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The Future of Money, Investments, and Financial Services

A Roadmap Powered by AI and Other Technologies
Edited by Renuka Sharma and Kiran Mehta
Copyright: 2026   |   Expected Pub Date:2026/07/30
ISBN: 9781394339402  |  Hardcover  |  
438 pages

One Line Description
Navigate the most significant shift in the history of finance with this essential
guide to leveraging AI, blockchain, and data analytics to drive growth and manage risk in an increasingly tech-driven economy.

Audience
Academics, researchers, students, financial and investment institutions, as well as asset management firms seeking to understand the implications of artificial intelligence and other technologies.

Description
Finance is undergoing a fundamental transformation. The rapid rise of artificial intelligence (AI) represents the most significant shift the financial sector has ever experienced, reshaping how money is created, invested, managed, and exchanged. AI is no longer a supporting tool; it is redefining the meaning and structure of finance itself. This book explores this transformation and the technologies
driving it, offering an in-depth exploration of how cutting-edge technologies are transforming the financial sector. In this groundbreaking edited volume, experts from various fields provide invaluable insights into how AI, blockchain, data analytics, and other innovations are revolutionizing the way we think about money, investments, and financial services. From the evolution of digital currencies to the future of personalized financial advice, this book provides a comprehensive
look at the technological advancements shaping the future of finance. As industries and economies around the world rapidly adapt to these technological disruptions, this vital resource delves into the practical implications of these advancements, offering strategies for harnessing their power to drive growth, manage risk, and enhance decision-making. Whether you’re an investor or a business leader navigating the complexities of AI-driven financial services, this book offers essential knowledge and actionable insights to succeed in an increasingly tech-driven financial world.
Readers will find the volume:
• Explores the transformative role of AI and emerging technologies in revolutionizing financial services and investment strategies;
• Provides a comprehensive roadmap for businesses and individuals looking to stay ahead in the rapidly evolving financial landscape;
• Features contributions from leading experts, offering practical insights and real-world applications for the future of finance;
• Discusses the intersection of technology and finance, highlighting key trends that will shape the future of money management;
• Offers actionable strategies for leveraging technological advancements to enhance financial decision-making and investment outcomes.

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Author / Editor Details
Renuka Sharma, PhD is a Professor of Finance at the Chitkara Business School, Chitkara University, Punjab, India with more than 20 years of extensive teaching and research experience. She has published more than 70 research papers in national and international journals and edited two books with Wiley-Scrivener: “Decentralized Finance and Blockchain” and “Deep Learning Tools for Predicting Stock Market Movements”. Her research focuses on innovation, responsible investment, corporate governance, behavioral biases, risk management, and portfolios.

Kiran Mehta, PhD is a Professor of Finance at the Chitkara Business School, Chitkara University, Punjab, India with more than 20 years of teaching and research experience. She has edited two books with Wiley-Scrivener: “Decentralized Finance and Blockchain” and “Deep Learning Tools for Predicting Stock Market Movements”. Her research focuses on sustainable business and entrepreneurship, cryptocurrency, ethical investments, and female entrepreneurship.

