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Submit a ProposalArtificial Intelligence for Risk Mitigation in the Financial Industry
 | Edited by Ambrish Kumar Mishra, Shweta Anand, Narayan C. Debnath, Purvi Pokhariyal, Archana Patel Copyright: 2024 | Status: Published ISBN: 9781394174713 | Hardcover | 374 pages Price: $195 USD  |
One Line Description
This book extensively explores the implementation of AI in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability.
Audience
This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come.
This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come.
Description
The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc.
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The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc.
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Author / Editor Details
Ambrish Kumar Mishra is a scholar in the School of Management at Gautam Buddha University in Greater Noida, Uttar Pradesh, India. He obtained his Master’s in banking services from Amity University Noida, India in 2014, and has spent six years in the banking industry teaching and as a mutual fund and GST trainer with the BFSI sector skill council in India. He has published research papers and received various awards in his field of research.
Ambrish Kumar Mishra is a scholar in the School of Management at Gautam Buddha University in Greater Noida, Uttar Pradesh, India. He obtained his Master’s in banking services from Amity University Noida, India in 2014, and has spent six years in the banking industry teaching and as a mutual fund and GST trainer with the BFSI sector skill council in India. He has published research papers and received various awards in his field of research.
Shweta Anand, PhD, is the dean of the School of Management at Gautam Buddha University in Greater Noida, Uttar Pradesh, India. She earned a PhD in Wealth Management and has 30+ years of experience of which 14 years were in industry. She has won several awards and accolades and has published numerous papers in international and national journals and conferences.
Narayan C. Debnath, PhD, is the Founding Dean of the School of Computing and Information Technology at Eastern International University (EIU), Vietnam where he also serves as the Head of the Department of Software Engineering. He has been the Director of the International Society for Computers and their Applications (ISCA), USA since 2014 as well as serving as a Full Professor of Computer Science at Winona State University, Minnesota, USA for 28 years. He is the author or co-author of more than 500 publications in numerous refereed journals and conference proceedings as well as the editor of several books.
Purvi Pokhariyal, PhD, is the Campus Director at the National Forensic Sciences University of the Delhi Campus, India. She has more than 25 years of academic and industry experience in the field of law and justice administration. She has published about 50 papers in national and international journals and conferences.
Archana Patel, PhD, is a faculty member of the Department of Software Engineering in the School of Computing and Information Technology at Eastern International University in Binh Duong Province, Vietnam. She completed her PhD in Computer Applications and a PG degree from the National Institute on Technology in Kurukshetra, India in 2020 and 2016 respectively. Dr. Patel has received various awards for her presentation of research work, and published 30 papers in peer-reviewed journals and conferences. Her research interests are in ontological engineering, semantic web, big data, expert systems, and knowledge warehouses.
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Table of Contents
Preface
1. Artificial Intelligence in Risk Management
Pankaj Yadav, Priya Gupta, Rajeev Sijariya and Yogesh Sharma
1.1 Introduction
1.1.1 Context and the Driving Force Behind It
