study for around 70 years, but its application has increased in recent years. The advancement of machine learning increases in processing power advances in data storage, and the development of effective and efficient communication networks have all contributed to a significant improvement in the use of artificial intelligence. As a result of the general decline in the cost of internet access, the increase in mobile penetration, and the rise in computing power throughout the previous revolution, digital consumers and operations have been able to generate a wealth of new and real-time data through cellular telephones and other digital devices. The emergence of more advanced methods of data storage capacities and the relatability in energy supply analyzed data to be cost-effective for businesses, which are now causing financial service providers to begin the integration of AI technologies in their service offerings, are two factors that have contributed to this trend. According to a survey of 151 financial technology (FinTech) enterprises and traditional banks that

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were carried out by the World Economic Forum and the Cambridge Centre for Alternative Finance, both types of businesses have acknowledged the incorporation of AI into their daily operations (Biallas & O’neill, 2020; Meunier, 2018) Roughly 85% of those who participated in the survey mentioned that they are currently implementing AI into their business processes. Because many people in emerging markets lack the traditional identification, collateral security, and credit history that are necessary for them to be able to access financial services such as credit, and because the cost of reaching rural customers is extremely high, many people in emerging markets find that they are excluded from the mainstream formal financial market. This is because the cost of reaching rural customers is extremely high. According to Meunier (2018), AI is contributing significantly to solving the difficulties that are associated with financial inclusion by automating various activities, such as customer service and engagement, to reduce the costs associated with those operations. To put it another way, artificial intelligence makes it possible to offer low-value transactions in huge numbers, so transforming previously underserved customers into profitable markets and thereby promoting financial inclusion.

Applications of Artificial Intelligence in the Financial Market, Including Case Studies Since the way that traditional data is utilized to build credit scores, the problem of financial exclusion has become far more severe in emerging markets. “Formal identity, bank transactions, credit history, income statements, and asset value” are examples of the conventional types of data that are used in the process of generating credit ratings. Many households in developing nations may not have access to the conventional forms of identification or collateral that creditors require to provide financial services. This makes it difficult for these households to obtain credit (Mhlanga, 2020b; Mhlanga et al., 2020). AI can assist service providers in evaluating a customer’s behaviour as well as evaluating the customer’s capacity to repay the loan by using alternative data sources such as public data, satellite images, company registries, and social media data such as SMS and messenger service interaction data. These data sources are all examples of alternative data sources (Mhlanga, 2020c; Mhlanga & Dunga, 2020). The most important application of artificial intelligence in emerging markets is in the financial sector, specifically in the analysis of alternative data points

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and real-time behaviour, with the goals of “Improving credit decisions; identifying threats to financial institutions and helping meet compliance obligations, and addressing financing gaps that are faced by businesses in emerging markets.” Enhancing credit decisions (Biallas & O’neill, 2020). AI is continually being used by lenders and credit rating agencies to study potential borrowers to determine whether they are creditworthy. FarmDrive is a company that provides financial services to smallholder farmers who are unbanked or underserved while also assisting financial institutions to increase their agricultural loan portfolios cost-effectively (Biallas & O’neill, 2020; Tinsley & Agapitova, 2018). FarmDrive is an agricultural data analytics company (Biallas & O’neill, 2020; Tinsley & Agapitova, 2018). The FarmDrive application makes use of simple mobile phone technology, alternative credit scoring, and machine learning techniques to reduce the data gaps that have been preventing smallholder farmers from accessing formal financial services, which would allow them to expand their agribusinesses and increase the levels of their incomes (Biallas & O’neill, 2020; Bosire, 2017; Henze & Ulrichs, 2016). These data gaps have been preventing smallholder farmers from accessing formal financial services. The application functions in such a way that it gathers information about a single farmer by sending him or her questions and requesting responses via text messaging. The application should, by the end of the process, be able to collect the following information: the farmer’s location, the crops cultivated, the size of the farm, assets such as tractors, and farming activities. The questions asked are constructed in such a way as to do this (Biallas & O’neill, 2020). To construct a comprehensive credit profile of the farmer, the data that was just taken will now be linked with data that already exists. In addition to this, testing is done with the application to estimate the likelihood of the farmer paying back the loan. One of the things that make FarmDrive stand out from similar services is the fact that the profile of the farmer that is generated from the application will be distributed to financial institutions for credit evaluation and funding. The application is currently in the second stage, as stated by Biallas and O’neill (2020). The first stage of the application took place between December 2015 and December 2016, and it was through the application that environmental data, economic data, and social data were acquired (Biallas & O’neill, 2020; Tinsley & Agapitova, 2018). Lending institutions can make use of the credit scores that are generated by FarmDrive’s

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algorithm after the data has been pooled and put into the algorithm. It is anticipated that the environmental component of the FarmDrive algorithm will be expanded during the development stages of the software by the addition of additional data sets, such as satellite images and remote sensing data. To enhance the application’s capacity for accurate prediction, the environmental data set will also be utilized in conjunction with the crop cycle data. It was hypothesized by Biallas and O’neill (2020) that once these data sets are integrated, the application will be able to accurately forecast the seasonal yield and the impact of agricultural insurance policies. According to Biallas and O’neill’s (2020) research, this application is helping smallholder farmers gain access to formal financial services such as lending, which will allow them to make a greater contribution to economic development and enhance their standard of living.

The Implications That Artificial Intelligence Will Have on Financial Inclusion Although being in its infancy, the body of research on how AI affects financial inclusion is growing. To address the exclusion of low-income earners, small enterprises, women, and adolescents from evaluating formal financial services like loans, scholars are increasingly crediting AI. For example, Mhlanga (2020a), Duan et al. (2019), Feng et al. (2020), and Ellahham et al. (2020). According to Feng et al.’s (2020) study of AI in marketing from a bibliographic viewpoint, the term “artificial intelligence” (AI) was first used to describe computer science’s focus on simulating human learning in the middle of the twentieth century. The development of processing power, data collecting, and storage, according to Feng et al. (2020), has made AI one of the most important topics for researchers and practitioners across a range of commercial and social scientific disciplines. The evolution, difficulties, and research agenda of AI for decision-making in the big data era are discussed by Duan et al. (2019) who argued in support of the work of Feng et al. (2020), pointing out that while AI has existed for some time, the development of Big Data technology has given it greater strength and made it a more interesting topic for research across disciplines. The difficulties with applying and utilizing the impact of revived AI base systems decision-making and for information systems researchers were evaluated by Duan et al. in 2019. The study produced twelve recommendations for information systems researchers on how AI may help with their work.

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The study by Ellahham et al. (2020), which described the application of AI in a setting related to healthcare safety but unrelated to the current investigation, also concurred with the study by Duan et al. (2019). Ellahham et al. (2020), even though their study was specifically aimed at the financial industry, were supported by Mhlanga (2020a). Mhlanga (2020a) looked at how AI affects the excluded groups’ ability to access digital finance. According to Mhlanga’s (2020a) report, FinTech firms are increasingly turning to artificial intelligence (AI) solutions to ensure that the objectives of financial inclusion are met. Mhlanga (2020a) also made note of how AI is being utilized to open official financial services to underserved groups like the poor, women, and young people. It was noted that AI is vital in resolving the problem of information asymmetry, fraud detection, and providing customer service and that it has a significant impact on financial inclusion in areas linked to risk identification, measurement, and management. Ozili (2018) also investigated the problems associated with digital financing. Similar to Mhlanga (2020a), Ozili (2018) discovered that digital finance and financial inclusion have numerous advantages for financial service providers, financial users, governments, and the economy. Moreover, Ozili (2018) identified several challenges that, if fixed, might improve the efficiency of digital money for governments, consumers, and enterprises. The existing disparity between the accessibility of finance and its availability was one issue that was brought up. Among the many problems, there is the question of bias in the delivery of digital finance. The use of AI and blockchain to the advancement of financial inclusion in India was also evaluated by (Saon et al., 2019). According to Saon et al. (2019), the use of AI in finance can aid in efficiency improvements in the financial industry and enable financial service providers to provide a wider range of superior products and services, both of which can help to further financial inclusion in developing countries. According to Saon et al. (2019), applying AI might be risky if it undermines factors like trust, competition, and monetary policy. The five broad functions of the financial sector are to make and receive payments, save, borrow, manage risks, and seek guidance from all other services, according to Saon et al. (2019). Radcliffe et al. (2012), who looked into the digital approach to financial inclusion, provided support for this. The financial sector’s payments, according to Radcliffe et al. (2012), are all economic systems’ connecting threads.

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Similar to the claims made by Saon et al. (2019), Radcliffe et al. (2012) said that financial services enable consumers to send money to friends and family as well as purchase goods, power, and water. The use of digital financial services by governments to collect taxes, disperse social benefits, and even help suppliers collect payments from customers was also emphasized. According to Radcliffe et al. (2012), digital finance makes transactions more affordable and convenient. The case made was that economic activity is significantly impacted when transaction costs are large and inconvenient. According to data from China in a study on digital financial inclusion and farmers’ vulnerability to poverty, access to money is one of the most important factors in reducing poverty. Yet, financial institutions still face challenges in extending access to the poor. According to Wang and He (2020), one of the methods that can be quickly utilized to address the issue of financial exclusion and poverty is digital financial inclusion. According to Wang and He (2020), farmers in China who used digital financing were less vulnerable as a result. One of the key conclusions by Wang and He (2020) was that, in comparison to traditional banks, digital finance from information technology businesses had a bigger impact on farmers’ vulnerability. According to How et al. (2020), financial service companies should realize that applying AI to legacy data might help shape how potential clients react when they are contacted. How et al. (2020) also mentioned that implementing AI projects continues to be difficult, particularly for financial service providers that cannot programme computers. Once more, How et al. (2020) developed a non-coding, AI-based technique that is humancentric and simulates potential interactions between the financial profiles of potential clients. The study’s objectives were to forecast consumer intentions towards the financial items being offered and to show how non-computer scientists may use AI for social good. According to Kandpal and Khalaf (2020), banks find it exceedingly challenging to provide financial goods to everyone through a brick-and-mortar strategy. Yet, branchless banking is enabling banks to reach many individuals thanks to technology like AI. According to Kandpal and Khalaf (2020), AI in banking offers a financially excluded population a cost-effective option for service delivery. Financial items can now be accessed more affordably by households who have been financially excluded thanks to digital technologies. The advantages and problems related to the use of big data and AI for financial inclusion were also covered by Ozili (2021). Ozili (2021) found that

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the advantages of big data and artificial intelligence for financial services companies include increased efficiency and risk management, as well as the offering of smart financial goods and services to unbanked people. AI can make it easier for households who are financially excluded to obtain accounts and create credit scores by using alternative data.

A Discussion of the Difficulties of Implementing Artificial Intelligence (AI) Solutions for Excluding People’s Financial Inclusion Using unobtrusive research methods like conceptual and documentary analysis, the discussion on the difficulties in applying AI solutions for the financial inclusion of the excluded. According to numerous academics including Wang and He (2020), Radcliffe et al. (2012), Saon et al. (2019), Mhlanga (2020a), and Ozili (2018), the development of the economy and an improvement in human welfare depend greatly on digital engagement. Yet, academics such as Mhlanga (2020a), Ellahham et al. (2020), and Feng et al. (2020) believe that AI is essential for enhancing financial inclusion. Nonetheless, Ellahham et al. (2020) and Feng et al. (2020) acknowledge in their studies that there are certain difficulties in adopting AI applications to address the problem of financial exclusion. This section of the study will highlight some of the difficulties in using AI technologies to combat financial marginalization.

Data Protection and Online Attacks as Obstacles on the Path of Consumer Protection There are many obstacles associated with the integration of AI in the financial sector, particularly for the financial consumer. According to Biallas and O’neill (2020), as the new financial service providers are typically exempt from the consumer protection laws that occasionally apply to traditional financial institutions, the supply of digital finance and the application of AI relate to agent risks. Likewise, Biallas and O’neill (2020) emphasized that there are hazards associated with digital technology that can result in data loss and, in certain cases, loss of payment instructions owing to dropped communications. At times, using AI can lead to issues with the danger of privacy or security breach brought on by digital transmission and data storage (Biallas & O’neill, 2020). As if that weren’t

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problematic enough, Biallas and O’neill (2020) also pointed out that algorithmic bias and privacy issues arise from the integration of AI in financial services. Several groups, notably the International Committee on Credit Reporting, endorsed this (ICCR). The ICCR identified certain dangers associated with the application of AI in credit scoring algorithms. Using data without receiving full and informed consent from consumers, utilizing inaccurate data, and the possibility of prejudice and discrimination in algorithm design and decision-making are a few of the dangers (Biallas & O’neill, 2020; International Committee on Credit Reporting, 2019). The other crucial aspect that was emphasized is that consumers are now more exposed to cyber dangers. In AI models, where data is fed back into systems to improve the decision-making process, these dangers, as described by Biallas and O’neill (2020), are exacerbated. Certain technologies, including biometric technology, have been praised for their success in the identification of data subjects, leading to their widespread use in user identification. Kaspersky Lab (2019), however, asserted that there were numerous data leaks and numerous attempts to use the stolen biometric information. Customers are in significant danger from this, especially if data leaks occur. In addition to using distributed ledger technology, Bouveret (2018) also uses biometric technologies. Blockchain is the most widespread application of distributed ledger technology and has gained popularity because of its high level of security. Yet several cyberattacks on cryptocurrency exchanges revealed the technology’s weakness (Biallas & O’Neill, 2020; Bouveret, 2018; International Council on Credit Reporting, 2019, etc.). According to Bouveret (2018), since 2013, cyber fraud has affected more than 10 online exchanges, totalling over US$1.45 billion. Cloud computing technology, which enabled the transfer of data, services, and other applications to the cloud, is the other crucial tool that was utilized in the financial sector. Many credit reporting service companies adopted cloud computing services and their many applications as a result. The primary difficulty that credit reporting service providers encounter is their propensity to contract out the security operation of some infrastructure to outside parties. The issue that arises is that some of the third parties are small, unregulated businesses (International Committee on Credit Reporting, 2019; Newman, 2017). The provision of credit reporting services will be impacted by any attack on these minor service providers.

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This data demonstrates that even while AI applications are pushing many positive effects on the delivery of financial services, there are still some issues that need to be resolved to ensure that people who were previously denied access to formal financial services can now do so. As if that weren’t enough, numerous digital finance service providers use mobile applications to offer services to low-income individuals, women, young people, and small enterprises. Some credit bureaus have developed creative methods for creating platforms for consumer-driven data sharing to enable the portability of credit information (Newman, 2017). The provision of financial services may be slowed down by cyberattacks, which are a concern with the development of services via mobile applications.

Problems Arise from the Lessening of Competition and the Irresponsible Deployment of Artificial Intelligence The problem of competition is what makes the incorporation of AI in the financial inclusion of the excluded so difficult. Early adopters of AI in financial services may gain the first-mover advantage, consolidating their market position and preventing other players from entering the market, leading to a winner-take-all situation (Biallas & O’Neill, 2020; International Council on Credit Reporting, 2019). Since there will be less competition as a result, customers may have fewer options and eventually lose out on the advantages of price competition (Biallas & O’neill, 2020). On the other hand, AI applications can develop new business models that improve cost-competitiveness among technology suppliers, which can help with product pricing and make their services accessible to consumers with limited financial means (Biallas & O’neill, 2020; International Committee on Credit Reporting, 2019). Government regulation is arguably the most important tool to guarantee that there is competition and that the winner-take-all situation is avoided. The benefits of AI in financial services can be enjoyed by customers if the government monitors the industry to filter out any anti-competitive corporate activities (Oliver & Marsh, 2019). The responsible deployment of AI is the other essential element in the use of AI in the financial sector. Financial service providers should prioritize hiring personnel with the necessary expertise in comprehending AI applications, such as credit

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scoring algorithms (Oliver & Marsh, 2019). Understanding these applications critically is essential because responsible lending practices must be followed at all times to prevent artificial intelligence (AI) apps from hurting poor consumers more than they are supposed to. If the AI applications are not implemented safely by knowledgeable individuals, there is a possibility that vulnerable consumers’ debt levels would rise. The other risk is that these customers lose faith in the sector, which would defeat the purpose of employing AI to increase the financial inclusion of the excluded (Biallas & O’neill, 2020; Oliver & Marsh, 2019). In addition to the difficulties, systemic risk is a danger that should be avoided when implementing AI applications, particularly credit scoring. Once the most vulnerable clients are impacted, they can stop believing in the system, which might have an impact on the entire financial system. Adopting responsible lending policies and efficient risk management procedures can aid in avoiding vulnerable clients from becoming overly indebted (Biallas & O’neill, 2020). Additionally, these dangers associated with AI adoption necessitate that financial service providers carefully manage their operations concerning data ownership, privacy, security, and biases (Newman, 2017). To promote the adoption of AI throughout the sector, Biallas and O’neill (2020) pointed out that coordination is required between financial service providers, international organizations, industry, and government to manage data ownership, privacy and security biases, cybersecurity, as well as supervisory regulations or processes. Governments and investors must make an effort to create these environments when the conditions for the successful adoption of AI are not present to avoid the numerous risks associated with the deployment of AI (Tinsley & Agapitova, 2018). One example cited by Biallas and O’Neill (2020) was the consultative Group to Assist the Poor, which found that massive investment in open digital platforms, share market infrastructure and data, as well as support for public goods like foundational identity cards (IDs), are necessary for successful digital financial innovation (Emeana et al., 2020). It has been discovered that without these conditions, the adoption of AI will not significantly and significantly contribute to eliminating financial exclusion. There are numerous instances where the World Bank has, through the International Finance Organization (IFCdigital)’s financial services and FinTech practice, guided 150 financial services providers since 2007 to ensure that AI is adopted responsibly to achieve the twin goals of the

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World Bank Group of ending extreme poverty and boosting shared prosperity (Biallas & O’neill, 2020). One of the IFC’s clients in Myanmar, Yoma Bank, developed a scoring system to provide loans to distributors and suppliers. By using this algorithm, the bank can create a loan book that provides funding for micro, small, and medium-sized businesses by leveraging the payment of suppliers and order data (MSMEs). As of 2020, Yoma Bank’s non-performing loan ratio is less than 1% (Biallas & O’neill, 2020; Tinsley & Agapitova, 2018). Ant Finance, one of the Alibaba Group’s companies, is the second illustration of the appropriate use of AI. To analyze the creditworthiness of loan applicants, even those without collateral security, this organization used machine learning that may use online transaction data (Biallas & O’neill, 2020; Mhlanga, 2020b). Collateral security has been one of the factors that give confidence to lenders, but an over-dependence on it has led to the exclusion of many small enterprises, including some with potential, from formal financial services. Ant Financial was able to apply AI to big data and use actual payment history to assess the creditworthiness of small businesses to such an extent that it was able to bring high-performing small businesses into the customer base at a rapid pace and lower cost, which would be difficult for traditional banks. Ant Financial did this by capitalizing on the over 560 million people connected to the internet and the increasing number of small businesses operating online. Over four years, Ant Financial was able to grow its loan portfolio from US$0.5 billion to US$4 billion (Biallas & O’neill, 2020). To publish a handbook on how data analytics are used in digital financial services, including how practitioners might use data in the construction of algorithm-based credit scoring models for financial inclusion, IFC is also collaborating with the Mastercard Foundation in 2017. Guidelines on credit scoring methodologies developed by the World Bank through the ICCR include some recommendations for the use of AI in credit scoring (International Committee on Credit Reporting, 2019; Oliver & Marsh, 2019). Also, the IFC collaborated with private investors to create standards for ethical investing in digital finance. Almost 100 investors and financial service firms have backed this effort. All of these measures were taken to ensure that AI applications are used responsibly, financial institutions uphold consumer faith in digital financial services, and risky lending practices are reduced (Biallas & O’neill, 2020; Oliver & Marsh, 2019).