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Table of Contents
Preface
Part I: Introduction to AI in Finance
1. The Rise of Artificial Intelligence in the Indian Banking Industry
Bhavna Sharma, Renu Bala and Meenakshi
1.1 Introduction
1.2 Previous Research
1.3 Applications of AI in Banking Sector
1.3.1 Impact of AI on Customers
1.3.2 Impact of AI on Bank Employees
1.4 How Banks are Using AI
1.5 Challenges Face by the Banks in AI
1.6 Implications of the Study
References
2. The Evolution of Finance and the Rise of AI
Sonal Trivedi and Mufaro Dzingirai
2.1 Introduction
2.1.1 Purpose
2.2 Historical Evolution of Finance
2.2.1 Early Financial Systems and Practices
2.2.2 Emergence of FinTech and Digital Winds of Change
2.2.3 Key Milestones in Financial Technology
2.2.4 Conclusion
2.3 AI Applications in Financial Services
2.3.1 Robo-Advisors and Wealth Management
2.3.2 Risk Management and Predictive Analytics
2.3.3 Fraud Detection and Prevention
2.3.4 Customer Service and Personalization
2.3.5 Conclusion
2.4 Impact of AI on Financial Markets
2.4.1 Improving Market Efficiency
2.4.2 Improving Market Liquidity
2.4.3 Impact on Market Volatility
2.4.4 Impact on Market Participants
2.4.5 Conclusion
2.5 Conclusion
References
3. Harnessing Artificial Intelligence for Sustainable Development and Empowering Social Finance Systems
Ram Singh, Vinay Pal Singh and Ajit Singh Tomar
3.1 Introduction
3.1.1 Sustainable Development and AI: A Transformative Synergy
3.1.2 Empowering Social Finance with AI
3.1.3 AI as a Catalyst for Inclusivity and Equity
3.1.4 Challenges and Ethical Considerations
3.1.5 Social Finance Systems
3.1.5.1 Impact Investing
3.1.5.2 Microfinance
3.1.5.3 Social Impact Bonds (SIBs)
3.1.5.4 Community Finance
3.1.6 Role of Artificial Intelligence in Social Finance
3.2 Review of Literature
3.2.1 Research Gap
3.3 Objectives and Methodology
3.4 Artificial Intelligence in Sustainable Development of Social Finance Systems
3.4.1 Financial Accessibility with the Help of AI
3.4.2 Specifically Managing Strategic Social Investments
3.4.3 Promoting Environmental Accountability Through AI
3.4.4 Overcoming Challenges in AI Adoption
3.4.5 Advancing Future Innovations
3.5 Systemic Inefficiencies in Resource Allocation and Social Impact Financing
3.5.1 Predictive Analytics for Resource Optimization
3.5.2 Blockchain Integration for Transparency and Trust
3.5.3 Machine Learning for Social Impact Financing
3.6 Examining the Ethical Implications of AI Deployment in Social Finance Systems
3.6.1 Transparency in AI Systems
3.6.2 Accountability in AI Deployment
3.6.3 Inclusivity in AI Design and Implementation
3.7 Impact on Financial Accessibility and Environmental Conservation
3.7.1 Evaluating Financial Accessibility
3.7.2 Assessing Environmental Conservation Efforts
3.7.3 Regional Variations and Insights
3.8 Challenges in Deploying AI for Sustainable Development in India
3.8.1 Data Accessibility and Quality
3.8.2 Infrastructure and Technological Gaps
3.8.3 Ethical and Governance Concerns
3.8.4 High Costs of Implementation
3.9 Opportunities for AI in Sustainable Development in India
3.9.1 Predictive Analytics for Agriculture and Resource Management
3.9.2 Blockchain Integration in Social Finance
3.9.3 AI-Driven Financial Inclusion
3.9.4 Environmental Conservation Through AI
3.9.5 AI-Enabled Disaster Management
3.10 Findings and Suggestions
3.10.1 Findings
3.10.1.1 Data Infrastructure and Quality
3.10.1.2 AI Potential in Agriculture and Resource Management
3.10.1.3 Blockchain and Financial Inclusion Synergies
3.10.1.4 Environmental and Disaster Management
3.10.1.5 Ethical and Policy Challenges
3.10.2 Suggestions
3.10.2.1 Enhance Data Infrastructure
3.10.2.2 Promote Public-Private Partnerships
3.10.2.3 Strengthen Ethical Governance Frameworks
3.10.2.4 Focus on Capacity Building
3.10.2.5 Expand AI Use in Disaster Management
3.11 Conclusion
3.12 Future Directions for Research
References
4. Benefits of Financial Inclusion to Financial Institutions, Technology Firms and the Government: Implications for Digital Financial Inclusion