1.1.2 Aim of This Chapter
1.1.3 Outline of This Chapter
1.2 The Role of AI in Risk Management
1.2.1 The Significance of Risk Management
1.2.2 Deficiencies in Conventional Methods of Risk Management
1.2.3 The Requirement for Advanced Methods
1.3 Role of Artificial Intelligence in Risk Management
1.3.1 An Overview of Artificial Intelligence and Its Applications
1.3.2 Applications of AI-Based Methods in Risk Management
1.4 The Challenges of Implementing AI-Based Risk Management Systems
1.5 The Benefits of Using Artificial Intelligence in Risk Management
1.6 Conclusions and Future Considerations of AI in Risk Management
1.6.1 A Brief Review of the Role of AI in Risk Management
1.6.2 Perspectives on the Future
1.6.3 The Transformative Power of AI in Risk Management
1.7 The Implications and Factors to Take Into Account While Using AI in Risk Management
1.8 Overcoming Obstacles and Putting AI to Work in Risk Management
1.9 Conclusion
References
2. Application of Artificial Intelligence in Risk Assessment and Mitigation in Banks
Ankita Srivastava, Bhartrihari Pandiya and Navtika Singh Nautiyal
2.1 Introduction
2.2 Transitions in Banking Due to AI
2.3 Risk Assessment and Mitigation through Artificial Intelligence
2.3.1 Fraud Recognition
2.3.2 Regulatory Compliance Management
2.3.3 Credit Risk Modeling
2.3.4 Insider Threat Prevention
2.4 General Banking Regulations Pertaining to Artificial Intelligence
2.5 Methodology
2.5.1 Bibliometric Analysis
2.5.2 Co-Occurrence Analysis
2.6 Theoretical Implications
2.7 Managerial Implications
2.8 Future Scope
2.9 Conclusion
References
3. Artificial Intelligence and Financial Risk Mitigation
Raja Rehan, Auwal Adam Sa’ad and Razali Haron
3.1 Introduction
3.2 Artificial Intelligence, Financial Sector, and Risk Mitigation
3.2.1 AI and Financial Risk Detection Processes
3.2.2 AI and Financial Risk Recognition Techniques
3.2.2.1 Risk Assessment and Prediction
3.2.2.2 Fraud Detection and Anticipation
3.2.2.3 Risk Modeling and Stress Testing
3.2.2.4 Portfolio Optimization and Asset Allocation
3.2.2.5 Regulatory Compliance
3.2.2.6 Cybersecurity and Data Privacy
3.2.2.7 Chatbots and Customer Service
3.2.2.8 Loan Underwriting and Processing
3.3 Financial Risks and AI Mitigation Practices
3.3.1 Credit Risk and Artificial Intelligence
3.3.2 Market Risk and Artificial Intelligence
3.3.3 Liquidity Risk and Artificial Intelligence
3.3.4 Operation Risk and Artificial Intelligence
3.3.5 Compliance Risk and Artificial Intelligence
3.4 AI and Financial Risk Mitigation Procedures
3.4.1 Identification and Assessment of Risks
3.4.2 Risk Prioritization
3.4.3 Developing Risk Mitigation Policies
3.4.4 Implementation of Risk Control Policies
3.4.5 Monitor and Evaluate Risk Mitigation Procedures
3.4.6 Testing and Validation
3.4.7 Continuously Improving the Risk Mitigation Process
3.5 Conclusion
References
4. Artificial Intelligence Adoption in the Indian Banking and Financial Industry: Current Status and Future Opportunities
Deepthi B. and Vikram Bansal
4.1 Introduction
4.2 Literature Review
4.2.1 Introduction to AI
4.2.2 Applications of Artificial Intelligence
4.2.2.1 AI Applications in e-Commerce
4.2.2.2 Applications of AI in Education
4.2.2.3 AI Applications in Agriculture
4.2.2.4 Artificial Intelligence in the Banking and Financial Industry
4.2.2.5 Utilization of AI in the Indian Banking and Financial Industry
4.3 Research Methodology
4.4 Findings of the Study
4.4.1 Current Status of AI-Based Application Adoption in the Indian Banking and Financial Industry
4.4.2 Future Opportunities in the Adoption of AI-Based Applications in the Indian Banking and Financial Industry
4.4.3 Challenges to the Deployment of AI in the Indian Banking and Financial Services Industry
4.5 Conclusion
References
5. Impact of AI Adoption in Current Trends of the Financial Industry
S. C. Vetrivel, T. Mohanasundaram, T. P. Saravanan and R. Maheswari
5.1 Introduction
