Preface xix Part I: Introduction to AI in Finance 1 1 The Rise of Artificial Intelligence in the Indian Banking Industry 3 Bhavna Sharma, Renu Bala and Meenakshi 1.1 Introduction 4 1.2 Previous Research 6 1.3 Applications of AI in Banking Sector 10 1.4 How Banks are Using AI 14 1.5 Challenges Face by the Banks in AI 17 1.6 Implications of the Study 17 2 The Evolution of Finance and the Rise of AI 25 Sonal Trivedi and Mufaro Dzingirai 2.1 Introduction 26 2.

2 Historical Evolution of Finance 28 2.3 AI Applications in Financial Services 30 2.4 Impact of AI on Financial Markets 32 2.5 Conclusion 35 3 Harnessing Artificial Intelligence for Sustainable Development and Empowering Social Finance Systems 39 Ram Singh, Vinay Pal Singh and Ajit Singh Tomar 3.1 Introduction 40 3.2 Review of Literature 44 3.3 Objectives and Methodology 47 3.4 Artificial Intelligence in Sustainable Development of Social Finance Systems 47 3.

5 Systemic Inefficiencies in Resource Allocation and Social Impact Financing 50 3.6 Examining the Ethical Implications of AI Deployment in Social Finance Systems 52 3.7 Impact on Financial Accessibility and Environmental Conservation 54 3.8 Challenges in Deploying AI for Sustainable Development in India 56 3.9 Opportunities for AI in Sustainable Development in India 57 3.10 Findings and Suggestions 58 3.11 Conclusion 60 3.12 Future Directions for Research 61 4 Benefits of Financial Inclusion to Financial Institutions, Technology Firms and the Government: Implications for Digital Financial Inclusion 65 Peterson K.

Ozili 4.1 Introduction 65 4.2 Conceptual Framework 67 4.3 Financial Inclusion Benefits to Financial Institutions, Technology Firms, and the Government 68 4.4 Criticism of Corporate and Government Interests in Financial Inclusion 72 4.5 Conclusion 73 Part II: AI in Investment Management 77 5 Revolutionizing Finance: Machine Learning for Smart Portfolio Optimization 79 Nitin Kulshrestha 5.1 Introduction 80 5.2 Review of Literature 82 5.

3 Quantitative Finance Applications 83 5.4 Role of Machine Learning in Quantitative Finance 84 5.5 Comparative Analysis of ML Models 86 5.6 Algorithms for Smart Portfolio Management 88 5.7 Application of Artificial Intelligence in Algorithmic Trading 90 5.8 Concerns and Considerations 92 5.9 Future Directions of Algorithmic Trading and Portfolio Optimization 95 5.10 Challenges in Algorithmic Trading and Portfolio Optimization 97 5.

11 Conclusion 100 6 Dependency of Machine Learning Based Prediction Performance on Market Specificity, Window Length, and Algorithm Choice in the Context of Global Stock Indices 105 Jasleen Kaur and Khushdeep Dharni 6.1 Introduction 106 6.2 Background of the Study 108 6.3 Materials and Methods 109x Contents 6.4 Results 117 6.5 Discussion 124 6.6 Conclusion 125 7 Analysis of Portfolio Using Machine Learning for Selected ESG Companies in India: A PCA Approach 133 Kapil Shrimal, Priyanka Mathur and Nidhi Solanki 7.1 Introduction 134 7.

2 Background of the Study 136 7.3 Literature Review 137 7.4 Research Gap 139 7.5 Research Questions 140 7.6 Purpose of the Study 140 7.7 ESG in Investment Decisions 142 7.8 Principal Component Analysis (PCA) in Financial Analysis 145 7.9 Research Methodology 148 7.

10 Data Analysis and Results 148 7.11 Discussion 158 7.12 Practical Implications for Investors and Portfolio Managers 160 7.13 Limitations of the Study and Areas for Improvement 160 7.14 Upcoming Research Plans in the Domain of Machine Learning in the Field of ESG Investing 161 7.15 Recommendations 162 7.16 Conclusion 162 8 Detecting Information Flow Between Cryptocurrencies Using Transfer Entropy 167 Faniza Joshi, Shivani Inder and Manisha Dheer 8.1 Introduction 168xii Contents 8.

2 Real-Time Information Sharing 170 8.3 Data 176 8.4 Findings 177 8.5 Conclusion 187 Part III: AI in Risk Management and Compliance 193 9 Combat against Financial Fraud in Indian Banking Sector: A Cyber Security Measures in the Era of AI 195 Bhaveshkumar Parmar and Hetal Thaker 9.1 Overview of Financial Fraud in the Indian Banking Sector 196 9.2 Literature Review 197 9.3 Observed Financial Frauds in Indian Banking 199 9.4 Understanding Industry 5.