Peterson K. Ozili
4.1 Introduction
4.2 Conceptual Framework
4.3 Financial Inclusion Benefits to Financial Institutions, Technology Firms, and the Government
4.3.1 Financial Inclusion Benefits to Financial Institutions
4.3.2 Financial Inclusion Benefits to Technology Firms
4.3.3 Financial Inclusion Benefits to the Government
4.4 Criticism of Corporate and Government Interests in Financial Inclusion
4.5 Conclusion
References
Part II: AI in Investment Management
5. Revolutionizing Finance: Machine Learning for Smart Portfolio Optimization
Nitin Kulshrestha
5.1 Introduction
5.1.1 Algorithmic Trading
5.1.2 Definition and Mechanism
5.1.3 Quantitative Finance
5.2 Review of Literature
5.2.1 Historical Development
5.2.2 Current-Landscape
5.3 Quantitative Finance Applications
5.3.1 Portfolio Optimization
5.3.2 Risk Management
5.3.3 Machine Learning in Finance and Big Data
5.3.4 Factor-Based Analysis
5.4 Role of Machine Learning in Quantitative Finance
5.4.1 Supervised Learning in Quantitative Trading
5.4.2 Contingent Learning and Predictive Analysis with Deep Learning
5.4.3 Deep Learning and Neural Networks
5.5 Comparative Analysis of ML Models
5.6 Algorithms for Smart Portfolio Management
5.6.1 Insider Trading and Fraud Detection
5.6.2 Deep Learning and Trading Strategy
5.6.3 Reinforcement Learning and Momentum Strategies
5.6.4 Genetic Algorithms and Technical Indicators
5.6.5 Breakout and Reversal Strategies
5.7 Application of Artificial Intelligence in Algorithmic Trading
5.7.1 Predictive Model Building and Portfolio Optimization
5.7.2 Predictive Model Testing and Portfolio Creation
5.7.3 Utilizing Advance Data Sets
5.7.4 Predictive Modeling and Risk Assessment
5.7.5 Portfolio Weight Optimization
5.7.6 Dynamic Portfolio Rebalancing
5.8 Concerns and Considerations
5.8.1 Challenges in Model Interpretability
5.8.2 Algorithm Attach in Trade Strategies
5.8.3 Explainability in AI Models
5.8.4 Data Biases During Training
5.8.5 Business Procedures and Policies
5.8.6 Ethical and Regulatory Differences
5.9 Future Directions of Algorithmic Trading and Portfolio Optimization
5.9.1 Algorithmic Approaches in Risk Management
5.9.2 High-Frequency Trading and Portfolio Optimization
5.9.3 Machine Learning for Portfolio Practice Enhancement
5.9.4 Strategies of Hybrid and Quantum Inspired Nature
5.9.5 State of the Art Strategies in Machine Learning
5.9.6 Synthesis of AI and Multiagent Systems
5.9.7 High-Frequency Trading, Algorithmic Risk Management and Dynamic Strategies
5.10 Challenges in Algorithmic Trading and Portfolio Optimization
5.10.1 Market Volatility and Complexity
5.10.2 Data Overfitting and Multicollinearity
5.10.3 Integration of Various Data Sources
5.10.4 Risk Management
5.11 Conclusion
Bibliography
6. Dependency of Machine Learning Based Prediction Performance on Market Specificity, Window Length, and Algorithm Choice in the Context of Global Stock Indices
Jasleen Kaur and Khushdeep Dharni
6.1 Introduction
6.2 Background of the Study
6.3 Materials and Methods
6.3.1 Data Collection and Sampling
6.3.2 Preprocessing of Data
6.3.3 Data Analysis
6.3.4 Calculation of Returns
6.4 Results
6.5 Discussion
6.6 Conclusion
Bibliography
7. Analysis of Portfolio Using Machine Learning for Selected ESG Companies in India: A PCA Approach
Kapil Shrimal, Priyanka Mathur and Nidhi Solanki
7.1 Introduction
7.1.1 Overview of ESG Investing and Its Importance in Today’s Financial Markets
7.1.1.1 Environmental
7.1.1.2 Social
7.1.1.3 Governance
7.2 Background of the Study
7.2.1 ESG Ratings Key Elements
7.2.1.1 Machine Learning in ESG Ratings
7.2.1.2 Portfolio Optimization Techniques
7.2.1.3 Productivity and Scalability
7.3 Literature Review
7.4 Research Gap
7.5 Research Questions
7.6 Purpose of the Study
7.6.1 Key Objectives
7.6.1.1 Enhancing Predictive Accuracy
7.6.1.2 Portfolio Optimization
7.6.1.3 Addressing Data Complexity
7.6.2 Contributing to Sustainable Investment Practices
7.6.3 Informing Regulatory Compliance
7.7 ESG in Investment Decisions
7.7.1 Key Factors Attributable to Importance of ESG in Investment Decisions