5.1.1 Brief Overview of AI Technology
5.1.2 Importance of AI Adoption in the Financial Industry
5.1.3 Impact of AI on Traditional Financial Services
5.2 AI-Based Trading and Investment Management
5.2.1 Role of AI in Trading and Investment Management
5.2.2 AI-Powered Robot-Advisory Services
5.2.3 AI-Based Risk Management and Portfolio Optimization
5.3 Fraud Detection and Prevention
5.3.1 Role of AI in Fraud Detection and Prevention
5.3.2 Real-Time Fraud Monitoring Using AI
5.3.3 Machine Learning-Based Fraud Prevention Techniques
5.4 Customer Service and Personalization
5.4.1 AI-Powered Chatbots for Customer Service
5.4.2 Personalized Recommendations and Offerings Using AI
5.5 Compliance and Regulatory Reporting
5.5.1 Streamlining Regulatory Reporting with AI
5.5.2 Risk Assessment and Compliance Monitoring Using AI
5.6 Impact of AI on Employment in the Financial Industry
5.6.1 Potential Job Displacement Due to AI Adoption
5.6.2 Opportunities for New Roles and Skills in the Industry
5.6.3 The Need for Reskilling and Upskilling the Workforce
5.7 Ethical and Social Implications of AI Adoption
5.7.1 Ensuring Transparency and Accountability in AI Decision-Making
5.7.2 Ethical Concerns Around AI Adoption in Finance
5.7.3 Addressing Potential Biases and Discrimination in AI-Based Financial Services
5.8 Future of AI Adoption in the Financial Industry
5.8.1 Emerging Trends and Technologies in AI Adoption in Finance
5.8.2 Opportunities for Innovation and Growth in the Industry
5.8.3 Challenges and Limitations to Widespread Adoption of AI
5.9 Case Studies on AI Adoption in the Financial Industry
5.9.1 ICICI Bank
5.9.2 HDFC Bank
5.9.3 Bank of America
5.9.4 Real-World Examples of Successful AI Adoption in Finance
5.9.5 Impact of AI on Business Operations and Customer Experience
5.9.6 Lessons Learned From AI Implementation in the Financial Industry
5.10 Conclusion and Future Directions
5.10.1 Summary of the Key Findings and Insights from the Chapter
5.10.2 Recommendations for Future Research and Development in AI Adoption in Finance
5.10.3 The Role of Policymakers, Regulators, and Industry Leaders in Shaping the Future of AI in Finance
5.11 Conclusion
References
6. Artificial Intelligence Applications in the Indian Financial Ecosystem
Vijaya Kittu Manda and Khaliq Lubza Nihar
6.1 Introduction
6.2 Literature Review
6.3 Evolution: From Operations to Risk Management
6.4 Banking Services
6.5 Payment Systems
6.6 Digital Lending
6.7 Credit Scoring/Creditworthiness/Direct Lending
6.8 Stockbrokers and Wealth Management
6.9 Mutual Funds and Asset Management
6.10 Insurance Services
6.11 Indian Financial Regulators
6.12 Challenges in Adoption
6.13 Conclusion
References
7. The Extraction of Features That Characterize Financial Fraud Behavior by Machine Learning Algorithms
George X. Yuan, Yuanlei Luo, Lan Di, Yunpeng Zhou, Wen Chen, Yiming Liu and Yudi Gu
7.1 Introduction
7.2 The Framework of Gibbs Sampling Algorithm
7.2.1 The Summary of Gibbs Sampling Algorithm
7.2.2 The Framework of Associative Feature Extraction Method
7.3 The Framework in Screening Features for Corporate Financial Fraud Behaviors
7.3.1 The Framework of Holographic Risk Assessment Based on the CAFÉ System
7.3.2 The Structure of the Company’s Financial Fraud Early Warning Risk System
7.3.3 The Method for Extracting Financial Fraud Characteristics of Listed Companies
7.3.3.1 Static Analysis
7.3.3.2 Dynamic (Trend) Analysis
7.3.3.3 Comparison of Peers
7.3.3.4 The Insight for the Relationship Between Financial Statements and Audits
7.3.4 Feature Extraction Based on AUC and ROC Testing for Financial Frauds
7.3.5 The Corporate Governance Framework of Financial Fraud Indicators
7.4 The Case Study for Financial Frauds from Listed Companies
7.4.1 The Case Study Background
7.4.2 The Case Study by Qualitative Analysis
7.4.3 The Quantitative Analysis Based on the CAFÉ Risk Evaluation System
7.4.4 Case Study Results and Remark