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The Problem of Providing Fuel for the Digital Divide, Exclusion, and Displacements A strong infrastructure is necessary for the digital economy to succeed, especially when applying AI to the finance industry. To enhance the enabling environment for the digital economy, the government and the private sector investors must make significant investments in telecommunications and energy infrastructure. Without this appropriate and ongoing assistance, there is a significant risk that financial services, even those utilizing AI technology, would remain practically and commercially unviable, widening the digital divide and increasing financial exclusion. The issue of employment relocation in emerging economies is another important risk. Job displacement is a result of automation and AI integration in the financial sector. Natural language processing, according to Biallas and O’neill (2020), can replace outsourced customer care services, a field that employs a sizable workforce in the financial and telecommunications sectors in nations like South Africa, Vietnam, and Morocco. Cuevas (2020) contends that even if AI has numerous uses and the ability to increase financial inclusion, there are still some inherent hazards that could unintentionally worsen the exclusion issue in the system. The first point made by Cuevas (2020) was that the elderly and those with disabilities may find it difficult to access financial resources once financial ecosystems are fully digitized. This is because these groups were not fully considered during the early stages of implementation. The other point was that while AI models are created independently using algorithms and databases, they may contain unconscious bias and be unable to accurately reflect the variety of demands of the unbanked population in terms of ethnicity, gender, and socioeconomic status (Cuevas, 2020; Oliver & Marsh, 2019). The other difficulties are caused by novelty hazards for the use of AI in services. Certain vulnerable financially excluded groups might not know the necessity to comprehend the goods, services, and suppliers of those goods, making them more susceptible to being taken advantage of and abused (Cuevas, 2020; Xie, 2019). The underprivileged population’s lack of financial literacy is the second layer of difficulties. When trying to extend financial services to previously underserved and excluded populations, financial illiteracy combined with little to no experience with digital tools, whether mobile or online, can pose a significant challenge to policymakers and service providers (Cuevas, 2020; Newman, 2017). All of these are actual difficulties that should

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be considered when implementing AI to lessen the effects of vulnerable populations’ financial marginalization (Cuevas, 2020). Many artificial intelligence (AI) applications are helping to automate several parts of digital finance, such as client engagement, customer care, and cost savings for financial service providers when providing personalized support to a larger range of customers. According to Juniper Research’s study, the usage of chatbot apps by banks would result in operating cost savings of US$7.3 billion by the year 2023 (Biallas & O’neill, 2020). Bank BCP in Peru is one of the instances when the chatbot is in use. IBM Watson and Bank BCP collaborated to create Arturito, a customized chatbot. Customers can use this chatbot to access 24-hour customer service, convert currencies, and make credit card payments over Facebook. Moreover, Bank Bradesco collaborated with IBM Watson to create a chatbot in Brazil that can respond to 283,000 queries about 62 goods in a month with a 95% accuracy rate (Biallas & O’neill, 2020; Oliver & Marsh, 2019). If financial services are made available to those who were previously excluded, unable to transact in their native tongue, or unable to physically contact a branch or banking representative, automation and customization have a significant potential to promote financial inclusion. Another example is MTN Cote d’Ivoire, a client of IFC, which is collaborating with tech company Juntos to include AI in its mobile wallet MoMo so that users can comprehend their financial commitments and goods. As a result, about 95% of MTN’s digital dialogue discussions are now automated. Nonetheless, Biallas and O’neill (2020) noted that there is still a need for research in emerging nations regarding the usage of chatbots and language processing to help solve the problem of consumer distrust and financial literacy hurdles to accessing financial services. The use of AI to ensure that the issues of trust and financial literacy barriers are addressed is an issue that still needs to be explored despite all the uses of AI (Biallas & O’neill, 2020; Oliver & Marsh, 2019). This is necessary to ensure that AI applications can fully address financial exclusion.

Conclusion and Policy Recommendation The purpose of this study was to examine the challenges associated with the adoption of AI solutions in improving financial inclusion for those who have been excluded from the financial system. The chapter found that while AI presents numerous opportunities for financial inclusion,

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it is also associated with several challenges. The use of alternative data sources enabled by AI has improved financial access for many households, particularly in emerging markets where traditional data sources are limited. However, the adoption of AI in financial inclusion is associated with challenges such as consumer and data protection, as well as the risk of cyberattacks. Other challenges include the potential for reduced competition, irresponsible deployment of AI, and the exacerbation of digital exclusion and displacement. In conclusion, we recommend that policymakers work in partnership with the private sector to prioritize the development of digital infrastructure as a foundational element of their economic and social development plans. By doing so, excluded individuals will have greater opportunities to participate fully in the financial system, thus addressing many of the challenges associated with inadequate infrastructure. It is essential to ensure that AI is deployed responsibly to promote financial inclusion while also addressing the potential risks associated with its adoption.

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CHAPTER 15

FinTech for Sustainable Development in Emerging Markets with Case Studies

Introduction Financial technology (FinTech) has transformed the way we conduct financial transactions and manage money. With the help of innovative technology, the traditional financial sector is being disrupted and reshaped. In recent years, the impact of FinTech has been felt in emerging markets, where it is being seen as a tool for sustainable development. FinTech has the potential to bridge the gap between traditional financial services and the unbanked population in emerging markets. With the help of digital payments and mobile banking, FinTech has enabled people to access financial services with ease, which was not possible before. This has led to a significant increase in financial inclusion and has opened new opportunities for businesses in emerging markets. Sustainable development is a key challenge facing emerging markets today. As the world moves towards achieving the Sustainable Development Goals (SDGs), there is a growing need for innovative solutions that can address the complex challenges faced by these markets. FinTech can play a significant role in achieving the SDGs, by promoting financial inclusion, improving access to credit and capital, and enhancing the efficiency of financial systems. The financial sector in modern countries plays a crucial role in supporting a wide range of financial and economic operations, making

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1_15

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it one of the most essential and valuable facilitators in the process of building the necessary socioeconomic capacity in the case of a crisis (Al Nawayseh, 2020). The financial sector has been transformed by technological developments over the past few decades, especially in the realm of information and communication technology (ICT). Because of this, companies can now offer customers services that are both cutting-edge and efficient. The most marginalized members of society and people with low incomes, especially in developing countries, have benefited greatly from the proliferation of digital banking platforms in recent decades. Several forms of financial technology (FinTech) provide consumers and the financial services sector with a more simplified, cost-effective, and riskfree manner of conducting business transactions when compared to more traditional means of acquiring finance. This is generally agreed upon as FinTech’s greatest strength in terms of what it can do for its users. Alwi (2021) claims that the widespread adoption of smartphones has allowed a wide range of FinTech services to reach previously inaccessible parts of poor countries. These services allowed a wider range of consumer products to become more flexible by allowing customers to do their regular financial transactions via their mobile devices. Financial sector innovation and the introduction of products like credit and debit cards, as well as FinTech services, are leading the world towards a cashless society in which customers can rely more on non-cash methods of making payments. This is because consumers increasingly prefer using non-cash methods of payment, contributing to a global trend towards a “cashless society.” Virtual payments are on the rise because of the proliferation of suitable technologies (Alwi, 2021). Electronic payment systems are quickly replacing more traditional ways of payment like cash. The volume of non-cash transactions increased by 12% over the previous year, reaching roughly 539 billion USD between 2016 and 2017, the largest amount in the preceding twenty years, as reported by the 2019 Global Payment Survey (Alwi, 2021). Over 32% of 2016 and 2017 transactions in emerging regions like Asia were conducted using a method other than cash. Growth in the Asia-Pacific region stayed steady at 7% during that same period, whereas it was roughly 19% in Central and Eastern Europe, the Middle East, and Africa (CEMEA). Al Nawayseh (2020) reports that investments in financial technology by banks and tech firms have been growing rapidly over the past few years. Investment in financial technology around the world reached an estimated $40 billion in 2019. According to Al Nawayseh (2020), ensuring that people with lower

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incomes have access to and benefit from these technologies remains an issue. This is happening even though funding for FinTech has increased. Especially in developing nations, the process of evaluating the benefits of new FinTech innovations against their possible drawbacks is crucial. The dilemma is exacerbated by the fact that in developing countries, those with fewer socioeconomic variables often seek out essential financial information but are often unable to find it. This only heightens the already dangerous situation. Conversely, the COVID-19 epidemic sparked a rise in demand for numerous financial technology services. For many people around the world, the digital economy and digital tools were a godsend at this time of isolation and social isolation. People were able to stay in touch with their social networks, businesses, and banks despite the pandemic thanks to the wide range of internet resources available to them. Access to services like education and healthcare, among others, was facilitated by these innovations and contributed to increased productivity. Several studies investigating various facets of the epidemic have been conducted, and all of them rely on data collected from real-world sources. Many surveys and studies were conducted after the epidemic had expanded globally (Rabbani et al., 2020) to try to understand how the pandemic has affected various areas of economies around the world. Rabbani et al. show that the COVID-19 pandemic had a more tangible impact on the real economy than the 2008 financial crisis (2020). The unemployment rate and the performance of the Gross Domestic Product (GDP) were just two of the many economic indicators that this epidemic affected. Joblessness in the United States, for instance, hit 14.8% during the pandemic, a rate that lasted for fewer than two months (Rabbani et al., 2020). Pinshi (2021) investigated the possibilities that FinTech offered individuals during the pandemic, whereas Alwi (2021) concentrated on defining the elements that affect the behaviour to implement mobile ewallets following the pandemic. The year 2021 saw the publication of both studies. Morgan (2022) analyzed the current developments in FinTech, addressing both the risks and benefits of the promotion of financial education and inclusiveness. Fu and Mishra (2021) documented the effects of the COVID-19 epidemic on the growth of digital finance and the use of FinTech, focusing on mobile programmes. Once the global financial system was disrupted by the Covid-disturbances, it was found that FinTech was one of the instruments used to help restore stability. Moreover, FinTech helped the financial sector become more robust and adapt

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to the crisis by keeping it operational even as containment measures were adhered to stop the spread of the virus. This body of empirical literature shows that many of the studies conducted lacked a genuine statistical and mathematical analysis of the economic relationships between the various variables present during the pandemic, thereby demonstrating a bias towards reviewing the situation that had been obtained. In this context, this paper examines the role of FinTech in promoting sustainable development in emerging markets, with a focus on case studies from different regions. The paper starts with an overview of the concept of sustainable development and its importance for emerging markets. It then goes on to discuss the potential of FinTech in promoting sustainable development and the challenges that need to be addressed. The chapter then presents case studies from different regions, highlighting how FinTech is being used to promote sustainable development. The case studies cover different areas such as financial inclusion, access to credit, and the efficiency of financial systems. The paper concludes with a discussion of the potential of FinTech to drive sustainable development in emerging markets, and the need for policymakers and stakeholders to support its growth.

FinTech Literature in General It is becoming obvious that the financial services industry will be significantly impacted by digitization. Puschmann (2017) cites one of the causes as the fact that the provision of financial services and goods, such as credit contracts, is wholly information dependent. Another justification offered was the fact that numerous operations, including stock trading, may be completed without the necessity of any form of direct physical contact. When the COVID-19 pandemic initially broke out in 2020, digitalization was shown to be quite helpful. Vasenska et al. (2021) noted that the pandemic had a wide-ranging impact on people, including travel restrictions and widespread depression. However, throughout this time, individuals were relieved by modern technologies. Vasenska et al. (2021) asserted that the use of FinTech has become the best course of action considering the pandemic and the rise in global financial transaction concerns. Vasenska et al.’s (2021) study continued to make the case that using different FinTech products is linked to a risk-reduction strategy, that FinTech can help save customers’ money, and that in times of crisis, FinTech financial services may be seen as more competitive than traditional banking systems.

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Vasenska et al. (2021) looked at the use of financial transactions enabled by FinTech before, during, and after the Bulgarian epidemic. The wealth of data indicating how crucial FinTech is during crises served as the impetus for our inquiry. Vasenska, et al. (2021) identified a few problems with FinTech transactions in Bulgaria and discussed them in their findings. One of these concerns is the general public’s ignorance about the many FinTech solutions that are offered in Bulgaria. By analyzing people’s behaviour and their opinions of FinTech before and during the epidemic, Abu Daqar et al. (2021) explored the role of FinTech in the prediction of how COVID-19 spread. This was accomplished by evaluating how individuals felt about FinTech before and after the outbreak. The study’s focus was on participants with prior exposure to the financial technology industry. The study’s conclusions showed that more people’s attitudes and knowledge of FinTech helped to prevent the epidemic from spreading by avoiding touch payment methods. The findings showing more people have positive attitudes towards and knowledge about FinTech have demonstrated that this is the case. Contactless payment methods, which reduce the number of persons who must come into direct physical touch with one another, are the minimum tools that are aiding in the fight against the transmission of the virus, according to Abu Daqar et al. (2021). According to Abu Daqar et al. (2021), contactless payment techniques in FinTech significantly decreased the possibility of the propagation of COVID-19, and users should embrace and use these ways to help decrease the likelihood of the virus’s further dispersal. To assess the potential advantages and disadvantages of these trends for financial inclusion and financial literacy, Morgan (2022) also examined recent developments in financial technology. Morgan (2022) came to several conclusions, one of which was the necessity of expanding national financial inclusion strategies to incorporate digital financial inclusion components that cater to underprivileged groups like “women, the poor, the poor, rural dwellers, and micro-, small, and medium-sized enterprises.” Fu and Mishra (2021) documented the impact that COVID-19 had on digital finance as well as the adoption of FinTech in a separate piece of study. The results of their investigation showed that the shutdown and the spread of the pandemic were the driving forces behind the rise in the number of people downloading finance mobile applications to use FinTech services. They used a sample that was globally representative and data from mobile applications for their investigation. Then, to comprehend the supply-side winners, Fu and

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Mishra (2021) examined the demand-side components that contribute to this effect. Findings show that traditional service providers originally witnessed a rise in the digital offerings they offered, but that over time Big Tech firms and new FinTech service providers were able to outperform them and gain the lead. To ascertain how the COVID-19 pandemic has impacted the rate of adoption of FinTech and digital finance, Fu and Mishra (2020) examined data gathered from mobile applications in 74 different nations. Fu and Mishra’s (2020) research indicates that the epidemic and lockdowns caused a surge in the daily download rate of financial mobile applications that ranged between 24 and 32% in the countries under study. Moreover, Fu and Mishra reported on the total number of daily application downloads (2020). They concluded that between 5.2 million and 6.3 million financial applications are downloaded on average every day. The results of Fu and Mishra (2020) indicate that since the start of the pandemic, there have been roughly 316 million application downloads worldwide. Likewise, Fu and Mishra (2020) emphasized that market size and demographics were the causes causing variances between the countries and that many places around the world showed gains in relative absolute and per capita terms. They also pointed out that many nations showed increases in absolute terms. Al Nawayseh (2020) also investigated the impact that FinTech played in the process of constructing resilience during the pandemic and the variables that influenced how quickly Jordanians embraced FinTech. The study’s conclusions indicate that perceived benefits and social norms are the main factors influencing people’s adoption of FinTech applications. The results also revealed that the factor mediating the relationship between the consumers’ perceptions of risk and their intentions about the adoption of financial technology apps was their level of trust. Al Nawayseh (2020), made the broad remark that suppliers of FinTech products should ensure that their products are easy to use, that they satisfy customer needs, and that they safeguard customer data to foster consumer acceptance. In a second study, Banna and Alam (2021) looked into the contribution that digital financial inclusion can make to preserving the financial stability of banks in the Association of Southeast Asian Countries (ASEAN) and the potential effects that this may have in the post-COVID-19 period. The implementation of digital financial inclusion in ASEAN nations increased bank stability and even resulted in a decrease in banks’ default risk, according to the study’s findings.

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The other finding by Banna and Alam was that, in the COVID19 era, digital finance has the potential to reduce liquidity crises and non-performing loans, which would sustain the stability of the banking sector while also increasing the financial mobility of the entire ASEAN region. According to the findings of Banna and Alam (2021), one of the important approaches to strengthening banking sector stability as well as economic and financial resilience during times of crisis may be to speed up the implementation of digital finance in ASEAN countries. In addition, Rabbani et al. (2021) assessed the contribution of the Islamic banking system to the post-COVID-19 economic recovery and the potential of FinTech to address the problems the event had caused. Rabbani et al. (2021) indicate that the pandemic provided a window of opportunity for the growth of social and open innovation. Also, according to Ravikumar (2019), FinTech financial services were impacted by the development of new technologies like the internet, AI, machine learning, big data, biometric identification, and blockchain. The Universal Payment Interface, the Instant Payment System, and Mobile Money were just a few of the different formats available for these services. Ravikumar (2019) discovered that the emergence of new technologies had an impact on FinTech financial services. Because of the speed, comfort, simplicity, and user-friendliness of the services, digital financial companies have been able to attract more customers thanks to the development of FinTech, claims Ravikumar (2019). In addition, Ravikumar (2019) emphasized how digital finance firms are far ahead in the field of digital financial inclusion, supporting SMEs, marginalized and underserved populations, and others through the development of new, quick, affordable, and high-quality digital financial services and products. Aziz and Naima (2021) also created a framework for digital financial inclusion to evaluate the discrepancy between notions of access and usage of digital technology and implicit assumptions in the discourse on financial inclusion. To ascertain whether there is a mismatch between the two, this was done. The findings of Aziz and Naima (2021) indicate that a change away from a fundamental individualistic adopter/ non-adopter binary framework and towards a supply-oriented financial infrastructure is required to address the social dynamics of financial engagement with new technologies. The social dynamics of financial engagement with new technologies are evolving at an unprecedented rate, necessitating this change. Even though digital services have helped to

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facilitate and close the gap between physical and digital access to financial services, the study’s findings indicate that these services are not being fully utilized because of a lack of basic connectivity, financial literacy, and social awareness. The same kind of investigation was done by Yue et al. in 2021 to investigate the effect that digital finance has had on households. The likelihood of families falling into debt traps has grown, according to Yue et al. (2021), even though expanding digital financial inclusion increases financial inclusion. This is due to Yue et al. (2021) discovery that the widespread adoption of digital finance promotes involvement in credit markets. Increased access to credit markets, according to Yue et al. (2021), may alter a household’s marginal propensity to consume, which could lead to an improvement in household consumption. This is true even if credit presents several difficulties. Banna et al. (2021) used data gathered from 534 financial institutions situated in 24 OIC states to study whether a higher level of FinTechbased financial inclusion causes banks to take on more risk. According to Banna et al. (2021), banks’ risk-taking behaviour is controlled by a higher level of financial inclusion based on FinTech. The study by Baber (2019), which received a fourth-place ranking, examined the performance of conventional and Islamic banking systems in various nations’ degrees of financial inclusion. Countries with Islamic banking are more inclusive than their counterparts when it comes to financial inclusion, and women there enjoy more financial liberty. On the other hand, fewer people use FinTech in countries that place a greater emphasis on conventional banking institutions. To draw lessons from the business models and national settings of these businesses that can help to include Peruvians in the financial system, Velazquez et al. (2022) evaluated the impact of a few selected FinTech companies on financial inclusion in their nations. GCASH in the Philippines, Easypaisa in Pakistan, M-PESA in Kenya, and Nubank in Brazil are a few examples of FinTechs that have entirely upended their respective countries’ established banking systems. The findings show that the provision of other financial services like savings accounts and credit cards has not been impacted by M-PESA and GCASH, which focus on basic mobile money operations like remittances and withdrawals. The fact that M-PESA and GCASH are widely used in Kenya lends credence to these findings. The fact that this was Easypaisa’s first collaboration with a microfinance institution probably played a role in the favourable effect it

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had on the measures under consideration. Although Nubank has experienced impressive growth in recent years, the company has not yet had a significant impact on Brazil’s level of financial inclusion.