7.7.1.1 Risk Management
7.7.1.2 Financial Performance
7.7.1.3 Consumer Demand
7.7.1.4 Long-Term Value Creation
7.7.2 Comparison of ESG and Non-ESG Portfolios in Terms of Risk and Return
7.7.3 Most Significant ESG Factors Portrayed in Indian Companies
7.7.4 Impact of ESG on Financial Performance
7.7.5 ESG Investing in India: Opportunities and Challenges
7.7.6 Factors in ESG Driving Portfolio Performance
7.7.7 Consequences of Incorporating ESG Indicators in Portfolio Management
7.8 Principal Component Analysis (PCA) in Financial Analysis
7.8.1 Need for PCA in ESG Portfolio Optimization
7.8.2 Main Components Identified by PCA in ESG Stocks
7.8.2.1 Aggregate ESG Score
7.8.2.2 Environmental Factors
7.8.2.3 Social Factors
7.8.2.4 Governance Factors
7.8.2.5 Disagreement Among Rating Agencies
7.9 Research Methodology
7.10 Data Analysis and Results
7.11 Discussion
7.12 Practical Implications for Investors and Portfolio Managers
7.13 Limitations of the Study and Areas for Improvement
7.13.1 Minimal Sample Size
7.13.2 Time Horizon Constraints
7.13.3 Static PCA Model
7.13.4 Lack of Sectorial Segmentation
7.14 Upcoming Research Plans in the Domain of Machine Learning in the Field of ESG Investing
7.15 Recommendations
7.15.1 Advice for Investors for Further Portfolio Management
7.15.1.1 Incorporating ESG Metrics into Financial and Investment Decisions
7.15.1.2 Implement Multifaceted Approach
7.16 Conclusion
Bibliography
8. Detecting Information Flow Between Cryptocurrencies Using Transfer Entropy
Faniza Joshi, Shivani Inder and Manisha Dheer
8.1 Introduction
8.1.1 Blockchain Technology and Decentralization
8.2 Real-Time Information Sharing
8.2.1 Quantifying Information Transmission through Transfer Entropy
8.2.2 Information Flow between Cryptocurrency Markets
8.3 Data
8.4 Findings
8.4.1 High Interconnectivity among Major Cryptocurrencies
8.5 Conclusion
Bibliography
Part III: AI in Risk Management and Compliance
9. Combat against Financial Fraud in Indian Banking Sector: A Cyber Security Measures in the Era of AI
Bhaveshkumar Parmar and Hetal Thaker
9.1 Overview of Financial Fraud in the Indian Banking Sector
9.2 Literature Review
9.3 Observed Financial Frauds in Indian Banking
9.3.1 Major Instances of Financial Fraud in the Past
9.3.2 Industry 5.0 and Types of Financial Frauds in Banking Sector
9.3.3 Prevention Techniques
9.3.4 Detection Techniques
9.4 Understanding Industry 5.0 and Its Implications
9.4.1 Characteristics of Industry 5.0
9.4.2 Implication of Industry 5.0 in Banking Sector
9.5 Cybersecurity Measures for Industry 5.0
9.5.1 Role of Artificial Intelligence and Machine Learning in Fraud Detection
9.5.2 Implementing Blockchain Technology for Secure Transactions
9.5.3 Securing IoT Devices and Interconnected Systems in Banks
References
10. Potential of Ethical AI in Detection of Insurance Claim Fraud
Shefali Saluja and Preeti Kaushal
10.1 Introduction
10.2 Literature Review
10.3 Research Methodology
10.4 Discussion and Analysis
10.4.1 Role of AI in Fraud Detection
10.4.2 Study Analysis
10.5 Conclusion
10.5.1 Integration of Ethical AI
10.5.2 Future of the AI in Fraud Landscape
10.6 Implications of the Study
Bibliography
11. Ensuring Ethical Adoption of AI: Addressing Bias and Fairness in Financial Services
Jeevesh Sharma and Swati Jain
11.1 Introduction
11.2 AI Adoption in Financial Services
11.3 Ethical AI’s Significance in Finance
11.4 Challenges and Risks of Bias
11.4.1 Potential Risk of Bias in AI in Financial Services
11.5 Ethical Concerns and Risks Associated with AI Implementation in Financial Services
11.5.1 Mitigating Bias in AI in Financial Services
11.6 Effect of Ethical Concerns in Ai on Stakeholders
11.7 Solutions to Address Ethical Concerns in AI-Powered Financial Services
11.8 Regulatory Aspect for Ethical AI in Financial Services
11.9 Strategies and Tactics for Executing Ethical AI in Financial Services
11.10 Conclusion
Bibliography
Part IV: AI in Customer Engagement and Personalization
12. AI in Customer Engagement and Personalization
Kanthavel R., Adline Freeda R. and Dhaya R.