7.5 Conclusion
Appendix A: The Description for Eight Types of Financial Frauds
Appendix B: The Summary of 12 Classes of Data Types in Describing Financial
Fraud Behaviors
References
8. A New Surge of Interest in the Cybersecurity of VIP Clients is the First Step Toward the Return of the Previously Used Positioning Practice in Domestic Private Banking
Gusev Alexey
8.1 Introduction
8.1.1 Cyber Hygiene
8.2 VIP Clients
8.3 Cyber Defense Against Simple Threats
8.4 Conclusion
References
9. Determinants of Financial Distress in Select Indian Asset Reconstruction Companies Using Artificial Neural Networks
Shashank Sharma and Ajay Kumar Kansal
Abbreviations
9.1 Introduction
9.2 Brief Review of Literature
9.3 Research Design
9.4 Data Analysis and Interpretation
9.4.1 Financial Ratio Analysis
9.4.2 Altman’s Z-Score Analysis
9.4.3 Determinants of Financial Distress in Indian ARCs—Analysis Using MLP-ANN
9.4.4 Impact of IBC, 2016, on the Capital Structure of Indian ARCs
9.5 Conclusion
References
Appendices
10. The Framework of Feature Extraction for Financial Fraud Behavior and Applications
George X. Yuan, Shanshan Yang, Lan Di, Yunpeng Zhou, Wen Chen and Yuanlei Luo
10.1 Introduction
10.2 The Feature Extraction for Financial Fraud Behaviors
10.2.1 Risk Characteristics of Financial Frauds from Listed Companies
10.2.2 The Method for the Identification of Financial Fraud Behavior Based on Financial Analysis
10.2.3 The Method for the Feature Extraction
10.2.4 Gibbs Sampling Method Under the MCMC
10.3 The Framework of Feature Extraction for Financial Fraud Behavior
10.3.1 The Initial Set of Characteristics that Characterizes Financial Fraud Behavior
10.3.2 Extracting Features Related to Companies’ Financial Fraud Behaviors
10.3.3 Features Extracted for Financial Fraud Behavior by AI Algorithms and Empirical Analysis
10.4 The Framework of Characterizations for Companies’ Financial Fraud Behavior
10.4.1 The Performance of the Extraction for Companies’ Financial Fraud Behavior Characteristics
10.4.2 The Characterization of Financial Fraud Behavior from Companies’ Governance Structure
10.5 Conclusion with Remarks
References
11. Real-Time Analysis of Banking Data with AI Technologies
S. C. Vetrivel, T. Mohanasundaram, T. P. Saravanan and R. Maheswari
11.1 Introduction
11.1.1 The Benefits of Real-Time Analysis of Banking Data with AI Technologies
11.1.2 Challenges in Applying AI Technologies to Banking Data
11.2 Data Collection and Preprocessing
11.3 Machine Learning Techniques for Real-Time Analysis
11.3.1 Machine Learning Techniques Used for Real-Time Analysis of Banking Data
11.3.2 Techniques Used to Evaluate the Accuracy of the Algorithms in Machine Learning for Real-Time Analysis
11.3.3 Challenges Associated with Applying Machine Learning Techniques to Banking Data
11.4 Natural Language Processing Techniques for Real-Time Analysis
11.4.1 Natural Language Processing Techniques Used for Real-Time Analysis of Banking Data
11.4.2 Techniques Used to Evaluate the Accuracy of the Algorithms for Real-Time Analysis of Banking Data
11.4.3 Challenges Associated with Applying Natural Language Processing Techniques to Banking Data
11.5 Deep Learning Techniques for Real-Time Analysis
11.6 Real-Time Visualization of Banking Data
11.6.1 Visualization Techniques Used to Visualize Banking Data in Real Time
11.6.2 Techniques Used to Evaluate the Accuracy of the Visualizations of Banking Data in Real Time
11.7 Real-Time Alerting Systems for Banking Data
11.7.1 Alerting Systems Used to Alert Users to Changes in Banking Data in Real Time
11.7.2 Techniques Used to Evaluate the Accuracy of Alerting Systems in Banking Data
11.7.3 Difficulties Associated with Implementing Real-Time Alerting Systems for Banking Data
11.8 Conclusion
References
12. Risks in Amalgamation of Artificial Intelligence with Other Recent Technologies
K. Sathya and A. Hency Juliet
12.1 Introduction