Financial Technology Scope in Emerging Markets Globally, the financial industry has seen a tremendous transformation brought on by technological advancements, altered consumer behaviour, and regulatory changes made in the wake of the financial crisis. The term “financial technology industry,” or “FinTech,” refers to businesses that use technology to offer clients or other financial services companies’ financial products, services, or capabilities. When compared to those provided by established financial institutions, these goods and services are typically more creative and far less expensive. The amount of money invested globally in FinTech, and the number of startups has increased dramatically in recent years (Mhlanga, 2022c). These investments result in a flurry of new business models, including 100% online banks and insurance providers, nonbank lenders, big data credit scores, and technology company payment systems (Mhlanga, 2022d). Due to a big gap in the traditional banking and financial services infrastructure, which China fills with most global transactions, Asia-Pacific and Africa have been forerunners of mobile payments, sparking the FinTech boom in these regions. In developing economies in 2021, 18% of adults paid utility bills directly from an account, and nearly one-third of these persons did so for the first time after the COVID-19 pandemic started, according to Demirgüç-Kunt et al. (2022). Likewise, following the COVID-19 epidemic, a greater percentage of adults made digital merchant payments. For instance, 80 million adults in India made their first digital merchant payment during the epidemic, and 82% of adults in China made one in 2021, including over 100 million adults, 11% of whom did so for the first time after the pandemic’s onset. According to Demirgüç-Kunt et al. (2022), 20% of adults in developing economies outside of China made a digital merchant payment in 2021. The 8% of adults who, on average, did so for the first time after the epidemic began, or almost 40% of those who made a digital merchant payment, are included in that 20% (Demirgüç-Kunt et al., 2022). These statistics show how social isolation barriers and the epidemic both contributed to the rapid adoption of digital payments. In developing economies, where 37% of adults made digital merchant payments in 2021

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(Demirgüç-Kunt et al., 2021), the Global Findex survey included an enlarged module on digital merchant payments for the first time. The widespread usage of digital merchant payments in China, where approximately 82% of adults utilize them, greatly distorts this average. The average percentage of adults in developing economies that use digital merchant payments, excluding China, is 20%. This proportion comprises all respondents who made a purchase in-store using a debit or credit card, a mobile phone, or the internet to make a direct payment from their account, according to Demirgüç-Kunt et al. (2022). All respondents who made an online purchase and paid for it using a digital online merchant payment directly from their account are also included. Digital financial services, like mobile money, enable users to safely store money and send it over great distances swiftly and affordably. This has increased remittances, consumption, and investment (Demirgüç-Kunt et al., 2022; Mhlanga, 2022a). For instance, mobile money users in Kenya who unexpectedly lost their jobs were able to get cash from a more geographically dispersed social network of relatives and friends, preventing them from having to cut back on household spending. When very poor rural households in Bangladesh with family members who had moved to the city had access to mobile money, they received higher remittance payments, were able to spend more on food and other necessities, reduce borrowing, and were less likely to live in extreme poverty (Demirgüç-Kunt et al., 2022; Mhlanga, 2022b).

The FinTech Disruption The proliferation of financial technology is directly attributable to the disruptions that have been caused by technological advancement and innovation in a variety of fields. Some of these enablers, such as widespread mobile and internet access, large-scale data analytics, and other technological advancements such as artificial intelligence, have resulted in three key disruptions that are responsible for the explosion of the FinTech industry. These disruptions are the use of alternative data in financial services, the rise of peer-to-peer transactions, and the emergence of non-traditional players offering financial services (Fig. 15.1).

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Fig. 15.1 FinTech explosion

Use of Alternative Data Endpoints are leveraging alternative data sources to establish a credit score for clients without a banking past, particularly for the financially excluded (Demirgüç-Kunt et al., 2022; Mhlanga, 2022d), and this is allowing those without a banking history to have access to funding. Credit decisions can be influenced by a variety of criteria, including utility and other payments, telecommunications bills, psychometric analysis, and credit card transactions. Traditional banks now have the tools made possible by FinTech to provide superior service to their customers and make banking easier for regular people. Automation of these procedures has the potential to drastically cut down on the time and money spent processing applications, as well as eliminate any room for error or discrimination that may have resulted from relying on a single set of eyes. Lenders may be able to streamline some of the manual processes involved in the loan approval process by using alternative data, such as data from online bank accounts. The use of alternative data has allowed financial institutions to continue serving their clientele even in situations when traditional verification methods, such as in-person visits, would be impractical or impossible.

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The cost of lending has increased significantly as a result, which has a direct effect on the cost of borrowing for the end user.

Peer-to-Peer Transactions The concept of peer-to-peer transactions is not a new one; however, with the advent of emerging technologies, there has been a rise in acceleration to deliberately shift from traditional centralized economic models to progressively decentralized collaboration-based models, with payments being at the core of the rise in peer-to-peer(P2P) transactions (DemirgücKunt et al., 2022, Mhlanga, 2022c). Peer-to-peer (P2P) payment systems have become increasingly popular in recent years, which has led to significant changes in the payments industry. These changes have been brought about in major part by the widespread adoption of P2P systems. This revolution in financial technology has been made possible in many different ways by the rise of peer-to-peer payments made through mobile apps or USSD on mobile devices. As a result of the expansion of peer-topeer (P2P) transactions to encompass lending and insurance, several new companies with unconventional business models have surfaced. Companies that provide peer-to-peer loans online are focusing their attention on the significant portion of the population that is not being adequately served by traditional financial institutions to facilitate easier access to credit. The rise of peer-to-peer (P2P) lending can be attributed to several factors, including the ability to conduct transactions across international borders, the absence of or limited adherence to regulatory requirements, the sector’s flexibility, P2P Insurance, also known as a risk-sharing network, is becoming increasingly popular. This type of insurance involves a group of related individuals pooling their premiums together to protect themselves against risk.

Emergence of Non-traditional Players One of the most notable developments that have emerged because of FinTech’s disruptive nature is the rise in the number of non-financial companies that are providing financial services. These are new financial service providers that have entered the market. They are typically not affiliated with traditional banking institutions and offer specialized financial services to certain customer segments. Some examples of these types of providers are as follows as shown in Fig. 15.2.

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Fig. 15.2 Non-traditional players in the financial sector

Figure 15.2 above outlines the non-traditional players in the financial sector and the provision of finance.

Technology Companies Corporations in the technology sector such as PayPal, Google (through Google Wallet), Apple (via Apple Pay), Samsung (via Samsung Pay), Konga Wallet in Nigeria, and WeChat all provide e-wallet, payment, and transfer services. Technology businesses can use their enormous user bases to their advantage by promoting the adoption of their payment solution.

Mobile Network Operators In the other category are companies that operate mobile network infrastructure. For the past few years, mobile network operators have found applications for novel business models, particularly in the payments and lending spaces across developing and less-developed economies such as

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those in Africa. They offer a variety of financial services to people who do not have access to traditional banking, such as microloans and basic payment services.

Cash Networks Cash Networks are the other category. Cash networks are businesses that are neither banks nor telecommunications firms; rather, they are organizations that establish their very own agent network. These agents are retail establishments, and customers of the cash network can make transfers, deposits, and withdrawals of cash at these locations.

E-Retailers There is also the group known as e-retailers, which is comprised of businesses with the primary objective of developing an online marketplace for a variety of goods and services. These include companies such as Alibaba and Amazon, which capitalize on their vast customer databases to offer additional financial services such as e-wallets, payments, as well as lending facilities. Other services include loan facilities. These disruptions, which were made possible by the value propositions, have resulted in the formation of thousands of financial technology companies all over the world, a startup phenomenon that has never been seen before. Due to the robustness of their business models, several FinTech companies have been able to taste success, which has also enabled traditional financial institutions to explore areas of cooperation and partnership to improve reach and efficiency.

FinTech in India The Unified Payments Interface (UPI) is a system that enables multiple bank accounts to be powered by a single mobile application (of any participating bank). This system also combines several banking features, seamless fund routing, and merchant payments into a single hood, which enables the emergence of the FinTech industry in India. Throughout the past few years, Indian financial technology companies have made remarkable progress in the payments market. UPI monthly transactions reached a value of approximately $135 billion as of June 22, an astounding nine times that of credit cards (CCs), which have been around for more than

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four decades in India. Digital payments have skyrocketed, with the value of UPI monthly transactions reaching approximately $135 billion as of June 22 (Trehan et al., 2022). Since 2016, India’s share of global FinTech funding has more than doubled, resulting in a considerable increase in the amount of money being invested in the country’s financial technology sector, which currently stands at over $35 billion. More than $19 billion was invested in financial technology companies over the years 2021 and 2022, and 18 new companies achieved the status of “unicorn” (Trehan et al., 2022). The financial technology industry in India has experienced phenomenal growth up to this point, which has facilitated the widespread adoption of digital solutions across a wide variety of financial services use cases. On the other hand, we anticipate that in the future it will be subject to a higher level of regulatory and compliance scrutiny. This is evidenced by the recent notifications that were issued by the Reserve Bank of India. It is anticipated that this, in conjunction with a funding climate that is becoming more restrictive, will promote increased collaboration with the sector’s existing companies and may lead to consolidation. It is anticipated that financial technology companies will play a significant part in expanding digital adoption and promoting financial inclusion.

Paytm Case Study Vijay Shekhar Sharma launched Paytm, one of India’s most successful and well-known financial technology firms, in August 2010. In 2010, One 97 Communication in Noida, India, provided the initial funding of 2 million dollars, which allowed Paytm to get its start in business. It began as a platform for mobile recharge, but it has since grown to become India’s largest payment gateway, providing mobile payment solutions for more than 7 million merchants and customers. Paytm has also introduced the Paytm Payments Bank to the Indian market to give underserved Indians access to banking services. It has a culture that promotes innovation and a team that is committed to making Indians’ day-to-day lives easier, and both aspects contribute to the company’s success. Paytm has already affected 80 million people and is working towards the goal of becoming India’s most trusted platform for conducting business. To continue to grow an amazing workforce, the company is always on the lookout in many parts of the world for applicants with high potential. The new company provides a variety of payment options, an electronic wallet, and business stages. Paytm’s plan of action has shifted to become

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more of a commercial centre and a model for a virtual bank, even though the company started in 2010 as an energizing stage. In addition to this, it was one of the first companies to implement a cashback programme. Paytm has developed into an Indian behemoth that manages mobile payments, banking services, a commercial centre, Paytm gold, energize and charge payments, Paytm wallet, and many other services that are provided to around 100 million registered customers. Paytm is currently available in 11 different Indian languages and serves the countries of India, Canada, and Japan as its primary markets. It provides online use cases such as recharging mobile devices, making payments for service charges, booking travel and movie reservations, and scheduling event appointments. Using the use of the Paytm QR code, customers can access in-store instalments at locations such as markets, fresh foods shops, cafés, stopping, tolls, drug stores, and instructional establishments. Paytm was the first firm to begin offering its services as an online payment company in India. They launched their business just around the same time that people started getting interested in using cell phones, so their timing couldn’t have been better. Paytm is the essence of convenience because it is active around the clock and makes it easy to make payments or transfers of monies whenever and wherever they are needed. Because of this, it is becoming more acceptable among urban populations who rely on online shopping for even things that are used regularly. Paytm was a significant contributor to the ease with which Indians may enter a major financial sector. The citizens of India may access banking services more quickly and easily thanks to Paytm. People can make purchases online and in stores using Paytm, as well as transfer money and pay bills using this straightforward and user-friendly platform. Users can swiftly and conveniently make financial transactions on their mobile phones with just a few touches and no typing required. To reiterate Paytm employs cutting-edge security solutions to safeguard the personal information and financial data of its consumers. For instance, it employs fingerprint and facial recognition technology to verify the identities of users and ensure that only authorized users can access their accounts. In addition, it uses voice recognition technology to ensure that only approved users can access their data. Paytm is a financial services provider that offers a variety of products and options, such as mobile payments, wealth management, insurance, and lending services. This makes it possible for those who may not have access to the services offered by traditional banks to nevertheless benefit from financial

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services. One additional benefit that Paytm has brought to the people of India is an increase in their level of financial literacy. Paytm offers a variety of educational courses and tools to help consumers gain a better understanding of the fundamentals of personal finance and investment. People’s ability to make educated judgments about their finances and their overall financial literacy can both improve because of this. After everything is said and done, Paytm is able to gain from increasing financial inclusion. Those living in rural and underserved areas have an easier time gaining access to financial services because of Paytm’s widespread acceptance in India. This contributes to the broadening of people’s access to financial services and the raising of their overall level of living. By way of illustration, a farmer who resides in a rural area of India can use Paytm on their mobile phone to pay for goods and services, transfer money to members of their family, and invest in wealth management products. All these activities can be accomplished from the convenience of their mobile phone. In addition, a proprietor of small businesses can use Paytm to manage their finances, collect payments from consumers, and have access to loans, all of which can assist them in expanding their enterprise.

FinTech in China One of the most active industries in the world is financial technology in China. A sizable section of the population of China had access to the internet in 2020 when there were roughly one billion internet users there. In addition, China outpaced the rest of the globe by a wide margin in terms of the adoption of FinTech services. As a result, China’s climate created the perfect conditions for a thriving FinTech industry (Slotta, 2022). From 69 billion yuan in 2013 to an anticipated 1.5 trillion yuan in 2019, the revenue of China’s FinTech business has increased more than 20 times since 2013. This development could be explained by the fact that Chinese businesses have led the way in the transformation of the financial services industry. Both startups and well-established companies that offer financial services have created online versions of a variety of services, including credit, payment, and insurance services. Big data analytics and artificial intelligence are expected to have an impact on the FinTech sector, according to industry insiders. By 2021, it is anticipated that more than 987 million Chinese citizens would be making payments electronically. In comparison to all other services combined, this number

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was significantly greater. There were 170 million users of personal finance products in the same year, compared to 15 million users of alternative kinds of financing. Alibaba-owned AntGroup rose to prominence as the market leader in China’s financial technology market. With an estimated value of $200 billion in US dollars, the business was the most valuable unicorn company ever. Yue’ebao, a platform for wealth management, sesame credit, and Alipay were among the firm’s main services. It was a subsidiary of the massive e-commerce company Alibaba. Ant Group-One of the top FinTech businesses in China is Ant Group, formerly Ant Financial. As an associate of the Alibaba Group, it was established in 2014. Among the many financial services offered by Ant Group are mobile payments, investment management, creditworthiness, insurance, and blockchain services. Alipay, an online payment platform that enables users to pay bills, transfer money, and make online and in-store purchases, is its most well-known product. In China, Alipay is utilized by more than a billion people and has established itself as the industry standard for mobile payments. An important player in China’s FinTech market, Ant Group is always looking for new ways to apply technology to enhance financial services. Ant Group offers a wide range of financial services to the Chinese people through its Alipay platform, which has many advantages. Paying bills, transferring money, and making online and in-store purchases are all made easy and convenient using Alipay. Users can easily and swiftly complete financial transactions on their mobile phones with only a few touches. Security is a feature of the platformTo safeguards users’ financial and personal information, Alipay employs cutting-edge security technology. By way of illustration, it makes use of facial recognition technology to confirm users’ identities and guarantee that only authorized users can access their accounts. All of this contributes to greater financial services accessibility. Wealth management, credit scoring, insurance, and blockchain services are just a few of the financial services offered by Ant Group through Alipay. Those who might not have access to traditional banking services can now make use of financial services. Any further advantage of FinTech is a greater understanding of finances. Ant Group offers tools and instructional resources to assist people in understanding the fundamentals of investing and personal finance. People’s financial literacy may arise as a result, and they may be better able to make financial decisions. Greater financial inclusion for the Chinese people is the result of FinTech Ant Group’s work; thanks to Alipay, which is well-liked in China and makes it simpler for those living in

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remote and underserved areas to access financial services. This raises the level of living for millions of individuals and promotes financial inclusion. For instance, a Chinese farmer living in a rural location can use Alipay from the convenience of their smartphone to pay for goods and services, send money to family members, and invest in wealth management products. Also, small business owners can use Alipay to handle their money, collect payments from clients, and obtain credit, all of which can aid in business expansion.

Lufax FinTech in China China’s top online investment management platform is called Lufax. It was established in 2011 and provides investment goods including peerto-peer (P2P) loan products, insurance products, and wealth management solutions. Under the guiding idea of “Technology-Driven Wealth Management,” Lufax uses big data, AI, and blockchain technology to offer its clients top-notch financial services. Millions of users, including individual investors and small and medium-sized businesses, have been drawn to its user-friendly platform. Being a major player in China’s FinTech market, Lufax is committed to employing technology to offer individuals in that nation cutting-edge and convenient financial services. As one of China’s top platforms for online wealth management, Lufax offers its members a variety of advantages, such as accessibility to investment possibilities. People have access to a variety of investment alternatives through Lufax, including wealth management, insurance, and peer-to-peer (P2P) lending products. This enables everyone, regardless of their financial background or experience, to invest their money and increase their wealth. Lufax also provides comfort. People may simply manage and invest their money thanks to Lufax’s user-friendly platform. Users can access financial products, keep an eye on their portfolios, and make modifications as needed with only a few clicks on their mobile devices. Lower entrance barriers give the underprivileged and smallholder farmers access to banking services, which is another crucial factor. The Lufax platform makes investing more accessible to a wider audience and lowers the entry hurdles for investors by allowing consumers to contribute with as little as 1 yuan, or roughly $0.15. Lufax promotes improved financial literacy, much like Ant Group. To assist users in understanding the

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concepts of personal finance and investing, Lufax offers tools and instructional resources. This can improve financial literacy and assist people in making well-informed financial decisions. Better returns are the other crucial factor. People can earn more on their money with Lufax’s investment products than they would with conventional savings accounts or other low-risk investments thanks to the company’s favourable returns. For instance, a young professional in China who doesn’t want to deal with the trouble of creating a typical brokerage account can utilize Lufax to invest in a wealth management product that offers a high return. A small company owner can also use P2P lending products through Lufax and get financing for their enterprise without going through the conventional banking system.

The Benefits of FinTech for Sustainable Development in Emerging Markets FinTech, which stands for “financial technology,” has completely disrupted the financial services industry and is becoming an increasingly vital component of economic growth in developing countries. The following are some of the most important advantages that emerging economies will receive from financial technology in terms of ensuring sustainable development and maybe achieving the goals of sustainable development.

Increased Access to Financial Services and Financial Inclusion FinTech businesses use technology to offer financial services to unbanked or underbanked individuals, notably those living in rural and low-income areas. People now have access to financial services that they were previously unable to obtain, which helps to improve financial inclusion. Mobile money services, like M-Pesa, for instance, have made it possible for millions of individuals in Kenya to access financial services, such as mobile banking, remittances, and insurance, even in remote areas where traditional banking services are not available. Financial inclusion has been identified by Mohammed et al. (2017), Mhlanga (2020), Mhlanga (2021), and Inoue (2019) as one of the beneficial and efficient methods of combating poverty. Financial literacy is greatly aided by

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FinTech companies. FinTech businesses frequently offer tools and instructional resources to assist customers in understanding personal finance and investing concepts. Enabling people to make knowledgeable financial decisions, can assist to enhance financial literacy. Companies like Paytm, for instance, provide financial literacy tools like educational films and articles to customers in India so they may learn the fundamentals of investing and personal finance. For instance, Askar et al. (2020) claimed that given its impact on financial behaviour, financial literacy has drawn significant interest from researchers and policymakers alike as a tool that can help with resolving some of the most urgent developmental issues. Askar et al. (2020) used data from a nationally representative survey in Indonesia to examine the relationship between financial literacy and poverty, which is a downstream welfare indicator. They found that financial literacy is crucial in lowering poverty. By conceptualizing its effects on the elimination of poverty in rural households and considering both short- and long-term economic income dynamics, Xu et al. (2021) also looked into financial literacy. Financial literacy has immediate and long-term effects on increasing rural households’ status by eradicating and significantly reducing poverty, according to research by Xu et al. (2021) using data from the 2015 to 2017 China Household Finance Survey (dynamic effects). Second, according to Xu et al. (2021), education and financial literacy have a strong complementary impact on reducing poverty in rural households.

Enhanced Financial Security The ability to access financial resources to maintain an adequate standard of living is what is meant by the term “financially secure condition,” and the financial insecurity of low-income household members is a serious problem; low-income households are more sensitive to shifts in the macroeconomic environment than wealthier households are (Lee & Kim, 2016). According to Lee and Kim (2016), the intensity of financial instability in households that are at or below the poverty threshold would be greater than in otherwise comparable homes. Companies in the financial technology sector employ cutting-edge security systems to safeguard the sensitive personal and financial data of their customers. This serves to lower the risk of fraud while also increasing confidence in digital financial services. For instance, in Brazil, financial institutions such as Nubank protect their customer’s financial information and transactions with the

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help of encryption and biometric verification. Those who are risk-averse will be able to join the formal financial industry more easily when there is an increase in financial security; this will contribute to an improvement in financial inclusion. As was previously mentioned, poverty and destitution will decrease because of an increase in financial inclusion. According to Howell et al. (2013), the need theory is the most notable theory to explain the curvilinear relationship between earnings and subjective well-being (SWB). The need theory proposes that enhanced income and wealth can lead to increased well-being in economic hardship because money is used to fulfill basic physiological needs. Howell et al. (2013) found that the need theory was the most notable theory to explain the curvilinear relationship between income and SWB. According to the findings of certain studies, the subjective assessment of an individual’s level of financial stability should affect that person’s level of happiness. Alterations in one’s financial situation over time may affect one’s sense of safety and are most likely a significant factor in determining one’s level of well-being. For example, Zumbo and Michalos (2000) conducted a study to determine whether financial security was a good predictor of overall life satisfaction. They found that it was, for several different groups, including students, financial security was a good predictor of overall life satisfaction.