12.1 Introduction
12.2 Credit Risk Assessment with AI
12.3 Fraud Detection and Prevention in Digital Age
12.4 Navigating Regulatory Compliance with AI Solutions
12.5 Conclusion
References
13. AI-Driven Financial Amenities: Exploring Customer Satisfaction and Behavioural Magnitudes Towards the Return Expectations in Equity Markets
Aparna Vajpayee
13.1 Introduction
13.2 Review of Literature
13.2.1 AI in Financial Services
13.2.2 Customer Awareness and Acceptance
13.2.3 Customer Acceptance and AI-Powered Channels
13.2.4 Effect of AI on Return Expectations
13.2.5 Demographic Factors and AI Adoption
13.2.6 AI-Driven Customer and Account Management
13.3 Research Gap
13.3.1 Less Focus on Customer-Centric Evaluation
13.3.2 Demographic Influences in Awareness and Adoption
13.3.3 Integrated Analysis of AI-Based Services
13.3.4 Expectations of Returns from AI-Driven Platforms
13.4 Research Methodology
13.4.1 Objectives of the Research
13.4.2 Research Design
13.4.3 Data Collection Method
13.4.4 Sampling Strategy
13.4.5 Variables and Hypotheses
13.4.6 Hypotheses
13.4.7 Data Analysis Techniques
13.4.7.1 Chi-Square Test of Independence
13.4.7.2 Factor Analysis
13.4.7.3 Regression Analysis
13.4.7.4 Descriptive Statistics
13.4.7.5 Validity and Reliability
13.4.7.6 Reliability
13.4.8 Ethical Issues
13.5 Analysis
13.6 Key Outcomes
13.7 Discussion
13.7.1 AI-Based Trading Tips and Branch Facility
13.7.2 Regression Analysis: Determinants of Return Expectations
13.8 Implications for Practice
13.9 Conclusion
References
Part V: Ethical and Future Considerations
14. Ethical Frontiers: Navigating AI’s Integration in the Behavioral Finance
Dilpreet Kaur, Arun Sharma, Supriya Bajaj and Bisman Kaur
14.1 Introduction
14.2 Literature Review
14.3 Objectives
14.4 Challenges of Bias in AI Models
14.5 Impact of Bias on Financial Decision Making
14.6 Emerging Trends in AI and Finance
14.7 Key Findings
14.8 Implications
14.9 Conclusion
14.10 Scope for Future Research
Bibliography
15. From Data to Decisions: A Bibliometric Analysis of the Role of Artificial Intelligence in Financial Innovation
Vimmy Bajaj and Vijaya Patil
15.1 Introduction
15.2 Research Methodology and Data Collection
15.3 Bibliometric Analysis and Discussion
15.3.1 Descriptive Data and Publication Growth Analysis
15.3.2 Source Analysis
15.3.3 Author Analysis
15.3.4 Country Analysis
15.3.5 Keyword Analysis
15.3.6 Thematic Maps Analysis
15.3.7 Trending Topics
15.3.8 Gap Analysis and Future Research Directions
15.4 Conclusion
References
16. The Convergence of AI and ESG in Financial Decision-Making: A Roadmap for Sustainable Fintech Solutions
Priya Sachdeva and Archan Mitra
16.1 Introduction
16.1.1 Background
16.1.2 Problem Statement
16.1.3 Objectives of the Study
16.1.4 Scope and Significance
16.2 Literature Review
16.2.1 Evolution of ESG Frameworks in Financial Systems
16.2.2 Applications of AI in Sustainable Finance
16.2.3 Challenges in ESG Implementation
16.2.4 Ethical Considerations in AI for ESG
16.2.5 Case Studies of AI-Driven ESG Implementations
16.2.6 Emerging Trends in AI and ESG
16.2.7 Research Gaps
16.3 Research Methodology
16.3.1 Research Design
16.3.2 Data Collection Methods
16.3.2.1 Secondary Data
16.3.2.2 Case Studies
16.3.3 Data Analysis Techniques
16.3.3.1 Thematic Analysis
16.3.3.2 Quantitative Analysis
16.3.4 Limitations of the Study
16.4 Case Studies
16.4.1 BlackRock’s Aladdin Platform
16.4.2 MSCI Inc. ESG Ratings
16.4.3 TruValue Labs’ AI Tools