0 and Its Implications 206 9.5 Cybersecurity Measures for Industry 5.0 208 10 Potential of Ethical AI in Detection of Insurance Claim Fraud 217 Shefali Saluja and Preeti Kaushal 10.1 Introduction 218 10.2 Literature Review 220Contents xiii 10.3 Research Methodology 227 10.4 Discussion and Analysis 227 10.5 Conclusion 231 10.

6 Implications of the Study 233 11 Ensuring Ethical Adoption of AI: Addressing Bias and Fairness in Financial Services 237 Jeevesh Sharma and Swati Jain 11.1 Introduction 238 11.2 AI Adoption in Financial Services 240 11.3 Ethical AI''s Significance in Finance 242 11.4 Challenges and Risks of Bias 244 11.5 Ethical Concerns and Risks Associated with AI Implementation in Financial Services 246 11.6 Effect of Ethical Concerns in Ai on Stakeholders 248 11.7 Solutions to Address Ethical Concerns in AI-Powered Financial Services 249 11.

8 Regulatory Aspect for Ethical AI in Financial Services 249 11.9 Strategies and Tactics for Executing Ethical AI in Financial Services 250 11.10 Conclusion 250 Part IV: AI in Customer Engagement and Personalization 253 12 AI in Customer Engagement and Personalization 255 Kanthavel R., Adline Freeda R. and Dhaya R. 12.1 Introduction 256 12.2 Credit Risk Assessment with AI 256 12.

3 Fraud Detection and Prevention in Digital Age 258 12.4 Navigating Regulatory Compliance with AI Solutions 261 12.5 Conclusion 264 13 AI-Driven Financial Amenities: Exploring Customer Satisfaction and Behavioural Magnitudes Towards the Return Expectations in Equity Markets 267 Aparna Vajpayee 13.1 Introduction 268 13.2 Review of Literature 269 13.3 Research Gap 272 13.4 Research Methodology 273 13.5 Analysis 276 13.

6 Key Outcomes 281 13.7 Discussion 282 13.8 Implications for Practice 283 13.9 Conclusion 283 Part V: Ethical and Future Considerations 291 14 Ethical Frontiers: Navigating AI''s Integration in the Behavioral Finance 293 Dilpreet Kaur, Arun Sharma, Supriya Bajaj and Bisman Kaur 14.1 Introduction 293 14.2 Literature Review 294 14.3 Objectives 296 14.4 Challenges of Bias in AI Models 296 14.

5 Impact of Bias on Financial Decision Making 299 14.6 Emerging Trends in AI and Finance 302 14.7 Key Findings 304 14.8 Implications 304 14.9 Conclusion 304 14.10 Scope for Future Research 305 15 From Data to Decisions: A Bibliometric Analysis of the Role of Artificial Intelligence in Financial Innovation 309 Vimmy Bajaj and Vijaya Patil 15.1 Introduction 310 15.2 Research Methodology and Data Collection 313 15.

3 Bibliometric Analysis and Discussion 314 15.4 Conclusion 331 16 The Convergence of AI and ESG in Financial Decision-Making: A Roadmap for Sustainable Fintech Solutions 335 Priya Sachdeva and Archan Mitra 16.1 Introduction 336 16.2 Literature Review 339 16.3 Research Methodology 344 16.4 Case Studies 346 16.5 Data Analysis 349 16.6 Findings 353 16.

7 Actionable Strategies to Effectively Leverage AI in ESG Practices 355 16.8 Conclusion 358 17 Revolutionizing Sustainable Energy Financing: The Role of Decentralized Finance (DeFi) in an AI-Driven Financial Landscape 363 Gagandeep Singh, Jasdeep Singh Walia and Nancy Sahni 17.1 Introduction 364 17.2 Review of Literature 365 17.3 Impediments to Mainstream DeFi Adoption 369 17.4 Roadmap for Seamless DeFi Adoption 378 17.5 Implications of the Study 380 17.6 Conclusion 383 18 Ethical Finance in the Age of AI: Balancing Privacy and Security 387 Yatisha Kalia, Gitika Arora and C.

A. Nitin Arora 18.1 Introduction 388 18.2 Role of AI in Finance 389 18.3 Literature Review 390 18.4 Ethical Dilemmas in Financial AI 391 18.5 Balancing Privacy and Security 394 18.6 Regulatory and Legal Landscape 398 18.

7 Case Studies 399 18.8 Discussion 402 18.9 Conclusion 403 References 404 Index 409.