Increased Efficiency and Reduction in Costs FinTech businesses employ technology to simplify financial procedures, lower expenses, and boost productivity. Financial services are now quicker, less expensive, and more practical, which benefits both individuals and enterprises. For instance, businesses like Ant Group and Lufax in China employ machine learning and artificial intelligence to automate financial operations and increase efficiency, cutting down on the time and cost of financial services. The prospective effects of FinTech on the banking industry were evaluated by Wang et al. in 2021. According to Wang et al. (2021), commercial banks may experience increased profitability, financial innovation, and better risk management as a result of FinTech development. Overall, Wang et al. (2021) found that commercial banks can use financial technology to improve their traditional business model by decreasing bank operating costs, enhancing service effectiveness, bolstering risk control capabilities, and developing improved customer-oriented business models for customers; thereby improving

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overall competitiveness. According to Wang et al. (2021), the degree of these results varies depending on how much each bank uses technological innovation. In their study published in 2021, Lee et al. explored the relationship between cost-effectiveness and the technology used in China’s banking sector between 2003 and 2017. State-owned commercial banks have the lowest cost efficiency and use worse technology, according to Lee et al. (2021). According to Lee et al. (2021), who studied the impact of FinTech development, FinTech innovations not only increase the cost-effectiveness of banks but also advance the technology those banks employ. The study by Lv and Xiong (2022) explores if and how FinTech increases the investment efficiency of listed enterprises by using data from a large sample of Chinese listed companies and provincial panel data from 2011 to 2018. Corporate investment efficiency was found by Lv and Xiong to be positively correlated with the FinTech level and to be concentrated in regions with low rates of marketization and urbanization, demonstrating the inclusive character of FinTech. According to Lv and Xiong (2022), the two governance mechanisms—separate ownership and management and diversified ownership of businesses significantly strengthen the positive association. This is due to a synergistic relationship between the impact of FinTech on corporate investment efficiency and the two governance mechanisms.

Boosting the Economy FinTech companies’ delivery of these resources to businesses and people who previously lacked access to finance and financial services can aid, which in turn can spur economic growth. In addition to fostering entrepreneurship and supporting the growth of small and medium-sized businesses, this has the potential to help create jobs. For instance, FinTech firms like Gojek and Grab have contributed significantly to the growth of tens of thousands of small businesses, the creation of new jobs, and the expansion of Indonesia’s general economy. In general, the application of FinTech, which has the potential to radically disrupt the business, stands to benefit the financial services sector tremendously. Millions of individuals could benefit from FinTech because it increases access to financial services, promotes better financial literacy and understanding, boosts financial stability and productivity, and promotes economic growth.

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Chapter Summary In conclusion, FinTech has emerged as a powerful tool for promoting sustainable development in emerging markets. Through increased access to financial services, innovative investment platforms, and the use of technology to promote responsible investing, FinTech has the potential to help drive progress towards a more sustainable and inclusive global economy. The case studies presented in this paper provide compelling evidence of the impact that FinTech can have in promoting sustainable development in a variety of emerging market contexts. However, it is important to recognize that there are also challenges and risks associated with the use of FinTech, such as potential privacy concerns and the need for regulatory oversight. As such, continued investment in research and development, along with ongoing collaboration between public and private stakeholders, will be essential to maximizing the potential benefits of FinTech while minimizing its potential risks. Overall, the opportunities presented by FinTech for sustainable development in emerging markets are significant and warrant continued attention and investment in the years ahead.

References Abu Daqar, M., Constantinovits, M., Arqawi, S., & Daragmeh, A. (2021). The role of FinTech in predicting the spread of COVID-19. Banks and Bank Systems, 16(1), 1–16. Al Nawayseh, M. K. (2020). Fintech in COVID-19 and beyond: What factors are affecting customers’ choice of fintech applications? Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 153. Alwi, S. (2021). Fintech as financial inclusion: Factors affecting behavioral intention to accept mobile e-wallet during Covid-19 outbreak. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(7), 2130–2141. Askar, M. W., Ouattara, B., & Zhang, Y.-F. (2020). Financial literacy and poverty reduction: The case of Indonesia (ADBI Working Paper 1097). Asian Development Bank Institute. https://www.adb.org/publications/financial-literacypoverty-reduction-case-indonesia Aziz, A., & Naima, U. (2021). Rethinking digital financial inclusion: Evidence from Bangladesh. Technology in Society, 64, 101509. Baber, H. (2019). Financial inclusion and FinTech: A comparative study of countries following Islamic finance and conventional finance. Qualitative Research in Financial Markets, 12(1), 24–42.

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Wang, Y., Xiuping, S., & Zhang, Q. (2021). Can fintech improve the efficiency of commercial banks?—An analysis based on big data. Research in International Business and Finance, 55, 101338. Xu, S., Yang, Z., Tong, Z., & Li, Y. (2021). Knowledge changes fate: Can financial literacy advance poverty reduction in rural households. The Singapore Economic Review, 1–36. Yue, P., Korkmaz, A. G., Yin, Z., & Zhou, H. (2021). The rise of digital finance: Financial inclusion or debt trap? Finance Research Letters, 102604. Zumbo, B. D., & Michalos, A. C. (2000). Quality of life in Jasper, Alberta. Social Indicators Research, 49(2), 121–145.

CHAPTER 16

Artificial Intelligence and Machine Learning for Sustainable Development Case Studies in Emerging Markets

Introduction Artificial intelligence (AI) can significantly improve human intelligence and change how we communicate, access goods and services, gather data, and produce goods (Mhlanga, 2021a). Artificial intelligence (AI) presents an opportunity for businesses in emerging regions to slash costs and entry barriers while delivering cutting-edge business models that can outperform conventional solutions and reach the underserved. The objectives of eradicating poverty and achieving shared prosperity may depend on utilizing the power of AI as technology-based solutions become more crucial to economic progress in many countries. While basic AI technologies are already being used in emerging markets to address pressing development challenges, much more can be done, and private sector solutions will be essential to scaling new business models, creating new means of providing services, and boosting the competitiveness of local markets. Innovative methods are needed for each of these solutions to increase the potential and reduce dangers related to this new technology (Mhlanga, 2021a, 2022a). The science of having robots act logically and intelligently is known as artificial intelligence (AI), and it is quickly influencing both business practices and society, according to the International Finance Corporation (2021).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1_16

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Artificial intelligence (AI) is already being used in a wide range of areas of our life, with financial services seeing the highest penetration (Li et al., 2017; Primi & Toselli, 2020). AI applications are offering new ways to bridge infrastructure gaps and address urgent development challenges in key sectors. Most recently, AI applications have offered new solutions to the complex problems posed by the COVID-19 pandemic, where organizations around the world are looking into new ways to use big data and AI analytics to flatten the curve and protect public health. Emerging markets can benefit significantly from AI (International Finance Corporation, 2021). Traditional brick-and-mortar businesses are reorienting towards technology- and AI-based business structures and platforms to offer goods and services in fresh, frictionless ways. Although the pandemic severely damaged several economic sectors, the IT sector and its AI applications are currently enjoying the growth that provides hope for the future of emerging markets (International Finance Corporation, 2021). Modern digital innovations make it possible for emerging nations to close infrastructure gaps more rapidly and effectively than in the past when the construction of important economic sectors necessitated extensive and expensive infrastructure. Several industries are using AI in different ways. AI provides new methods to progressively boost productivity in some industries, while in others it enables nations to completely skip old development patterns, avoiding the need to create expensive infrastructure or at the very least making it considerably less capital heavy (Corea, 2019; Dhanabalan & Sathish, 2018; Morley et al., 2020). One example of how artificial intelligence is being used in the financial sector is M-Shwari, which enables consumers in rural and underserved areas to submit online loan applications (Mhlanga, 2022b; Parlasca et al., 2022). The company utilizes AI to analyze applications and forecast the likelihood of default rather than dealing with the logistical challenges of maintaining a vast network of offices and credit agents. This is where AI is playing a vital role in this organization (International Finance Corporation, 2021). This strategy is effective since by the end of 2017, according to the International Finance Corporation (2021), 21 million Kenyans have received modest loans from M-Shwari. Companies like Clinicas de Azucar in Mexico are employing AI in the healthcare industry to analyze data and improve health outcomes for thousands of diabetic patients who are at risk. The world’s first commercial drone delivery service for blood and medical supplies to hard-to-reach distant areas was introduced in Rwanda by Zipline (International Finance

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Corporation, 2021; Susar & Aquaro, 2019). Every day, more AI applications are added, providing emerging nations with fresh chances to overcome development-related obstacles (Kalyanakrishnan et al., 2018; Pedro et al., 2019). Numerous studies are being conducted using a piecemeal methodology to examine the role of AI and machine learning in various industries. For instance, Susar and Aquaro (2019) argued that AI holds the promise to be a catalyst in accelerating development and allowing developing countries to overcome some traditional obstacles. However, according to Susar and Aquaro (2019), there are drawbacks to AI, including the way it affects the workforce, the moral ramifications of certain of its uses, and the need for capacity building, which would fundamentally alter the type of education needed for the coming generation. According to Kalyanakrishnan et al. (2018), as the Artificial Intelligence (AI) revolution permeates society and permeates every aspect of daily life, India’s development and growth would undoubtedly be significantly impacted. According to Kalyanakrishnan et al. (2018), AI holds promise for India as a catalyst to expedite progress and as a means of overcoming more conventional barriers such as a lack of infrastructure and bureaucracy. However, Kalyanakrishnan et al. (2018) suggested that because there are dangers associated with investing in AI that could have a long-term impact on society, they must be assessed now. The effect of artificial intelligence (AI) on the workforce in emerging economies was also examined by Nabi (2019). According to Nabi (2019), artificial intelligence can do amazing things to improve people’s quality of life. It can also be used to make businesses smarter and faster. According to Nabi (2019), artificial intelligence is specific, has extremely significant implications for development, and has its worst, smack and illustrate it as a malicious object of taking human society, and jobs, and also dominating the world. However, AI will produce as many new jobs as those lost to robots; these new jobs will be concentrated in specific regions of the developed world, leaving the developing world out. Using case studies from emerging markets, this chapter will describe how artificial intelligence and machine learning are used for sustainable development.

General Investment in Artificial Intelligence Over the past few years, emerging economies have seen a rise in the amount of money they invest in artificial intelligence. According to the International Finance Corporation (2021), artificial intelligence start-ups

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in the United States raised $4.4 billion in 2017 from 155 investments. In comparison, Chinese AI start-ups raised $4.9 billion from 19 investments. This is likely since Chinese start-ups tend to concentrate more on mature AI applications. In addition, artificial intelligence (AI) businesses that were founded in China garnered $5 billion in venture capital funding in 2017, surpassing their counterparts in the United States (Sheikh, 2018). It was also demonstrated that investors have faith in the business strategy and market potential of Chinese entrepreneurs, as well as the technologies that Chinese start-ups have developed (Sheikh, 2018). However, it is believed that the most vibrant AI hubs worldwide are located in Silicon Valley in California, New York City, Beijing, Boston, London, and Shenzhen. These hubs benefit not only from the creation of jobs that require a high level of expertise and pay but also from the knowledge and innovation spillovers that occur in these hubs. Again, Silicon Valley is the leading centre in the world for new business start-ups, with 12,700 to 15,600 active start-ups and close to two million people employed in the technology industry. On research investments, Beijing leads the world in the volume of academic research output in artificial intelligence (AI), which comes from Tsinghua, Beihang, and Peking Universities; it has considerable involvement from industry giants, especially Baidu; and the Chinese government views AI as being of strategic importance (International Finance Corporation, 2021; Mhlanga, 2022a). The Chinese government is making a concerted effort to support artificial intelligence-related businesses and activities, with the declared objective of creating an AI sector worth $150 billion by the year 2030. (Roberts et al., 2021; Zeng, 2021). The private sector in China is also quite active in the space industry. Since it established the Institute for Deep Learning in 2013 and the Silicon Valley AI Lab the following year, the internet company Baidu has been actively pursuing an “AI first” agenda. This goal has been in effect since 2013. Under the direction of Kai-Fu Lee of Sinovation Ventures, the Beijing Frontier International AI Research Institute was formed in January 2018. It is projected that China’s GDP will reach $38 trillion by the year 2030, with $7 trillion of that coming from artificial intelligence (AI) in the form of new business creation in fields such as autonomous driving and precision medicine, as well as existing business upgrades in the form of improved efficiencies and reduced costs (Garbuio & Lin, 2019; International Finance Corporation, 2021).

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AI Sectorial Investment Analysis In recent years, there has been a meteoric rise in the application of artificial intelligence, sometimes known as AI, and there is no hint that this trend will abate any time soon (Batty, 2018; Gams et al., 2019; Topol, 2019). AI technology, often known as the science of making machines behave in ways that are rational and intelligent, has made its way into practically every aspect of our lives and every sector of the economy as a direct result of advancements in computing power and algorithmic skills (International Finance Corporation, 2021; Mhlanga, 2021b). In many different fields, including but not limited to healthcare, education, agriculture, manufacturing, e-commerce, and finance, AI has already been used to improve the efficiency of production and the delivery of services (Mhlanga, 2022c).

Artificial Intelligence in Energy The energy industry around the world is currently struggling with several issues, some of which include an increase in consumption and inefficiency, fluctuating trends in demand and supply, and a lack of critical analytics for effective management. These issues all contribute to an overall lack of progress (Ahmad et al., 2021; Mhlanga, 2023). Because it can analyze highly complex systems in real time and optimize them in ways that are not possible with conventional information technology, artificial intelligence has the potential to be a useful technology in the energy industry. This is the primary factor that contributes to AI’s potential in this industry. Because the energy grid is transitioning from constant baseload systems to intermittent renewable generation, a change that significantly raises the level of system complexity, Artificial intelligence is becoming increasingly valuable as a tool. For instance, artificial intelligence (AI) plays a key role in the energy sector in emerging markets. This is especially true in terms of improving energy generation and distribution, optimizing energy efficiency, and cutting carbon emissions. Artificial intelligence can be used in predictive maintenance. It is possible to employ AI algorithms to forecast the amount of maintenance that will be required for various types of energy infrastructure, such as wind turbines and power plants.

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This contributes to a reduction in downtime and an improvement in the overall efficiency of the energy generation process. The data from electric metres can be disaggregated using heuristics-based machine learning, which can then produce insights that lead to extra energy cost savings. Also, the sales of renewable energy as well as its deployment might be sped up with the help of AI. AI has the potential to boost energy efficiency at the grid level by lowering the number of standby reserves required for thermal base load generation. This will make it possible for the system to follow load and renewable energy sources more closely. Likewise, AI algorithms can be utilized to maximize the integration of renewable energy sources like wind and solar electricity into the grid. Other examples of such sources include geothermal power. For instance, AI algorithms may be applied to the forecasting of weather patterns and the optimization of the distribution of renewable energy sources to satisfy the demand for energy in real time. In addition to this, AI may be utilized to improve energy efficiency in buildings, industrial operations, and transportation systems. For instance, AI algorithms might be used to control the heating, cooling, and lighting systems in a building to cut down on unnecessary energy waste and increase energy efficiency. In grid management, artificial intelligence algorithms can be used to improve the distribution of energy from power plants to customers. This helps to reduce energy waste while also boosting the energy system’s reliability. AI algorithms can be used to monitor and analyse patterns of energy use, which can help find possibilities to cut carbon emissions. This is made possible by the reduction of carbon emissions. In addition, AI can provide consumers with more power by improving the disaggregation of data from electricity meters, which makes it possible to save resources by changing consumers’ behaviours. These examples illustrate the potential for artificial intelligence to improve energy efficiency and sustainability in emerging markets, which are seeing a rapid increase in energy demand and have an urgent requirement for sustainable solutions. Emerging markets have the potential to make considerable progress towards a more sustainable energy future by utilizing artificial intelligence.

Artificial Intelligence in Healthcare In the field of healthcare, artificial intelligence technology can be put to a variety of uses. These technologies are rapidly reaching their maturity and are already being employed in a variety of applications, ranging from

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assisting with diagnosis to increasing the operational workflow efficiencies of healthcare facilities. The primary objective of a good number of these apps is to perform tasks in a manner analogous to that of people but in a more timely, precise, and dependable manner. Because of this, they could be useful in settings that are short on resources and have restricted access to physicians and other health professionals. They could also be useful in situations where there is pressure to keep costs down. According to the International Finance Corporation (2021), the most prominent applications of AI in the healthcare industry include the following: AI-enhanced medical imaging and diagnostics are aimed to improve the speed and reliability of the analysis, and they can be especially advantageous in situations where there is a shortage of trained physicians and radiologists. Analyses of patient data and potential risks. The potential of artificial intelligence lies in its ability to do data analytics and machine learning on patient data, such as electronic health records, which will allow for predictive diagnosis and, ultimately, improve outcomes. AI is also utilized in the process of discovering new drugs. Deep learning approaches, which make use of convolutional neural networks, are particularly effective in determining which molecular structures could lead to the development of useful medications. Applications that are geared towards vertical systems are now being created by both inhouse research and development departments as well as by independent start-ups. These applications are anticipated to hasten the process of drug discovery. Personalized medicine, which is the targeting of medications based on an individual’s genetics and other genomic analysis, is another field that benefits from AI. It is anticipated that the use of AI in the pharmaceutical supply chain to process real-time data and make predictive recommendations will promote data-driven supply chains, boosting both the efficiency of operations and the management of costs. By freeing up the time of busy physicians and making diagnosis easier, artificial intelligence (AI) has the potential to expand people’s access to high-quality medical treatment. It is possible to provide care at a lower cost through higher productivity, which in turn enables available medical personnel to concentrate more intently on providing care to patients and interacting with other people. By improving data management and finding more effective ways to discover new drugs, it has the potential to reduce costs.

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Artificial Intelligence in Education Although the application of artificial intelligence technologies in the field of education is still in its infancy stage of development, these technologies have the potential to significantly improve the way students learn both inside and outside of the classroom. In addition, these technologies can help expand the accessibility, relevance, and efficiency of education (Alam, 2021a; Alam, 2021b). Learning content can be customized using machine learning, which provides instructors and faculty members with actionable insights based on student performance to better understand and meet the needs of students (Alam, 2022; St-Hilaire et al., 2022). Another important aspect is that AI has the potential to enhance online tutoring, assist educators in automating mundane tasks such as grading, and fill gaps in their curricula. Additionally, AI has the potential to provide students with immediate feedback, which can assist them in better understanding concepts at their own pace and with a greater degree of individualization. The incorporation of AI into educational settings has the potential to not only improve teaching and learning environments as well as the outcomes of students’ education but also has the potential to free up the time of educators and academics, enabling them to direct their attention towards students who have unique requirements, and it has the potential to better align educational programmes with the requirements of businesses and industries. In addition, it can democratize education by delivering high-quality instruction in settings that are not often associated with academic achievement. AI has the potential to provide parents with a bigger role in the education of their children by providing them with new tools and platforms. Moreover, AI has the potential to decentralize education to minimize the number of students in schools, campuses, and individual classes (Huang et al., 2020; Mou, 2019). According to the International Finance Corporation (2021), the value of artificial intelligence is not restricted to the academic world; rather, it can also be utilized to make on-the-job training programmes more effective. Applications of AI also have the potential to better prepare young people for the transition from school to the workforce utilizing specialized work readiness programmes, while also assisting working adults in remaining competitive in their respective fields through the provision of individualized opportunities for reskilling and upskilling. According to the predictions of industry experts, artificial intelligence will have a significant impact

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on standardized testing, intelligent tutoring, the creation of global classrooms, the acquisition of foreign languages, and the process of matching the demand for and supply of talent (International Finance Corporation, 2021).