16.4.4 Emerging AI Platforms for ESG Integration
16.5 Data Analysis
16.5.1 ESG Performance Scores Pre- and Post-AI Integration
16.5.2 Greenwashing Detection Accuracy
16.5.3 Regional Analysis of ESG Improvements
16.5.4 Qualitative Analysis
16.5.4.1 Thematic Analysis of Case Studies
16.6 Findings
16.6.1 Improved ESG Performance
16.6.2 Enhanced Greenwashing Detection
16.6.3 Regional and Sectoral Insights
16.6.4 Real-Time Monitoring and Operational Scalability
16.6.5 Ethical and Transparency Challenges
16.6.6 Validation and Limitations
16.7 Actionable Strategies to Effectively Leverage AI in ESG Practices
16.7.1 Strategies for Financial Institutions
16.7.1.1 Adopt Explainable AI (XAI)
16.7.1.2 Leverage Real-Time ESG Monitoring
16.7.1.3 Build Cross-Functional AI-ESG Teams
16.7.2 Strategies for Regulators
16.7.2.1 Develop Standardized ESG Reporting Frameworks
16.7.2.2 Promote AI-Specific ESG Regulations
16.7.2.3 Establish AI-ESG Sandboxes
16.7.3 Strategies for Policymakers
16.7.3.1 Incentivize AI-Driven ESG Innovations
16.7.3.2 Facilitate Global Collaboration
16.7.3.3 Address Ethical and Social Implications
16.8 Conclusion
References
17. Revolutionizing Sustainable Energy Financing: The Role of Decentralized Finance (DeFi) in an AI-Driven Financial Landscape
Gagandeep Singh, Jasdeep Singh Walia and Nancy Sahni
17.1 Introduction
17.2 Review of Literature
17.2.1 Disruptive Potential of Decentralized Finance
17.2.2 Funding Prototypes in Sustainable Energy Financing
17.2.3 DeFi’s Impact on Sustainable Energy Financing
17.2.4 Challenges and Considerations for DeFi Adoption in Sustainable Energy
17.3 Impediments to Mainstream DeFi Adoption
17.3.1 Regulatory Uncertainty Leads to the Knowledge Gap
17.3.2 Regulatory Uncertainty Leads to Interoperability Challenges
17.3.3 Access Barriers Lead to the Knowledge Gap
17.3.4 Technical Complexity Leads to Access Barriers
17.3.5 Centralization Risks Lead to Technical Complexity
17.3.6 Centralization Risks Lead to Security Risks
17.3.7 Technical Complexity Leads to Trust Concerns
17.3.8 Access Barriers Lead to Trust Concerns
17.3.9 Security Risks Lead to Trust Concerns
17.3.10 Governance Issues in the DeFi Ecosystem
17.4 Roadmap for Seamless DeFi Adoption
17.5 Implications of the Study
17.5.1 Establishment of Comprehensive Regulatory Framework
17.5.2 Addressing Uncertainties Governing DeFi Platforms
17.5.3 Fostering Inclusive Sustainability and Reliability
17.5.4 Creating an Adaptive Approach to DeFi Regulations
17.5.5 Supporting Long-Term Viability of DeFi Projects
17.5.6 Balancing Decentralization with Accountability
17.5.7 Facilitating Sustainable Investment Opportunities
17.6 Conclusion
References
18. Ethical Finance in the Age of AI: Balancing Privacy and Security
Yatisha Kalia, Gitika Arora and C.A. Nitin Arora
18.1 Introduction
18.2 Role of AI in Finance
18.3 Literature Review
18.4 Ethical Dilemmas in Financial AI
18.4.1 Privacy Issues
18.4.2 Security Issues
18.5 Balancing Privacy and Security
18.5.1 Framework for Balancing Privacy and Security in AI-Driven Financial System
18.5.1.1 Ethical Foundation
18.5.1.2 Strategies for Balancing Privacy and Security
18.5.1.3 Building Trust through Ethical AI
18.6 Regulatory and Legal Landscape
18.7 Case Studies
18.7.1 Case Study 1: Apple Pay—A Privacy-Centric Financial Institution
18.7.2 Case Study 2: AI-Based Fraud Detection System of PayPal
18.8 Discussion
18.9 Conclusion
References
Index

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