Artificial Intelligence in Manufacturing The manufacturing sector is an essential component of developing economies, as it is the primary engine of economic expansion and a significant source of employment opportunities. The manufacturing industry has the potential to become even more productive, efficient, and competitive because of the emergence of artificial intelligence (AI). There are several instances when AI has proven to be effective in the manufacturing sector of developing nations. Examples include the following: Through predictive maintenance, artificial intelligence (AI) has made one of its most significant contributions to the manufacturing sector. Artificial intelligence algorithms can be used to determine when machinery in a manufacturing plant is likely to break down. This helps to cut down on unplanned downtime and improves overall efficiency. AI algorithms can discover patterns that suggest an imminent failure by evaluating sensor data from machinery and equipment. This allows maintenance workers to be alerted so that they can take preventative action before the failure occurs. AI also plays an essential role in the optimization of supply chains. The supply chain in manufacturing can be optimized with the use of AI algorithms, which can result in reduced waste and improved delivery times. For instance, AI algorithms can be used to forecast the demand for products and then alter production accordingly. This lowers the likelihood of either producing too much or not having enough stock on hand. AI algorithms can be used to automate quality control operations in manufacturing, which can reduce the chance of faults and improve product quality. This is another example of how AI can be utilized in quality control. For instance, algorithms powered by AI can be used to perform quality checks on products as they make their way down a production line, automatically identifying flawed items and removing them from the process. AI can also be utilized in the process of performance enhancement. In the manufacturing industry, artificial intelligence algorithms can be used to optimize production processes, thereby reducing waste and increasing efficiency. For instance, AI algorithms may be applied to the analysis of data gathered from machines and other pieces

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of equipment to locate areas of a process that may be enhanced, hence lowering the amount of time and resources necessary to make a product. The interaction of humans and machines is the other contribution that artificial intelligence has made. The use of AI algorithms in manufacturing has the potential to enhance the human–machine interface, hence making the manufacturing process both safer and more productive. AI systems, for instance, may be used to supply workers with knowledge and direction in real-time, so lowering the likelihood of mistakes being made by humans and raising overall productivity. These examples illustrate the potential of artificial intelligence to increase the efficiency and competitiveness of the manufacturing sector in developing economies. Emerging economies have the potential to boost their economic growth and raise their competitiveness in global markets if they adopt AI technologies.

AI in Financial Services The financial services industry is an essential part of rising countries, contributing significantly to economic growth, and granting access to financial services and products to a larger population, particularly the underprivileged, the young, and women. The financial services industry in emerging nations has the potential to undergo a revolution thanks to artificial intelligence (AI), making it more effective, accessible, and inclusive. There are numerous examples of how AI is used in emerging economies’ financial services sector to provide services like fraud detection. AI algorithms can be used to identify and stop fraud in the financial services industry, lowering the likelihood that banks, clients, and the economy would suffer financial loss. AI algorithms, for instance, can be used to examine transaction data to spot trends that point to fraud and flag them for additional examination. Financial services companies can utilize AI algorithms to enhance client service, making it more effective and available. For instance, simple actions like account inquiries and balance transfers can be automated by AI algorithms, freeing up human customer service personnel to answer more complicated queries. AI can even assist with risk management, a critical component of finance. Artificial intelligence (AI) algorithms can be used to improve risk management in the financial services industry, lowering the chance of financial loss and enhancing sector stability. AI systems, for instance, can be used to examine financial market data to spot trends that point to possible problems and flag them for more research. The financial services

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industry can automate lending and credit rating procedures to make them more effective and inclusive. AI algorithms, for instance, can be used to evaluate data from a variety of sources, including social media and credit reports, to establish credit scores for borrowers who would otherwise be shut out of the formal financial system. Investment management is a key component as well. AI algorithms can be used to improve returns and lower risk in the financial services industry by optimizing investment management. AI algorithms, for instance, can be used to evaluate market data and produce investment suggestions, assisting investors and financial advisors in making better choices. Asset management can become substantially more affordable thanks to AI, making it accessible to regular investors as well as high-net-worth individuals. These illustrations show how AI has the potential to enhance the effectiveness, usability, and stability of the financial services industry in developing nations. Emerging economies may boost the uptake of financial goods and services, promote economic expansion, and enhance the quality of life for millions of people by utilizing AI.

Artificial Intelligence in Transport There is a significant opportunity for artificial intelligence technology to help solve problems in the transportation industry, notably those relating to safety, reliability and predictability, efficiency, and environmental concerns such as pollution (Conde & Twinn, 2019; Mou, 2019). Artificial intelligence has the potential to usher in a new era of traffic management, ushering in innovative solutions to route cars more effectively, avoiding accidents, crashes, and fatalities while also assisting law enforcement. It is possible to improve the reliability of routes while also designing more efficient public transportation networks for communities. This can be accomplished by installing smarter traffic lights and other forms of transportation infrastructure. Altering the routes that trucks and motorbikes take to make intra-city deliveries can help reduce the amount of time it takes to make deliveries, while also reducing the amount of time individuals spend commuting. All these solutions have an effect on pollution since optimizing routes cuts down on the amount of fuel used and the number of emissions produced by various modes of transportation, including automobiles, trucks, and ships, among others. The topic of artificial intelligence (AI) in transportation is typically dominated by

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discussions about autonomous vehicles (AVs), although the effects of AI on transportation and logistics go far beyond AVs and even roads. It is anticipated that a wide variety of transportation modes, including railways, ships, and various delivery vehicles, would one day operate without drivers or crews. These innovations have the potential to become commercially feasible in the near to medium term. The use of AI can provide shippers with shorter delivery times, better reliability, and reduced costs when it comes to transporting products from factories to land-based distribution hubs by sea (Mou, 2019; Tang & Veelenturf, 2019). Moreover, AI can provide considerably more precise estimates of arrival times for container ships and can identify patterns as well as threats in shipping lanes and ports. Machine Learning can assist in the analysis of historical shipping data by considering aspects such as weather patterns and busy or slow shipping seasons. This type of analysis can help to identify inefficiencies, inaccuracies, and duplicates in the data. The bulk shipping industry can also benefit from the provision of digital chartering platforms thanks to AI’s assistance. This has advantages for interfaces aboard ships, including voice recognition programmes that directly control equipment and AI technologies are also used to mimic human impression and cognitive abilities such as seeing, hearing, reading, and analyzing sensor data. AI technologies are also being used to imitate human perception and cognitive skills such as seeing, hearing, reading, and interpreting sensor data.

The Role of Artificial Intelligence in Agriculture The use of artificial intelligence (AI) has quickly become an important factor in the progression of a wide range of industries, including agriculture. The implementation of AI technology can bring about significant improvements in the agricultural sector in developing economies, where most of the population is engaged in agricultural labour. These improvements can include increased productivity and efficiency, improved crop yields, and decreased waste. Precision farming is one of the most significant advantages brought about using AI in agriculture. To gather information on numerous aspects of a crop’s environment, such as the soil moisture, temperature, and nutrient levels, precision farming is a modern farming approach that makes use of technology, such as GPS mapping and remote sensing. Precision farming is also known as modern farming. Because farmers now have access to this information, they can make more

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educated decisions regarding the optimal times to plant, fertilize, and water their crops, which ultimately results in increased agricultural yields and decreased crop waste. AI systems can interpret this data and deliver real-time insights, which enables farmers to make decisions based on real-time data rather than manually observing their crops and depending on their gut instinct. The creation of new crop types is one more area where artificial intelligence is having a substantial impact in the agriculture sector. Artificial intelligence systems can select the greatest qualities to breed into new crop varieties by evaluating massive volumes of genetic data. This makes new crop types more resistant to disease, pests, and extreme weather conditions. This results in enhanced yields and improved food security, particularly in locations where the weather is difficult to predict or where access to food is restricted. The application of artificial intelligence (AI) in agriculture has expanded beyond crop breeding and precision farming to include the monitoring and management of diseases and pests. AI systems can detect the early warning signals of an outbreak by utilizing algorithms for machine learning to examine vast amounts of data. This enables farmers to take fast action to restrict the spread of the disease and prevent further outbreaks. This leads to a reduction in the amount of crop loss, which in turn improves overall food security. The application of artificial intelligence (AI) in the agricultural sector of emerging nations is extremely important for tackling the issues that farmers confront, such as a lack of resources and the inconsistency of the weather. AI algorithms can assist farmers in making better-educated decisions by giving them real-time information, which can ultimately lead to increased production, improved crop yields, and decreased waste. As artificial intelligence technology continues to grow, there is a substantial possibility that the agriculture sector in developing economies may reap additional benefits.

Case Studies Anti-Theft Technology in Brazil Ampla, a Brazilian energy business, has initiated a project known as the Anti-Theft Machine Project to give medium-voltage clients a solution to the problem of power theft. This initiative makes use of cuttingedge technology, such as artificial intelligence algorithms, to identify and prevent power theft in real time. The pervasive problem of power theft is

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one of the most significant challenges that energy providers in Brazil and other rising nations must contend with. Instances like these occur when people break the law and illegally tap into the power grid to steal electricity, which results in enormous losses for energy corporations. Ampla developed the Anti-Theft Machine as a solution to this problem. This machine makes use of artificial intelligence algorithms to examine data gathered from the power grid in real-time. It can then identify any suspect patterns of power usage that may imply theft. Medium voltage consumers, who are more susceptible to power theft due to the higher voltages employed in their systems, are the focus of the Anti-Theft Machine Project. This project is meant to protect medium-voltage customers. The machine employs AI algorithms to perform constant monitoring of the power grid, during which it looks for any irregularities in the consumption of power and locates the perpetrator of the theft. In the case that theft is identified, the device will transmit an alarm to the energy company. This will enable the company to take immediate action to prevent additional losses. The fact that the Anti-Theft Machine Project is both highly efficient and effective in identifying instances of power theft is one of the most significant advantages it offers. When compared to more conventional approaches, the machine’s artificial intelligence algorithms can process large amounts of data in real time, which results in a detection method that is both more accurate and more productive. In addition, the device has been made to be simple to operate, and it is equipped with an intuitive user interface that makes it possible for power companies to monitor their power grids swiftly and simply. The Anti-Theft Machine Project has already had a considerable impact in Brazil, where it has been widely embraced by medium-voltage clients. This adoption has allowed the project to have an immediate and significant effect. The initiative has assisted energy providers in lowering their losses and increasing the dependability of their power grids by giving a solution to the problem of electricity theft. This has had a good impact on the energy industry in Brazil, helping to boost access to electricity and improving energy security in the process.

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FarmDrive FarmDrive is a technology firm with a focus on agriculture that is headquartered in Kenya. The company is utilizing artificial intelligence (AI) to improve small-scale farmers’ access to loans. The mission of the company is to make it possible for farmers to gain access to the financial services they require so that they can expand their operations and boost their yields. The technology behind FarmDrive uses machine learning algorithms to assess enormous volumes of data, such as information on farmers’ prior transactions, crop patterns, and market pricing. This data also includes information on the market. This information is used to construct a credit scoring model that provides an accurate assessment of the creditworthiness of small-scale farmers. Small-scale farmers are frequently excluded from traditional banking services due to a lack of credit history or collateral, so an accurate assessment of their creditworthiness is essential. FarmDrive can assist farmers in gaining access to the funding they require for the purchase of inputs like seeds and fertilizer, as well as for the investment in new technologies, by delivering this information to financial institutions. As a direct consequence, this leads to higher yields, enhanced crop quality, and higher incomes for farmers. In addition to this, FarmDrive gives farmers access to crucial market information, which enables them to make more informed choices on the kinds of crops to cultivate, when to sell them, and how much to ask for them. In Kenya, where it has been implemented, FarmDrive has had a huge impact, as it has enabled thousands of small-scale farmers to gain access to financing and improve their standard of living. Also, the business has been honoured for its forward-thinking strategy, having been presented with several prizes, such as the FINCA Impact Finance Innovation Prize and the Global Impact Investing Network’s Award for Innovative Financial Services, among others. FarmDrive is a significant contributor to the growth of agriculture in Kenya. Using artificial intelligence technology, the company assists small-scale farmers in gaining access to the funding they require to expand their enterprises. FarmDrive is assisting Kenyan farmers in increasing their yields, improving their quality of life, and contributing to the general growth of the agricultural sector in the country by providing them with useful market information and enhancing their access to financial resources.

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Branch Mobile Application The branch is a mobile app-based digital lender serving customers in India, Kenya, Nigeria, and Tanzania, in addition to Mexico. Individuals living in developing economies can obtain microloans using mobile technology through the Branch Mobile application, which is a digital lender. Those who are historically denied access to formal financial services due to a lack of collateral or credit history will be the target demographic of the company’s efforts to broaden the availability of credit. The Branch Mobile programme employs algorithms that are powered by artificial intelligence (AI) to conduct an in-depth investigation into a broad variety of data, such as information regarding people’s mobile phone usage, transactional data, and other behavioural data. This information is used to construct a credit scoring model that accurately evaluates the creditworthiness of borrowers. This model enables Branch to make loans available to those who, under normal circumstances, would be unable to obtain credit. The accessibility of the Branch Mobile application is among the most important advantages it provides. Because the app is user-friendly and can be used from a smartphone or other mobile device, it enables individuals to apply for loans and manage their accounts regardless of where they are located. Borrowers can maintain a level of financial command and knowledge because of the app’s provision of real-time feedback on the status of their loans and the progress of their repayments. People in emerging nations who would not normally have access to formal financial services are being allowed to obtain credit using an application called Branch Mobile, which is having a huge influence on these markets. Because the organization makes use of AI technology, it can precisely evaluate the creditworthiness of borrowers. This, in turn, reduces the likelihood of borrowers defaulting on their loans and makes it easier for individuals to better their financial conditions.

Aadhar Housing Finance Ltd Aadhar Home Finance Ltd is a housing finance company based in India that is making use of artificial intelligence (AI) to broaden people’s access to credit in the country. The mission of the company is to make affordable housing financing available to individuals who, due to a lack of collateral or credit history, have traditionally been denied access to formal financial services. A wide variety of data, such as information on individuals’

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payment information, mobile phone usage, and other behavioural data, is analyzed by AI algorithms at Aadhar Housing Finance Ltd. The company uses this information to construct a credit scoring model that provides an accurate assessment of the creditworthiness of borrowers. This enables the company to make loans available to individuals who, in the absence of this model, would be unable to obtain credit. The capacity of Aadhar Housing Finance Ltd’s AI technology to evaluate the creditworthiness of borrowers rapidly and precisely is one of the most significant advantages offered by this technology. This allows the organization to make more educated lending decisions, which helps to lower the danger of loans going unpaid while also guaranteeing that the money is given to borrowers who can pay it back. The application of artificial intelligence (AI) technology by Aadhar Housing Finance Ltd. is helping to improve the whole loan experience for borrowers, in addition to enhancing access to loans. Borrowers can maintain financial knowledge and control thanks to the platform offered by the company, which gives real-time feedback on the status of loans and the progress made in repaying them. The application of artificial intelligence (AI) by Aadhar Housing Finance Ltd. has had a tremendous impact in India, where it has enabled thousands of individuals to gain access to cheap housing finance. The company’s forward-thinking methods have earned it several accolades, including the title of Home Financing Business of the Year at the Indian Financial Awards, which was one of the company’s many accolades. Aadhar Housing Finance Ltd. is a crucial participant in the growth of the housing finance industry in India. The company makes use of artificial intelligence to assist individuals in gaining access to the credit they require to improve their current financial situations and realize their ambitions. The company is helping to improve access to credit for people who have traditionally been excluded from formal financial services by utilizing its innovative approach and easily accessible platform. As a result, the company is contributing to the overall financial intermediation and economic development of India.

Aavas Financiers Ltd. Aavas Financiers Ltd is a non-banking financial corporation (NBFC) with its headquarters in India. The organization offers a wide variety of financial goods and services to people living in rural and semi-urban areas, with a primary emphasis on providing affordable housing finance. Aavas

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Financiers Ltd uses a variety of technologies, one of which is artificial intelligence (AI), to improve access to credit for people who have traditionally been excluded from formal financial services due to a lack of collateral or credit history. This group of people is typically considered to be ineligible for such services. The artificial intelligence algorithms used by the organization examine a variety of data, such as information on individuals’ financial transactions, mobile phone usage, and other behavioural data, to construct a credit scoring model that reliably analyzes the creditworthiness of borrowers. People living in rural and semi-urban areas have access to more cheap housing finance according to Aavas Financiers Ltd’s strategy, which is one of the most important advantages of the company’s approach. The loans offered by the organization are intended to assist individuals in the purchasing, construction, or improvement of their homes, thereby enhancing the individuals’ financial security and overall quality of life. Aavas Financiers Ltd offers a wide variety of additional financial products and services in addition to its housing financing solutions. Some of these products and services include loans for small enterprises as well as loans for personal costs. The mission of the organization is to furnish its clients with an extensive assortment of financial goods and services, to assist those clients in enhancing their economic well-being and realizing their ambitions. Aavas Financiers Ltd. has been recognized for its forward-thinking strategy, which has resulted in the company’s receipt of multiple honours, including the prize for Best Home Financing Company from the Indian Financial Awards. People living in rural and semi-urban areas have had easier access to credit because of the usage of AI technology by the company. This has contributed to the general inclusion of these areas in the financial system as well as the economic growth of these places. Aavas Financiers Ltd is a non-banking finance firm with its headquarters in India. The company is making use of artificial intelligence technologies to increase people’s access to loans in rural and semi-urban areas. The company’s primary focus is on providing affordable housing finance, and it offers a wide variety of financial products and services. As a result, the company is assisting in the enhancement of its customers’ financial well-being and is contributing to the expansion of India’s economy.

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Chapter Summary In conclusion, this paper has shown that Artificial Intelligence (AI) and Machine Learning (ML) have tremendous potential to support sustainable development in emerging markets. The case studies presented demonstrate that these technologies can provide innovative solutions to complex challenges in various sectors, including agriculture, healthcare, and energy. However, the successful deployment of AI and ML in emerging markets requires careful consideration of the local context, stakeholder engagement, and addressing potential risks and challenges. It is essential to ensure that these technologies are used in a way that benefits the broader society and not just a select few. Furthermore, capacity building and technology transfer are crucial in promoting the adoption of AI and ML in emerging markets. Ensuring that local communities are involved in the process of developing and implementing AI and ML solutions will help to promote ownership and long-term sustainability. In summary, AI and ML have the potential to revolutionize sustainable development in emerging markets, and their successful deployment will require a holistic approach that considers the unique needs and challenges of these contexts.

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CHAPTER 17

Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning

Introduction The rapid pace of technological growth and global interconnectedness has brought about substantial changes in society, the economy, and the environment. These changes are collectively referred to as megatrends. As the twenty-first-century progresses, these megatrends are expected to continue (Haluza & Jungwirth, 2023). The field of artificial intelligence has made significant strides in recent years, which has led to the development of innovative technologies such as Open AI’s ChatGPT. The ChatGPT language model is cutting-edge technology that has the potential to bring about a sea change in the field of education. As the implementation of ChatGPT in educational settings becomes more widespread, it must be done so following principles of responsibility and ethics. To this day, ChatGPT is the most advanced chatbot that has ever been created. In contrast to previous chatbots, it is capable of producing outstanding text within a matter of seconds, and it has generated much buzz and doomsday predictions regarding student assessment in higher education as well as a variety of other issues (Rudolph et al., 2023). ChatGPT is a cutting-edge language model that is a modification of OpenAI’s Generative Pretrained Transformer (GPT) language model. Its purpose is to generate text that is indistinguishable from content that was

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authored by people. It can hold conversations with users in a way that is deceptively simple and easy to understand. Throughout the development of educational technology, many different technological advancements have been envisioned as the end of traditional education in its current form. This has frequently been the result of a euphoric and rather illogical love of technology (Rudolph et al., 2023). Since the beginning of the twentieth century, many forms of media such as film, radio, television, computers, the Internet, mobile technologies, social media, and virtual, augmented, mixed, and extended reality have been hailed as having the potential to revolutionize the educational system. Nevertheless, throughout the development of educational technology, there was frequently insufficient consideration given to how instructors deployed such resources and how students interacted with them. Even though machines have significantly altered many aspects of daily life in the twentieth century, a visitor from the nineteenth century would feel quite at home in a modern classroom. This statement is still relevant because the traditional learning environment in physical classrooms has remained fundamentally unchanged (Ferster, 2014). Students continue to place a high value on credentials, which educational institutions have a monopoly on. These credentials include how students are taught as well as research and other forms of educational activity. Although there is a long history of viewing technology as a panacea, hopes for radical innovation in education are often exaggerated. This is because it turned out that credentials continue to be highly valued by students. Having said that, the fact that ChatGPT can comprehend and react to human language has made it an extremely useful tool for teachers, students, and other types of learners. However, because ChatGPT is becoming more widely used in educational settings, its application needs to be governed by principles of responsibility and ethics. Responsible and ethical use of artificial intelligence in education goes beyond ensuring technical accuracy; it involves considering the potential social and ethical implications of its implementation, such as concerns about personal privacy and bias, as well as the role that AI will play in forming the educational landscape of the future. The ethical and responsible use of ChatGPT in educational settings is a complicated and multidimensional issue that calls for an approach that is nuanced and interdisciplinary. The necessity for the responsible and ethical use of artificial intelligence in education has been brought to light by recent research. The studies that have been conducted on this topic

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have focused on topics such as privacy, bias, and the potential for AI to contribute to the digital divide (Borenstein & Howard, 2021; Garrett et al., 2020; Holmes et al., 2021; Nguyen et al., 2022). Other academics have also stressed the significance of considering the role that AI will play in determining the course of the future of education, as well as the requirement for multidisciplinary approaches to the ethical and responsible application of AI in educational settings (Carvalho et al., 2022; Hoppe et al., 2003; Paulus & Langford, 2022). The previous research will serve as a foundation for the current investigation, which will expand upon previous findings to focus on the ethical and responsible application of ChatGPT in educational settings. This study is an investigation of the ethical and responsible use of ChatGPT in educational settings, with a particular emphasis on the facilitation of lifelong learning. In this session, we will talk about the different ways that ChatGPT can be used in education, as well as the opportunities and difficulties that are presented by using it. The purpose of this article is to provide a comprehensive review of the responsible and ethical use of ChatGPT in education, as well as to promote more research and discussion on this vital topic.

A Brief History of OpenAI and ChatGTP OpenAI is a research laboratory for artificial intelligence that was founded in San Francisco to promote and create “friendly AI” for the benefit of humans. When the company was first launched, prominent figures in the technology industry like as Elon Musk, Reid Hoffman, Peter Thiel, Greg Brockman, and Sam Altman were among those responsible for launching it. While Altman is the Chief Executive Officer of OpenAI now, Brockman acts as the company’s President. It is stated in the mission statement for OpenAI that the organization’s ultimate goal is to create “artificial general intelligence.” Artificial intelligence has the potential to significantly improve many different industries, and the founders of OpenAI believe that it should be developed safely and beneficially, with opensource software and advanced AI tools being available without restrictions based on intellectual property. In addition, they believe that artificial intelligence should be developed in a manner that will allow it to significantly improve many different industries. OpenAI became a for-profit company in 2019, after having previously operated as a nonprofit organization. As a part of this move, Microsoft made an investment of one billion dollars

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in OpenAI, and OpenAI licenced its most recent language model, GPT3, solely to Microsoft. Both of these events took place in 2018. Since that time, Microsoft has increased its prior investment in OpenAI by an additional $2 billion, and the corporation is currently in discussions to increase that investment by an additional $10 billion. The shift in OpenAI’s approach to finance has cast questions on the organization’s commitment to democratizing artificial intelligence as well as its openness and transparency throughout the process. The most cutting-edge and potent language model that has ever been produced is GPT-3, which was made available to the public in the year 2020. It does this by employing a technique known as deep learning to produce text.

ChatGPT the Meaning The ChatGPT language model was developed by OpenAI, which is widely recognized as one of the most influential organizations in the field of artificial intelligence research. The architecture known as GPT (Generative Pretrained Transformer), which was initially introduced by OpenAI in 2018, serves as the basis for this system. The first version of the GPT model was trained on a vast amount of text data obtained from the internet by utilizing a deep learning technique known as transformers. This training took place on data acquired from the internet. It was able to generate text that was almost indistinguishable from writing done by humans because of this. OpenAI decided to create GPT-2, which is a substantially improved and more resilient iteration of the GPT model, because of the tremendous success that the first version of the GPT model experienced. Despite this, OpenAI has decided not to make the full version of GPT-2 available to the public because of worries surrounding the model’s potential for inappropriate use. In the year 2020, OpenAI made available the GPT-3 language model for the public to use. It is the most advanced language model that has ever been created and can perform a wide variety of tasks that involve the processing of natural languages. Some examples of these tasks include the translation of languages, the summarization of information, the answering of questions, and the generation of text. The ChatGPT test is a variant of the GPT-3 protocol that has been optimized for conversational tasks such as providing replies to questions presented in natural language. This optimization was accomplished by taking the GPT-3 exam and modifying it specifically for conversational tasks. As a result of this, it is of

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great use in the development of chatbots and other artificial intelligence applications that involve dialogue (Rudolph et al., 2023). The implementation of ChatGPT in educational settings carries with it the potential to significantly improve students’ educational experiences; nevertheless, the technology must be utilized responsibly and ethically. This involves making sure that students continue to develop their ability to think critically and find solutions to problems, as well as taking steps to reduce any existing prejudices and forms of discrimination. The responsible and ethical use of ChatGPT towards lifelong learning is an essential component of the larger discourse on AI ethics. To guarantee that the technology is used in ways that promote beneficial results for students and society, continual research and monitoring are required.

Empirical Literature Review The Open AI in Education literature review centres its attention on the ethical and appropriate usage of ChatGPT towards the pursuit of lifelong learning. ChatGPT is a cutting-edge language model that was developed by OpenAI. It has been making waves in the world of artificial intelligence (AI) as well as natural language processing (NLP). However, because it is being used more frequently in a wide variety of applications, one of which being education, there is a growing worry regarding the ethical and responsible use of technology of this kind. ChatGPT can completely transform the method by which students acquire knowledge and access information within the realm of education. Students can benefit from learning opportunities that are both tailored to their interests and intellectually stimulating thanks to the capability of the model to produce responses that mimic those of humans. Learning may also be made more accessible to students all around the world because of ChatGPT’s capability of handling several languages as well as a wide variety of subject matters. However, the implementation of ChatGPT in educational settings gives rise to questions about the user’s ethical and responsible behaviour. Because of its sophisticated artificial intelligence capabilities, ChatGPT runs the potential of reinforcing preexisting prejudices and forms of discrimination, which could result in learning experiences that are unequal and unfair. In addition, the implementation of such technology in educational settings may cause students to become less adept at critical thinking since they may come to rely excessively on the responses that are generated by AI rather than coming up with their ideas.

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Rudolph et al. (2023) argued that ChatGPT is the world’s most advanced chatbot so far because, unlike other chatbots, it can create impressive prose within seconds, and it has created much hype and doomsday predictions when it comes to the assessment of students in higher education as well as a variety of other topics. ChatGPT is a stateof-the-art language model, according to Rudolph et al. (2023). It is a variant of OpenAI’s Generative Pretrained Transformer (GPT) language model, which was developed to generate writing that is indistinguishable from text written by humans. Rudolph et al. (2023) feel that ChatGPT is capable of engaging in discussion with users in a way that gives the impression of being natural and straightforward. Rudolph et al. (2023) provided a condensed summary of OpenAI’s history, which is the organization that developed ChatGPT. We bring attention to the fundamental shift from a model of a not-for-profit organization to one of a commercial enterprise. According to Rudolph et al. (2023), our review is one of the first academic journal publications to be peer-reviewed and investigate the significance of ChatGPT for higher education, particularly in the areas of evaluation, learning, and teaching. Rudolph et al. (2023), focus on the technology’s implications for higher education and discuss what the future holds for learning, teaching, and assessment in higher education in the context of artificial intelligence chatbots such as ChatGPT. Rudolph et al. (2023) provided a description of ChatGPT’s functionality as well as a summary of its strengths and limitations. Rudolph et al. (2023) examine student-facing, teacher-facing, and system-facing applications, as well as potential dangers in the field of artificial intelligence in education (AIEd). They place ChatGPT into the context of current research on artificial intelligence in education (AIEd). In the final section of this paper, Rudolph et al. (2023), offer some recommendations for students, professors, and institutions of higher education. Thurzo et al. (2023) presented an up-to-date summary of the impending changes and a brief study of the influential breakthroughs in the application of AI in dentistry education since the year 2020. This research was published in the journal Dental Education. In addition, Thurzo et al. (2023) presented a manual for an updated dental curriculum that may be used for both undergraduate and postgraduate education. This manual was written in the context of developments in AI applications and the influence these developments have had on dentistry. It shouldn’t come as a surprise that the majority of dental educators have limited knowledge and skills to evaluate AI applications because they

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were not taught to do so, as stated by Thurzo et al. (2023). Additionally, Thurzo et al. (2023) pointed out that recent years have seen exponential growth in the development of AI technologies. According to Thurzo and colleagues (2023), factual reliability and opportunities with OpenAI Inc.‘s ChatGPT are regarded as crucial turning moments in the era of generative artificial intelligence (AI). According to Thurzo et al. (2023), when advanced deep learning algorithms take over the clinical fields of dentistry and redefine diagnosis, treatment planning, management, and telemedicine screening, it is inevitable that dental institution curricula will need to be updated. According to Thurzo et al. (2023), recent advancements in AI language models will cause a shift in how dentists communicate with their patients. As a result, the fundamentals of dental education, such as the writing of essays, theses, or scientific papers, would need to be modified. On the other hand, Thurzo et al. (2023) suggest that there is a rising worry about its ethical and legal consequences and that greater consensus is required for the safe and responsible deployment of AI in dental education. Pfeffer et al. (no date) argued that large language models represent a significant advancement in the field of artificial intelligence, with the underlying technology as the key to further innovations. Large language models are here to stay, despite critical views and even bans within communities and regions. Pfeffer et al. (no date) addressed the possible benefits and problems of educational uses of large language models, from the perspectives of both students and teachers. The authors focused on the benefits of using large language models. Pfeffer and colleagues (no date given) talked about the current state of massive language models and the uses that they have. After that, we focus on how these models might be utilized to produce educational content, enhance student engagement and interaction, and tailor learning experiences. Pfeffer et al. (no date) argue that large language models in education demand both educators and students to create sets of areas of expertise and information literacy necessary to comprehend the technology as well as the constraints and unexpected brittleness of such systems. This is about the challenges that are presented. In addition, Pfeffer and colleagues (no date) argued that a clear strategy within school institutions and a clear teaching approach with a strong concentration on critical thinking and strategies for factchecking are required to integrate and make full use of large language models in classroom styles and teaching curricula. This is necessary to take full advantage of the benefits that large language models offer. Pfeffer

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et al. (no date) also presented other obstacles, some of which are not exclusive to the application of AI in education. These challenges include the potential for bias in the output, the requirement of continual human monitoring, and the potential for misuse. Pfeffer et al. (no date) feel that if these difficulties are handled wisely, they can offer insights and possibilities in education settings to acquaint students with potential social biases, criticalities, and risks of AI application early on in their academic careers. According to Qadir (2022), engineering education is perpetually changing to keep pace with the most recent advances in technology and to adapt to the shifting requirements of the engineering industry. According to Qadir (2022), the application of generative artificial intelligence technologies in this industry, such as the ChatGPT conversational agent, is one of the exciting developments that has recently taken place. Students can receive individualized feedback and explanations with ChatGPT, which also enables the creation of realistic virtual simulations for hands-on learning opportunities. This has the potential to make students’ educational experiences more productive and personalized. Qadir (2022), on the other hand, believes that it is necessary to take into account the constraints imposed by this technology. According to Qadir (2022), ChatGPT and other generative AI systems are only as good as the data that they use to train themselves, and they may perpetuate biases or even generate and propagate false information. In addition, Qadir (2022) argued that the use of generative AI in education raises ethical concerns. These concerns include the possibility that students will use the technology in an unethical or dishonest manner, as well as the possibility that humans will become unemployed as a result of technology rendering their jobs obsolete. In conclusion, Qadir (2022) believes that engineering educators must comprehend the implications of this innovation and study how to make adjustments to the engineering education ecosystem to ensure that the next generation of engineers will be able to realize the benefits offered by generative AI while minimizing any negative consequences. Qadir’s research was published in the journal Engineering Education and Research in 2022. Zhuo et al. (2023) claimed that recent advances in natural language processing have made it possible to synthesize and comprehend coherent text in an open-ended manner. This has allowed the theoretical techniques to be translated into practical implementations. According to Zhuo et al. (2023), the huge language model has had a substantial impact on a variety

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of enterprises, including those that develop a report summarizing software and copywriters. Large language models may exhibit social prejudice and toxicity, as suggested by the findings of Zhuo et al. (2023), which poses ethical and societal hazards in the form of consequences deriving from irresponsibility. As a result, large-scale standards for accountable big language models must be constructed following the recommendations made by Zhuo et al. (2023). Zhuo et al. (2023) further educate future efforts on responsibly constructing ethical large language models, we perform a qualitative research method on OpenAI’s ChatGPT to better understand the practical features of ethical dangers in recent large language models. Zhuo et al. (2023) further educate future efforts on constructing ethical large language models. The potential of artificial intelligence (AI) to address social megatrends was also investigated by Haluza and Jungwirth (2023), with a particular emphasis on OpenAI’s Generative Pre-Trained Transformer 3 as the subject of their research (GPT-3). To accomplish this, Haluza and Jungwirth (2023) utilized GPT-3 to investigate the potential benefits of AI in the context of digitization, urbanization, globalization, climate change, automation and mobility, global health challenges, and an ageing population. Within the scope of this study, Haluza and Jungwirth (2023) considered not only the topic of sustainability but also that of rising markets. The only way to interact with GPT-3 was through a series of predetermined questions, and the responses it generated were examined. According to the findings presented by Haluza and Jungwirth (2023), AI has the potential to considerably enhance our comprehension of these tendencies by illuminating how their manifestations evolve and suggesting potential responses to the challenges they provide. Further, Haluza and Jungwirth (2023) claimed that more research is required to assess how effective AI will be in successfully tackling these challenges; nonetheless, the first findings are encouraging. According to Haluza and Jungwirth (2023), additional research needs to be conducted to determine how to make the most effective use of emerging technologies such as GPT-3 when addressing these difficulties. Finally, Haluza and Jungwirth (2023) concluded that while there is still a great deal of work to be done before any tangible effects can be seen from utilizing AI tools such as GPT-3 on societal megatrends, early indications suggest that it may have a positive impact if used correctly. This is although there is still a great deal of work left to be done before any such effects can be seen.

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Use of ChatGPT for Education: Challenges It is one of the key reasons why the use of ChatGPT for grading written tasks is discouraged because of the potential threat that it poses to more conventional methods of assessing written work, such as essays. Some educators are afraid that students will outsource their work to ChatGPT because the platform can rapidly generate acceptable text. This makes it more difficult to identify instances of plagiarism, which causes some educators to be concerned (Rudolph et al., 2023). However, this may be due to a reluctance to alter the methods that are used to evaluate the learning of students. Written assignments are commonly criticized for being uninteresting and ineffective in determining students’ levels of knowledge and skills; however, this may be due to a reluctance to modify the methods that are used to evaluate the learning of students. Another issue that causes some cause for concern is the fact that ChatGPT, which is effectively just a text-generating machine, does not grasp the information it generates, nor does it judge whether it is accurate or relevant. It is feasible that this will result in regulations that ban its utilization; nevertheless, it is also conceivable that ChatGPT technology will become ubiquitous before institutions have the time to alter their policies. An approach that focuses on correcting the issues that have been caused by ChatGPT while also considering the potential benefits and drawbacks of the platform would be more effective. When an educational piece of technology that has the potential to revolutionize the field is made available to the public, it is the responsibility of educators and policymakers to address any problems that may arise as a result of its implementation and to devise strategies to do away with educational practices that are inefficient. The instance of a Chinese schoolgirl who used a machine to copy enormous amounts of text serves as an illustration of how important it is to develop a responsible approach when making use of technology in educational settings.

Use of ChatGPT for Education: Opportunities The fact that ChatGPT is capable of writing essays paves the opportunity for fresh and innovative methods to be used in educational settings. Many specialists in the field, such as McMurtrie (2022) and Sharples (2022), believe that artificial intelligence (AI) technologies, such as ChatGPT, will soon be a vital component of education, and they propose making

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use of technology to enhance learning. One way that assessment procedures can be improved is by giving teachers the tools they need to use testing both as a tool for learning and as a means of learning itself. In addition, ChatGPT can be leveraged to build teaching approaches, boost student participation and teamwork, and promote hands-on, experiential learning. Even though ChatGPT is a technology that can be considered disruptive, it presents a tremendous opportunity to modernize the educational system. In a word, the implementation of ChatGPT in the context of an educational institution brings with it a wide range of opportunities as well as challenges for teachers. The capability of ChatGPT to generate essays is seen by some as a potential risk to traditional methods of evaluating students, but it also gives teachers the chance to develop brand-new approaches to testing students’ knowledge and skills. It is possible to use ChatGPT to improve the evaluation capabilities of instructors, stimulate collaboration and teamwork among students, and give students more possibilities to learn via trial and experience. In conclusion, ChatGPT is a technology that is considered as being disruptive in the education sector; nonetheless, it has the potential to transform education via innovation.

Responsible and Ethical Use of ChatGPT in Education To ensure that ChatGPT is utilized in a manner that is safe, fair, and courteous to students, instructors, and all other stakeholders, it is necessary to adhere to responsible and ethical practices while implementing the technology in educational settings. The application of artificial intelligence (AI) technology in educational settings, such as ChatGPT, can bring about a great many benefits; nevertheless, it also raises problems regarding ethics and responsibility. The obligations and ethical usage of ChatGPT in the education sector are outlined in the figure that can be found above labelled as Fig. 17.1. These responsibilities include respect for privacy, fairness and non-discrimination, transparency in the use of ChatGPT, and a few more.

Respect for Privacy To start, protecting the privacy of users’ data is a primary priority. Because ChatGPT is trained on enormous volumes of data obtained from the internet, it is essential to ensure that the personal data of students is

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Fig. 17.1 Responsible and ethical use of ChatGPT in education

safeguarded and not put to inappropriate use. Before using ChatGPT in the classroom, educators should inform students about how their data is gathered, used, and kept and acquire their consent. In addition, students should be aware of the security measures that are in place to protect their data. When it comes to the application of ChatGPT in the realm of education, the protection of users’ personal information is an essential concern. It is imperative that the confidentiality of all individuals participating in the educational process, including students, teachers, and anybody else, is always maintained. The General Data Protection Regulation (GDPR) in Europe and the Children’s Online Privacy Protection Act (COPPA) in the United States both require organizations to protect

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the personal data of individuals (Berger, 2022; Botha et al., 2017). These laws are similar in that they require organizations to do whatever they can to protect the personal data of individuals, including students. This comprises student information, grades, and assessment results that were gathered using ChatGPT in the educational setting. Should one fail to preserve sensitive data, may face serious repercussions in both the legal and financial spheres. Again, the usage of ChatGPT in educational settings frequently entails the communication of private information, such as grades, student performance, and personal particulars. To maintain the confidentiality of the individuals who are involved, it is necessary to prevent unauthorized access to, use of, or disclosure of the information in question. When it comes to the learning process, trust is an essential component (Myskja, 2023; Tartavulea et al., 2020). When students, teachers, and other individuals participating in education have faith that the confidentiality of their personal information will be maintained, they are more likely to give their full attention and participation to the learning process. On the other side, if a person’s right to privacy is violated in any way, the trust may be broken, which may have a detrimental effect not only on the outcomes of learning but also on overall contentment. The use of ChatGPT in educational settings should be conceived of and carried out in a manner that is consistent with ethical principles such as informed consent, transparency, and accountability. Respecting individuals’ right to privacy is an ethical obligation because it demonstrates respect for individuals and their rights. When it comes to the educational use of ChatGPT, protecting users’ privacy is of the utmost importance. It is necessary to take appropriate safeguards to safeguard the personal data of those involved in the educational process, to maintain trust, and to ensure that the use of ChatGPT is consistent with ethical principles. These three goals must be met for ChatGPT to be used ethically.

Fairness and Non-Discrimination It is possible for ChatGPT to exhibit bias, particularly in the language that it was trained on. It is essential to be aware of this, as well as to provide a suitable context when utilizing ChatGPT in the classroom setting. The potential for harm should also be brought to the attention of educators, particularly if ChatGPT is used as a grading or evaluation tool for pupils. Students shall not be subjected to bias, discrimination,

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or unfair treatment because of their use of ChatGPT, regardless of variables such as their colour, gender, or socioeconomic background. Because AI algorithms like ChatGPT can perpetuate and magnify existing biases, prejudices, and discrimination, fairness and non-discrimination must be strictly adhered to whenever ChatGPT is used in educational settings. Pupils may be treated unfairly and unequally if these biases are not recognized and remedied immediately. As a result, it is essential to make certain that ChatGPT and other AI systems are conceived, developed, and used in a fair and non-discriminatory manner at every stage of the process. Consider the possibility of using ChatGPT to assign grades to student essays, for instance. Students who come from underrepresented groups may have their essays receive unjust grades because the training data that was used to train ChatGPT was skewed towards a certain set of people. This could lead to more marginalization of populations that are already at a disadvantage in terms of education and could further exacerbate existing educational gaps. Furthermore, language models like ChatGPT can propagate and magnify unwanted preconceptions, biases, and prejudices. This is a risk that comes with using these models. When employed in educational contexts, ChatGPT may provide responses that are prejudiced and discriminatory, for instance, if the training data that was used to train it had unfavourable prejudices about groups. Students, instructors, and other members of the educational community may suffer because of this. Because of this, it is essential to conduct an in-depth analysis of artificial intelligence (AI) systems such as ChatGPT and address the possibility of discrimination and bias before implementing them in educational contexts.

ChatGpt is not a Substitute for Human Teachers ChatGPT is not intended to replace in-person instruction from qualified instructors. It ought to be regarded as a device that supplements classroom instruction and student learning rather than one that supplants it. Rather than depending solely on the AI tool to carry out all of the necessary tasks, teachers should utilize ChatGPT in a manner that improves their methods of instruction and encourages students to engage in analytical and critical thinking. In recent years, there has been a lot of discussion over the application of artificial intelligence, such as ChatGPT, in the classroom setting. It is important to note that even though ChatGPT presents several opportunities for improvement in educational settings, it

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is not intended to take the place of actual instructors. This is true for several different reasons, all of which are valid. The human connection that exists between an instructor and a student can never be replaced by ChatGPT. The relationship that exists between a teacher and their pupils is among the most essential components of the educational process. Teachers can create connections with their pupils and gain an understanding of the student’s particular requirements, as well as their strengths and weaknesses (Alam, 2022a; Attard & Holmes, 2020; Kim, 2020). On the other hand, ChatGPT is only a machine that is incapable of developing meaningful connections with its users (the students). The goal of education should not be limited to the dissemination of information but should also include the cultivation of interpersonal ties and relationships. It is largely up to the teachers to establish an atmosphere in the classroom that is encouraging and welcoming to students of all backgrounds. Because it is an AI language model, ChatGPT does not possess the emotional intelligence, empathy, or interpersonal skills that human teachers do. Because of this, it is difficult for ChatGPT to comprehend the unique requirements of each student, to offer individualized assistance, and to produce a learning environment that is positive and engaging. Creativity and analytical thinking are not strong points of ChatGPT. Teachers receive training to develop their creative and critical thinking skills, which enables them to provide individualized support and direction to the students in their classrooms. On the other hand, ChatGPT is constrained by the programming and algorithms that it uses, and as a result, it is unable to think creatively or critically in the same way that a human instructor can. The field of education is always shifting and developing (Mhlanga, 2021, 2023, 2022a). The ever-evolving requirements of students can be accommodated by human instructors, who can also add novel concepts and strategies into their lessons and adjust their approach to instruction in response to the comments and suggestions of their pupils. On the other hand, ChatGPT operates based on the data it was trained on and is unable to alter its approach to match the requirements of individual students or courses.

ChatGPT is not Capable of Comprehending the Surrounding Context In the field of education, it is frequently vital to have a solid understanding of the environment in which a student is functioning to offer the most pertinent assistance and direction (Alam, 2022a; Mahoney et al., 2021).

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This context considers the student’s culture, background, and the experiences they’ve had throughout their life (Adams et al., 2022; Alam, 2022b; Mahoney et al., 2021). ChatGPT, being a machine, is not capable of comprehending or appreciating these aspects in the same manner that a human educator can do so. To reiterate, ChatGPT is unable to deliver either hands-on training or experiential education. The greatest way for many children to learn is through opportunities for hands-on and experiential learning (Jacobs, 2020; LeBaron et al., 2019). It is in the best position of those who teach to provide these possibilities, and they can do so with hands-on projects, laboratory experiments, or field trips learning (Jacobs, 2020; LeBaron et al., 2019). On the other hand, ChatGPT is not able to deliver these kinds of educational opportunities as effectively as other platforms can. Creativity, invention, and originality are all things that can be brought to the classroom by human teachers. They can develop interesting courses, employing unique teaching approaches, and encouraging students to think critically and creatively outside of the box. Because it is an AI model, ChatGPT functions according to preprogrammed algorithms and does not have the same level of creativity and originality as human instructors have. In conclusion, although ChatGPT has the prospective to play a part in the field of education, it should not be seen as a replacement for actual teachers. The talents, experiences, and points of view that human teachers bring into the classroom cannot be reproduced by a machine in any way, shape, or form. It is essential to be aware of the constraints imposed by ChatGPT and to make certain that the platform is applied in ways that complement and do not supplant the role of human educators.

Responsible AI, It’s Important to Educate Students About AI and Its Limitations Students need to be aware that ChatGPT is not a sentient creature but rather an artificial intelligence model with the ability to generate text. Students should be encouraged to challenge the output of ChatGPT, and teachers should assist students in developing a critical and educated viewpoint on the application of artificial intelligence in the classroom. When using ChatGPT in the classroom, it is essential to educate students about artificial intelligence (AI) as well as the limitations of AI for several reasons. To begin, artificial intelligence (AI) systems such as ChatGPT are not flawless, and students must understand the limitations and biases

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that these systems have. For instance, ChatGPT was trained on a massive body of textual material sourced from the internet, which may contain information that is unreliable, biassed, or deceptive. As a consequence of this, pupils need to be able to conduct an in-depth analysis of the data that is produced by ChatGPT and comprehend how to discriminate between genuine and unreliable sources of information. Second, students need to understand the potential ethical and social ramifications that artificial intelligence could have. For instance, if ChatGPT is used to assess students’ essays or provide feedback to them, there is a possibility that it will perpetuate existing biases and prolong unfairness. This is a risk that ChatGPT poses. Students have a responsibility to be aware of these problems and to comprehend the various solutions available. Third, for students to successfully traverse a technological landscape that is always evolving, they need to be prepared with the knowledge and skills necessary to do so. Students need to understand how artificial intelligence works, as well as its capabilities and limitations since AI continues to progress and become more pervasive (Alam, 2022c; Wong et al., 2020). Because of this, they will be equipped with the knowledge and abilities necessary to make educated judgments regarding the application of AI in their future employment and their personal lives. Finally, educators can contribute to the development of a more educated and responsible connection between humans and AI by teaching students about the limitations of AI. This can help ensure that AI is employed in a way that is not only safe but also ethical and useful to society. Educating students about artificial intelligence (AI) and the limitation of AI is important for the development of critical thinking, the promotion of ethical and responsible use of AI, and the equipping of students with the skills and knowledge they need to navigate a technological landscape that is rapidly changing.

Transparency in the Use of ChatGPT It is also essential to be open and honest about the implementation of ChatGPT in educational settings and to offer frequent forums in which students and teachers can debate the moral and responsible application of artificial intelligence (AI). This can take the form of recurring workshops, discussion groups, or forums, at which participants can ruminate on the merits and drawbacks of utilizing AI in educational settings, as well as formulate suggestions for its responsible and ethical application. Because

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it guarantees that students, professors, and educational institutions understand how the technology works and what it can do, transparency is a crucial feature of adopting ChatGPT in education. Both students and teachers must understand how artificial intelligence technology such as ChatGPT processes information and creates responses before implementing the technology in educational settings. This assists in the elimination of any ambiguities or misconceptions that may develop and ensures that the technology is used ethically and responsibly (Mhlanga, 2022b; Rodgers et al., 2023). In the context of education, examples of transparent use of ChatGPT could include informing students about the algorithms and data sources used by the technology, as well as describing how it processes and creates responses. This knowledge may be made accessible in a variety of formats, such as educational materials or guides for academic institutions and their respective students and professors. In addition, educational institutions can make it a priority to utilize open-source or transparent AI technology to ensure that students and teachers have access to the source code and underlying data. This can be accomplished by making the adoption of open-source or transparent AI technology a priority. The provision of students with a clear explanation of the potential biases and limitations of the technology used in the classroom is another illustration of the transparent nature of the use of ChatGPT in educational settings. It is essential, for instance, that students be made aware of the fact that artificial intelligence algorithms, such as ChatGPT, are only as objective as the data they are trained on. The replies that are generated by the technology are susceptible to reflecting any biases that may be present in the training data. Students can be better prepared to critically analyze and comprehend the replies generated by ChatGPT if they are educated about the constraints described here and how they apply. Transparency is of the utmost importance while utilizing ChatGPT in educational settings because it not only assists in fostering an ethical and responsible utilization of the technology but also provides students with a better comprehension of the latter’s capabilities and restrictions. This gives students the ability to use ChatGPT in an informed manner and ensures that the technology is utilized in a manner that is in line with the core values and guiding principles of the educational establishment in which they are enrolled. In conclusion, the use of ChatGPT in education that is responsible and ethical needs an understanding of potential biases and limitations, security of students’ data, transparency in its usage, and critical evaluation of

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the influence it has on the teaching and learning process. It is the role of educators to assist students in forming well-informed and analytical viewpoints on artificial intelligence (AI) and to utilize AI technologies such as ChatGPT in a manner that supplements, rather than replaces, their teaching.

Accuracy of Information The accuracy of the information is very significant in the field of education since it guarantees that the material that is being taught and learned is accurate, trustworthy, and credible. When it comes to the instruction of scientific principles, providing accurate information is quite necessary, as providing the material that is not accurate might lead to misunderstandings and misconceptions. For instance, if a student is taught that the world is flat, they will have an incorrect grasp of geography, astronomy, and any other relevant disciplines that are taught in conjunction with this topic. Accurate knowledge is necessary for history lectures to comprehend the significance of the past to the present and how it has evolved. If a student is led to believe that the first black president of South Africa was Thabo Mbeki or George Washington served as the first president of France rather than the United States, then that student will have an incorrect grasp of the history of South Africa or the United States. Accuracy is essential in mathematics for both the problem-solving process and the comprehension of mathematical concepts (Al-Mutawah et al., 2019; Santia & Sutawidjadja, 2019). If a pupil is taught that adding two and four equals five, then they will struggle to solve arithmetic problems and understand more sophisticated mathematical ideas that build upon this fundamental understanding. As a result, it is essential to make certain that the information that is offered by ChatGPT as well as any other instrument that is utilized in the educational process is accurate. Both teachers and students need to exercise critical thinking when it comes to the information they are given and check it with reliable sources.

Conclusion and Policy Recommendations Significant shifts have taken place in society, the economy, and the environment as a direct result of the accelerated rate of technological advancement and increased global interconnectedness. In recent years, the field of artificial intelligence has made considerable advancements,

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which has resulted in the development of cutting-edge technologies like Open AI’s ChatGPT. The ChatGPT language model is a cutting-edge piece of technology that can usher in a period of profound transformation within the field of education. This article’s goals are to (1) provide a complete evaluation of the responsible and ethical usage of ChatGPT in education, and (2) encourage additional research and discussion on this extremely important topic. The document analytical method was used in the research, and it was found that for ChatGPT to be used in education, it is essential to ensure that privacy is respected, that there is fairness and non-discrimination, that there is transparency in the use of ChatGPT, and that there are a few other conditions outlined in the research. According to the findings of this research, it is advised that all these ideas be followed to guarantee that the integrity and responsibility of the education sector are preserved all over the world.

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McMurtrie, B. (2022). A ‘Stunning’ level of student disconnection. https://equ itydiversity.cals.wisc.edu/wp-content/uploads/sites/349/2022/05/A-%E2% 80%98Stunning-Level-of-Student-Disconnection.pdf Mhlanga, D. (2021). The fourth industrial revolution and COVID-19 pandemic in South Africa: The opportunities and challenges of introducing blended learning in education. Journal of African Education, 2(2), 15. Mhlanga, D. (2022a). Human-centered artificial intelligence: The superlative approach to achieve sustainable development goals in the fourth industrial revolution. Sustainability, 14(13), 7804. Mhlanga, D. (2022b). Rethinking Education in the Industry 4.0 in Africa, the effective management and leadership of education post-COVID-19 pandemic towards digital transformation. Available at SSRN 4245885. Mhlanga, D. (2023). Digital transformation in education: relevant paradigms and theories of teaching and learning in the industry 4.0. In: Kahraman, C., Haktanır, E. (eds.), Intelligent systems in digital transformation. lecture notes in networks and systems, Vol. 549. Springer, Cham. https://doi.org/10. 1007/978-3-031-16598-6_19 Myskja, B. K. (2023). Technology and Trust–A Kantian Approach. In Technology ethics (pp. 122–129). Routledge. Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2022). Ethical principles for artificial intelligence in education. Education and Information Technologies, 1–21. Paulus, M. J., & Langford, M. D. (Eds.). (2022). AI, faith, and the future: An interdisciplinary approach. Wipf and Stock Publishers. Pfeffer, O. P., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. ChatGPT for good? on opportunities and challenges of large language models for education. Qadir, J. (2022). Engineering education in the Era of ChatGPT: Promise and pitfalls of generative AI for education. Available online: https://www. techrxiv.org/articles/preprint/Engineering_Education_in_the_Era_of_Cha tGPT_Promise_and_Pitfalls_of_Generative_AI_for_Education/21789434 Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 100925. Rudolph, J., Tan, S., & Tan, S. (2023) ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1). Santia, I., & Sutawidjadja, A. (2019). Exploring Mathematical Representations in Solving Ill-Structured Problems: The Case of Quadratic Function. Journal on Mathematics Education, 10(3), 365–378.

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PART III

Conclusion and Policy Recommendations

CHAPTER 18

Conclusion on FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals

Introduction The terms “financial technology” and “artificial intelligence” (AI) have just become trendy today, and their influence may be seen in a variety of industries. The potential of these technologies to contribute to the achievement of the Sustainable Development Goals (SDGs) established by the United Nations has garnered a lot of attention in recent years. The convergence of these technologies and sustainable development is investigated in depth in the book titled “FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Objectives.” According to the World Economic Forum, our efforts to reach the United Nations Global Goals for sustainable development by the year 2030 are slipping farther and further behind schedule. These goals were established by the United Nations. Even while several of the Goals have made progress in a variety of different areas since 2015, the global response has not been nearly as ambitious as it ought to have been (Glemarec, 2022; World Economic Forum, 2020, 2021; PWC, 2020; Wei et al., 2021). According to the most recent Sustainable Development Progress Report, the world is not on track to eliminate poverty by the year 2030 and the process of industrialization in these nations is

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1_18

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advancing at a rate that is insufficient to meet the objective established by the 2030 Agenda, particularly in spheres of endeavour that are tied to technology (Palomares et al., 2021; World Economic Forum, 2020). Also, the research indicated that we are failing in our efforts to put a stop to climate change and safeguard biodiversity. This is because the global material footprint is expanding at a rate that is larger than both the growth rate of the economy and the growth rate of the population. Not only is performance inconsistent between different goals, but it is also inconsistent within different goals itself. In terms of the number of women who have access to the internet, OECD nations are well on their way to achieving Goal 5 on gender equality; however, when it comes to the gender gap in unpaid employment, these countries are still a long way behind. OECD nations are well on their way to achieving Goal 5 on gender equality in terms of the number of women who have access to the internet. The ongoing conflict between Russia and Ukraine is the second factor that is proving to be a big barrier. This conflict is having a significant impact on the situation (Ben Hassen & El Bilali, 2022; Rawtani et al., 2022). On the other hand, the technologies that are a part of the Fourth Industrial Revolution, such as artificial intelligence, blockchain, and the Internet of Things, among other things, are rapidly gaining public recognition and making it possible to modify entire networks and systems, particularly in the financial sector. This is especially true in the context of the financial industry (Agbehadji et al., 2021; Mhlanga, 2020, 2021, 2022). In recent years, there has been a significant amount of change throughout many aspects of society, including businesses, markets, and governments. These fundamental transformations, which influence practically every industry and constitute a threat to both tried-and-true business models as well as wholly creative ones made viable by 4IR, are causing the rate of market expansion to accelerate while simultaneously increasing the size of the market overall. There is a chance that new technologies, despite the enormous potential that they bring, may create an even larger load on the resources of the globe and our civilization. Because of this, we ought to ensure that these technologies are utilized to the full extent of their capabilities to realize their potential to revolutionize our world, transform the lives of people, and open new doors to prosperity. This would hasten the process of environmentally sustainable development on a global scale. Using emerging technology to expand and deepen the scope of existing activity is one of the most effective tactics for accelerating

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progress towards Global Goals. This strategy is one of the most effective ways to accelerate progress towards Global Goals. It is believed that the 169 targets that support the Goals may be strengthened by technological innovation and that the technologies of the Fourth Industrial Revolution may have a “strong” impact on more than half of those targets. These beliefs assume that technological innovation will continue to play an important role in the advancement of society. Maybe even more incredible is the reality that big data platforms and artificial intelligence (AI) can contribute to the accomplishment of each one of the Global Goals. To assess the role that technologies of the Fourth Industrial Revolution and more specifically FinTech will play in the attainment of the Sustainable Development Goals, the objective of this book is to use this brief introduction as a basis. This book presents an in-depth analysis of the current state of artificial intelligence (AI) and financial technology (FinTech), as well as an examination of the potential of both fields to address issues about development. The topics of financial inclusion, sustainability, ethics, and regulation are only some of the topics that are covered in this book. Other topics include AI and FinTech. It sheds light on the potential of these technologies to advance sustainable development and bring about Sustainable Development Goals (SDGs). This final chapter provides a synopsis of the most important suggestions made throughout the book. This demonstrates the potential that FinTech and AI have in overcoming obstacles to sustainable development and meeting Sustainable Development Goals (SDGs). This chapter underlines the significance of adopting and implementing these technologies in a manner that is both responsible and inclusive to guarantee that they are beneficial to all members of society. It recommends ways in which governments, entrepreneurs, and other stakeholders might make use of FinTech and AI to contribute to the achievement of sustainable development goals. The purpose of this book is to contribute to the expanding body of knowledge regarding the role that FinTech and AI could play in fostering sustainable development. We can use the potential of these technologies to bring about a future that is more egalitarian and sustainable if we encourage the adoption and application of these technologies in a way that is both responsible and inclusive.

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The General Outline of the Book The book FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technology in Achieving Development Objectives has a total of 18 chapters. The book has three major themes that are saved as the focal point for each chapter. The introduction and the background contain the details required to comprehend the main concepts of the book. The second theme focuses on the potential contribution of different smart technologies to the achievement of some of the specified Sustainable Development Goals. The last topic will focus on the strategies that various stakeholders might use to accomplish sustainable development goals within the context of the Fourth Industrial Revolution. Chapter 1 introduces the Book FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals. Chapter 2 discusses the emergence of the Fourth Industrial Revolution and the development of artificial intelligence. This chapter describes the concept underlying the so-called “Fourth Industrial Revolution,” also known as “Industry 4.0,” through an analysis of its numerous component pieces. This discussion paid close attention to artificial intelligence, which is one of the technologies that inform the study of the book. The primary objective of Chapter 3 was to introduce readers to FinTech (or financial technology). As we saw in Chapter 3, FinTech entails the digitization of conventional financial services offered by institutions such as banks, credit unions, investment banks, credit card companies, and others in the banking and financial sectors. FinTech is rapidly replacing the traditional financial system, as was discussed in Chapter 3. It’s disruptive since it leads to the emergence of many new kinds of financial institutions, each with its ecosystem. To stimulate long-term economic growth and development, FinTech is highlighted in Chapter 3 as a fresh sector with special qualities that set it different from the existing financial industry. Because of this, the FinTech business has received substantial investment from financiers all around the world. Sustainable development and the SDGs were placed in their historical context. Chapter 4 offered a historical perspective on sustainable development and sustainable development goals. The definition of “sustainable development” in Chapter 4 was given as development that meets the needs of the present generation without jeopardizing the ability of future generations to meet their own needs. Once more, Chapter 4 made clear

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that the idea of sustainable development is based on two main tenets: the importance of meeting the basic needs of the underprivileged and the understanding that current social and technological structures limit the environment’s capacity to meet present and future needs. Chapter 5 examined how financial technology and artificial intelligence (AI) help achieve Sustainable Development Goals (SDGs), with a focus on the reduction of poverty. It was shown in Chapter 5 that reducing poverty is crucial for fostering economic expansion and opening opportunities for people in developing economies. This is because one of the most significant issues facing the entire planet right now is poverty. In Chapter 6, we examined the significant roles that FinTech and AI are playing in the agriculture sector to guarantee food security. The chapter explains why AI and financial technology are crucial for ensuring food security and ending hunger. The use of financial technology (FinTech) and artificial intelligence (AI) in the field of medicine was shown in Chapter 7. This chapter explored the intersection of healthcare, artificial intelligence, and financial technology as well as the lessons that might be learned from it. Chapter 8 covered financial technology, digital transformation, and high-quality education with the fourth industrial revolution. This chapter examines the connection between excellent education and financial technology within the context of the fourth industrial revolution. Chapter 9 examined the use of artificial intelligence and machine learning to address the issue of enhancing the reliability, dependability, and efficiency of transportation in emerging nations. Chapter 10’s goal is to study whether financial technology, often known as FinTech, might help to lessen the risks and challenges associated with climate change within the context of the fourth industrial revolution. The influence of artificial intelligence (AI) and machine learning on the power sector is investigated in Chapter 11. In the chapter, it was underlined that the global energy sector must deal with a growing number of difficulties, including those that were brought on by altering patterns of supply and demand, rising consumption, increasing efficiency, and a lack of the analytics necessary for successful management. These challenges are experienced more severely in nations that are rapidly ascending the market leadership ranks. In Chapter 12, the role of Block Chain for Digital Financial Inclusion in the direction of inequality reduction was examined. The goals of this chapter were to study how blockchain technology has affected the participation of formerly excluded persons in mainstream

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financial systems and to offer an opinion on the most significant lessons and advantages that sustainable development has taught us. Chapter 13 discusses the ability of intelligent technology to promote international cooperation for development. In this chapter, we’ll look at how technology might be able to help create successful partnerships that can advance society. In Chapter 14, we examined the difficulties in applying artificial intelligence to the problem of financial exclusion. The purpose of this chapter was to conduct a thorough analysis of the difficulties associated with implementing AI-based solutions, particularly those for those who are now shut out of the financial system. Case studies were offered in Chapter 15 to illustrate how the availability of digital financial services is assisting emerging economies in their efforts to address development concerns. Case studies of developing markets for artificial intelligence and machine learning applied to sustainable development were discussed in Chapter 16. This chapter featured realworld case studies of how artificial intelligence and machine learning help address changes in growing economies around the world. These economies include those in Africa, Asia, Latin America, the Middle East, and Eastern Europe. In addition to presenting a comprehensive examination of the acceptable and ethical utilization of ChatGPT in educational settings geared towards lifelong learning, the purpose of Chapter 17 was to promote additional research and debate on this significant topic. This chapter contains the conclusion of the book.

Key Recommendations for FinTech and Artificial Intelligence for Sustainable Development After examining the intersection between FinTech, AI, and sustainable development, the book “FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals” provides the following key recommendations outlined in Fig. 18.1 as the basis for these technologies to contribute more towards sustainable development. The recommendations listed above aim to support the responsible adoption and implementation of FinTech and AI towards achieving the SDGs and promoting sustainable development. The adoption and implementation of FinTech and AI for sustainable development require collaboration among various stakeholders, including governments, the private sector, civil society, and academia. Therefore, it is recommended to

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Fig. 18.1 Key recommendations for FinTech and artificial intelligence for sustainable development

establish partnerships and networks that bring together these stakeholders to leverage their expertise and resources towards achieving the SDGs. Again, Continuous research and development are needed to improve the effectiveness and impact of FinTech and AI in sustainable development. Therefore, it is recommended to invest in innovation, including research, incubation, and acceleration programmes that support the development of new and innovative solutions. The other important aspect is that FinTech and AI must be used responsibly and inclusively to ensure that they benefit all members of society. It is recommended to establish ethical frameworks and guidelines that promote the responsible use of these technologies, including data privacy, transparency, and accountability. Additionally, it is crucial to ensure that these technologies are accessible and affordable to marginalized and underserved communities. Furthermore, the successful adoption and implementation of FinTech and AI for sustainable development require skilled and trained personnel. Therefore, it is recommended to invest in capacity building and training programmes that equip individuals and organizations with the necessary knowledge and skills. Again, given the potential risks associated with FinTech and AI, appropriate regulatory frameworks are needed to

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ensure their responsible use. It is recommended to develop regulatory frameworks that strike a balance between promoting innovation and safeguarding against potential harm. The other issue is that the use of FinTech and AI solutions for sustainable development must be tailored to the specific context and needs of different regions and communities. Therefore, it is recommended to focus on developing context-specific solutions that consider local contexts, cultures, and norms. Also, given the fact that the use of FinTech and AI technologies involves the collection, storage, and processing of large amounts of data. Therefore, it is recommended to prioritize data privacy and security measures to protect against unauthorized access, data breaches, and other cyber threats. Concerning the promotion of sustainable development, it is crucial to evaluate the impact of FinTech and AI solutions in promoting sustainable development. Therefore, it is recommended to prioritize impact measurement and evaluation to assess the effectiveness and efficiency of these solutions in achieving the SDGs. The adoption and use of FinTech solutions for sustainable development require financial literacy and understanding. Therefore, it is recommended to prioritize financial literacy and education programmes that equip individuals and communities with the necessary knowledge and skills to use these technologies effectively. The fact that FinTech and AI solutions have the potential to support entrepreneurship and small businesses, which are essential drivers of economic growth and development. Therefore, it is recommended to prioritize the development of FinTech and AI solutions that support entrepreneurship and small businesses, including access to finance, markets, and other resources. These recommendations aim to promote responsible and inclusive adoption and implementation of FinTech and AI solutions for sustainable development. By focusing on context-specific solutions, data privacy and security, impact measurement and evaluation, financial literacy, and supporting entrepreneurship and small businesses, we can leverage these technologies towards achieving the SDGs and promoting sustainable development.

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Summary of the Policy Recommendations for FinTech and Artificial Intelligence for Sustainable Development • The development and application of FinTech and AI to further the SDGs should be supported and promoted by governments and lawmakers. • Establish a legal structure that encourages innovation while ensuring that issues like data security, privacy, and ethics are considered. • Boost spending on FinTech and AI research and development for sustainable growth. • Encourage public–private partnerships to take advantage of each sector’s advantages and advance sustainable growth. • Encourage cooperation between FinTech and AI businesses to exchange information, skills, and best practices. • Create incentives, like grants or tax breaks, for FinTech and AI businesses to create products that explicitly address the SDGs. • Increase access to finance for individuals and communities affected by the SDGs, particularly in developing countries, using FinTech solutions. • Promote digital literacy and education to ensure that individuals and communities can effectively utilize FinTech and AI solutions to advance sustainable development. • Encourage international collaboration, information exchange, and the exchange of best practices to promote the use of FinTech and AI for sustainable development. • Promote the development of open and interoperable standards for FinTech and AI solutions to guarantee their accessibility and compatibility across various platforms and systems. • Encourage the use of FinTech and AI technologies to combat climate change and environmental sustainability, including smart grids, energy management systems, and sustainable supply chain management. • Promote the development of FinTech and AI solutions that promote financial inclusion, such as mobile banking and digital wallets, so that people and communities can access financial services and participate in the formal economy.

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• To address inequities and promote equitable development, stimulate the development of FinTech and AI solutions that promote social and economic justice, such as microfinance and impact investing. • Provide incentives for FinTech and AI firms to prioritize ethical issues in the creation and deployment of their products, such as certification programmes and industry standards. • Encourage the use of FinTech and AI technologies to improve health and well-being, such as telemedicine, electronic health records, and AI-based disease detection and treatment. • Promote the growth of FinTech and AI technologies that promote sustainable urbanization and infrastructure, such as intelligent transportation and building management systems. • Promote the use of FinTech and AI technologies to promote ethical and sustainable consumption and production, notably through employing supply chain transparency and circular economy concepts. • Promote the development of FinTech and AI solutions that promote peace, justice, and strong institutions, such as identity verification systems based on blockchain technology and AI-based fraud detection. • Establish a forum for stakeholder interaction and dialogue to promote inclusive, transparent, and accountable development and implementation of FinTech and AI solutions for sustainable development. • To guarantee the effectiveness and sustainability of FinTech and AI solutions, it is important to monitor, assess, and adjust as needed.

Conclusion The purpose of the book titled “FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals” was to investigate the relationship that exists between artificial intelligence (AI), financial technology (FinTech), and sustainable development. This book investigates the role that these technologies could play in accomplishing the Sustainable Development Goals (SDGs) set forth by the United Nations, which include the reduction of inequality, the eradication of poverty, and the combating of climate change. The book is broken up into 17 chapters, each of which delves deeper into a specific element of sustainable development as it relates to

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FinTech, AI, or one of the other smart technologies. This final chapter of the book presented a synopsis of the most important discoveries and accomplishments made throughout the earlier chapters. This demonstrates the potential that FinTech and AI have in overcoming obstacles to sustainable development and accomplishing Sustainable Development Goals (SDGs). This chapter places a strong emphasis on the importance of collaboration between various stakeholders, such as governments, the private sector, and civic society, to make effective use of these technologies to advance sustainable development. In addition, the chapter highlights the necessity of continuing research and innovation to improve efficiency as well as the influence that FinTech and AI have in the field of sustainable development. In conclusion, it makes a plea for an approach to the utilization of these technologies that is both responsible and inclusive, intending to ensure that they are beneficial to all members of society and that they contribute to a more egalitarian and sustainable future.

References Agbehadji, I. E., Awuzie, B. O., & Ngowi, A. B. (2021). COVID-19 pandemic waves: 4IR technology utilisation in the multi-sector economy. Sustainability, 13(18), 10168. Ben Hassen, T., & El Bilali, H. (2022). Impacts of the Russia-Ukraine war on global food security: Towards more sustainable and resilient food systems. Foods, 11(15), 2301. Glemarec, Y. (2022). How to ensure that investment in new climate solutions is sufficient to avert catastrophic climate change. In Handbook of international climate finance (pp. 445–474). Edward Elgar Publishing. Mhlanga, D. (2020). Industry 4.0 in finance: the impact of artificial intelligence (ai) on digital financial inclusion. International Journal of Financial Studies, 8(3), 45. Mhlanga, D. (2021). Artificial intelligence in industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability, 13(11), 5788. Mhlanga, D. (2022). The role of artificial intelligence and machine learning amid the COVID-19 pandemic: What lessons are we learning on 4IR and the sustainable development goals. International Journal of Environmental Research and Public Health, 19(3), 1879. Palomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., Alonso, S., Melero, F.J., Molina, D., Fernández, B., Marchena, R., Moral, C., Javier Pérez de Vargas., & Herrera, F. (2021). A panoramic

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view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: Progress and prospects. Applied Intelligence, 51, 6497–6527. Pwc. (2020). https://www.pwc.com/gx/en/services/sustainability/publicati ons/accelerating-sustainable-development.html Rawtani, D., Gupta, G., Khatri, N., Rao, P. K., & Hussain, C. M. (2022). Environmental damages due to war in Ukraine: A perspective. Science of the Total Environment, 850, 157932. Wei, T., Wu, J., & Chen, S. (2021). Keeping track of greenhouse gas emission reduction progress and targets in 167 cities worldwide. Frontiers in Sustainable Cities, 3, 696381. World Economic Forum. (2020). Unlocking technology for the global goals. https://www3.weforum.org/docs/Unlocking_Technology_for_the_Global_ Goals.pdf World Economic Forum. (2021). Harnessing technology for the global goals: A framework for government action. https://www3.weforum.org/docs/WEF_ Harnessing_Technology_for_the_Global_Goals_2021.pdf

Index

A Aadhar Housing Finance Ltd, 380, 381 Aavas Financiers Ltd, 381, 382 Access to electricity, 249 Access to Essential Services, 105 Access to sanitation, 83 Advanced robotics, 310 Affordable and Clean Energy, 12 Agenda for Sustainable Development, vii, 76 Ant Colony Optimizer, 197 Anti-Theft Technology in Brazil, 377 ARPANET, 20 Artificial Immune System, 196 Artificial intelligence (AI), vii, viii, 4–12, 15, 16, 23–37, 51, 53, 54, 57, 68, 90, 92, 94, 95, 97, 102–114, 121, 123, 126, 128–140, 145–148, 153–163, 165, 166, 182, 183, 185, 188, 193–195, 197–205, 207–210, 217, 218, 241–244, 246–248, 250, 251, 253–259, 295–299,

301–303, 310, 317–320, 322–331, 343, 346, 353, 355, 358, 365–383, 387–397, 400, 402–406, 413–422 Artificial neural networks, 195 Augmented Reality (AR), 308 autonomous vehicles, 25, 37, 198, 205, 209, 376

B Bangladesh, 107, 214, 346 Barter Trade, 44 Bee Colony Optimization, 197 Bills of exchange and cheques, 45 Biometric payments, 57 Blockchain, 51, 54, 56, 128, 150, 264–270, 273, 275, 277–282, 284, 286, 287, 307, 326 Blockchain networks, 269–271 Block chain technology, 266, 284, 307 Branch mobile application, 380 Brundtland Report, 72, 273

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Mhlanga, FinTech and Artificial Intelligence for Sustainable Development, Sustainable Development Goals Series, https://doi.org/10.1007/978-3-031-37776-1

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INDEX

C Cards, 45, 59 Career advancement, 189 Cash networks, 350 Central bank digital currency, 55 ChatGPT, 11, 387, 388, 390–406, 418 Child mortality, 75, 80, 162 Climate action, 12 Climate change, 220, 228, 229, 231 Climate-related challenges, 227 Cloud computing, 25, 51, 90, 176, 247, 326 CO2 emissions, 10, 194, 199, 215, 216, 232, 242, 259, 296 Cobots, 25 Coins, 44 Community inclusion, 283 Computational design, 25 Computer vision systems, 135 Consensus services, 55 COVID-19, 78, 79, 110, 119, 122, 157, 163, 297, 298, 339, 342, 345, 366 Credit access, 229 Cryptocurrencies, 55, 61, 90, 95, 147, 264–266, 276, 278

Digital lending, 51 Digital payments, 45, 351 Digital payments/electronic payment, 48 Digital savings, 52 Digital Transformation in Education, 181–183 Distributed ledger technology, 56

D Deep learning, 27–29, 31, 32, 34, 35, 95, 120, 122, 130, 131, 133, 160, 205, 247, 248, 284, 371, 390 Democratic Republic of the Congo, 96, 107 Development, vii, 3–5, 7, 11, 69, 70, 74–76, 79, 81, 85, 97, 102, 114, 162, 214, 219, 286, 291–295, 300, 413, 415, 416, 418, 422 Digital asset, 55 Digital capital raising, 49 Digital currency, 55

F Fairness and Non-Discrimination, 399 FarmDrive, 318, 321, 379 Financial inclusion, 124, 218, 221, 226, 227, 271, 286, 322, 356, 417 Financial security, 357 Financial services, 226, 232, 278, 356, 374, 379 Financial Technology (FinTech), vii, 4–9, 11, 12, 41, 42, 46–48, 50–54, 57–59, 61–63, 89, 91, 92, 94, 97–101, 114, 121, 123–128, 140, 146, 147,

E Education and Digital Transformation, 176 Education management, 186 Electricity, 80, 248, 249 Electricity access, 80 Emerging markets, 200, 256, 257, 345, 356 Energy demand, 254 Energy efficiency in homes, 254 Energy prices, 253 Energy storage, 6, 25, 37, 250 Environment and Development, 68–70, 72, 73, 273 E-Retailers, 350 Ethiopia, 107 Extreme poverty, 96

INDEX

149–152, 184–189, 215, 216, 219, 226, 319, 323, 337–348, 350, 353–356, 358–360, 413, 415, 416, 418–422 Food security, 121–124, 128, 129, 219 Food waste reduction, 134 Fourth Industrial Revolution (4IR), vii, 4–6, 9, 12, 15, 16, 23–26, 37, 46, 67, 156, 159, 162, 174, 178, 184, 217, 218, 226, 272, 299–301, 414–416 Fuzzy logic model, 196

G GDP, 98, 105, 199, 209, 224, 286, 339, 368 Global Partnership, 292, 299, 300 Global Partnership for Development, 292, 300 Good Health and Well-being, 12

H Healthcare Sector, 151 Hunger and malnutrition, 93

I Identification, Control, and Monitoring, 160 Improved Water Access, 82 Industry 4.0, 6, 15, 16, 37, 176, 177, 217, 272, 416 Innovative technologies, vii, 19, 387 Insurance, 54, 126, 151, 152, 231, 284, 348 Interbank Financial Telecommunications, 60 Internet, 6, 10, 12, 21–23, 25, 45, 60, 90, 99–101, 176, 199, 217,

427

242, 245, 252, 254, 259, 264, 276, 277, 297, 305, 306, 388 Internet of Things (IoT), vii, 4, 6, 10, 12, 22–25, 37, 90, 102, 136, 176, 217, 218, 242, 245, 246, 252, 254, 259, 264, 305, 306, 414

M Machine learning, 32–36, 62, 130, 156, 197, 200, 244, 245, 253, 256, 303, 305, 376, 383 Materials science, 6, 25, 37, 243 Microinsurance policies, 127 Millennium Development Goals, 74–76, 97, 162 Mobile network operators, 349 Modern energy services, 80 M-Pesa, 62, 125, 356 M-TIBA, 151

N Nanotechnology, 6, 15, 25, 37 Natural language processing, 33, 122, 330 Non-governmental organizations, 73, 75, 136 No Poverty, 12

O Online learning, 187 OpenAI, 387, 389–393, 395 Open application programming interfaces, 56

P Paper money and banknotes, 44 Partnerships for the goals, 12 PAYPAL, 60

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INDEX

Paytm case study, 351 Peer-to-peer transactions, 348 Personalized learning, 188 Poverty, 92, 93, 95, 97, 98, 102, 104, 107, 219, 226 Precision agriculture, 137, 138 Production, 18, 139, 250, 251 Q QR codes, 56 Quality education, 12, 184 Quantum computing, 6, 24, 25, 37, 217 R Real Time Gross Settlement, 46 Reduced inequalities, 12 Reinforcement learning, 35, 245, 258 Respect for privacy, 397 Robots, 6, 25, 28, 121, 156, 178, 197, 209, 217, 218, 246, 255, 310, 311, 319, 365, 367 S Smart Parking Management, 206, 207 Smart Technologies, 4, 5, 11, 300, 413, 416, 418, 422 Supervised learning, 35 Sustainable cities and communities, 12 Sustainable Development Goals (SDGs), vii, viii, 3–5, 7, 11, 36, 63, 69, 73, 75, 76, 78–85, 90, 97, 109, 113, 114, 120, 127, 159, 162, 163, 214, 219, 241, 272, 286, 287, 291–295, 298, 312, 337, 356, 413, 415–422

T Technology companies, 349 The Detection of incidents, 202 The First Industrial Revolution, 16 The Food and Agriculture Organization, 119 The World Bank, 94, 96, 97, 214, 219, 221, 276 Third Industrial Revolution, 20–23 3D printing, 6, 15, 37, 217, 218

U UNICEF, 280, 283, 297, 298 United Nations, vii, 3, 11, 69, 71–73, 75–83, 85, 90, 93, 94, 107, 108, 119, 120, 140, 162, 163, 219, 220, 273, 291–293, 295, 299, 312, 413, 422 United Nations Environment Program (UNEP), 71 Universal and equitable access, 80 Unsupervised learning, 35, 245

W Wealth Management and Investment, 52 World Economic Forum, 3, 15, 217, 320, 413 World Food Programme (WFP), 122, 136 World Health Organization (WHO), 72, 81, 159

Z Zero Hunger, 12