12.2 Risks of Artificial Intelligence in the Healthcare System
12.2.1 Automating Drudgery in Medical Practice
12.2.2 Managing Patients and Medical Resources
12.2.3 Risks and Challenges
12.3 Risks of Artificial Intelligence in Finance
12.3.1 Forecasting
12.3.2 Investment and Banking Services
12.3.3 Risk and Compliance Management
12.3.4 Prudential Supervision
12.3.5 Central Banking
12.3.6 Cybersecurity
12.3.7 Data Privacy
12.3.8 Impact on Financial Stability
12.4 Common Risks of Artificial Intelligence Techniques
12.4.1 Social Manipulation and AI Algorithms
12.4.2 Social Surveillance with AI Technology
12.4.3 Bias in Artificial Intelligence Systems
12.4.4 Expanding Socioeconomic Inequality as a Result of AI
12.4.5 Descending Goodwill and Ethics Because of AI
12.4.6 Autonomous Weapons Powered by Artificial Intelligence
12.4.7 Financial Implications of AI Algorithms
12.5 Risks in Smart Home Systems
12.5.1 Identity Theft
12.5.2 Password Exploitation
12.5.3 Location Tracking
12.5.4 Home Intrusions
12.5.5 Appliance or Property Damage
12.5.6 Unauthorized Recordings and Privacy Violations
12.5.7 External Vulnerabilities
12.5.8 Privacy Breaches and Data Exploitation
12.5.9 Unsupported Software
12.5.10 Hacking Through the Cloud
12.5.11 Batteries Drain Too Fast
12.5.12 The Hidden Risks of Granting Permissions
12.6 Risks of AI in Education
12.6.1 Quality Over Quantity
12.6.2 Stimulating Technology Addiction
12.6.3 The High Cost of Delivery
12.6.4 Unemployment
12.6.5 Power Imbalance and Control
12.6.6 Reducing the Ability to Multitask
12.6.7 No Alternative Teaching Methods
12.6.8 Enlarge the Gap Between the Rich and the Poor
12.6.9 Unclear Ability to Learn with a Virtual Assistant
12.6.10 Diminished Emotional Support and Mentorship
12.6.11 Loss of Information
12.7 Conclusion
References
13. Exploring the Role of ChatGPT in the Law Enforcement and Banking Sectors
Shubham Pandey, Archana Patel and Purvi Pokhariyal
13.1 Introduction
13.2 Leveraging ChatGPT in Law Enforcement and the Banking Sector
13.2.1 Banking Sector
13.2.2 Law Enforcement
13.3 Issues with Regard to ChatGPT
13.3.1 Threats Caused by ChatGPT Architecture and Inherent Flaws
13.3.2 ChatGPT Used as a Weapon of Crime
13.4 Regulations and Nonlegal Solutions to Address Crimes Relating to ChatGPT
13.4.1 Legal Regulations to Address the Misuse of ChatGPT
13.4.2 Cyber Awareness/Cyber Hygiene to Combat the Misuse of ChatGPT
13.5 Road Ahead for ChatGPT and Emerging Technologies
13.6 Conclusion
References
Index
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Preface
1. Artificial Intelligence in Risk Management
Pankaj Yadav, Priya Gupta, Rajeev Sijariya and Yogesh Sharma
1.1 Introduction
1.1.1 Context and the Driving Force Behind It
1.1.2 Aim of This Chapter
1.1.3 Outline of This Chapter
1.2 The Role of AI in Risk Management
1.2.1 The Significance of Risk Management
1.2.2 Deficiencies in Conventional Methods of Risk Management
1.2.3 The Requirement for Advanced Methods
1.3 Role of Artificial Intelligence in Risk Management
1.3.1 An Overview of Artificial Intelligence and Its Applications
1.3.2 Applications of AI-Based Methods in Risk Management
1.4 The Challenges of Implementing AI-Based Risk Management Systems
1.5 The Benefits of Using Artificial Intelligence in Risk Management
1.6 Conclusions and Future Considerations of AI in Risk Management
1.6.1 A Brief Review of the Role of AI in Risk Management
1.6.2 Perspectives on the Future
1.6.3 The Transformative Power of AI in Risk Management
1.7 The Implications and Factors to Take Into Account While Using AI in Risk Management
1.8 Overcoming Obstacles and Putting AI to Work in Risk Management
1.9 Conclusion
References
2. Application of Artificial Intelligence in Risk Assessment and Mitigation in Banks
Ankita Srivastava, Bhartrihari Pandiya and Navtika Singh Nautiyal
2.1 Introduction
2.2 Transitions in Banking Due to AI
2.3 Risk Assessment and Mitigation through Artificial Intelligence
2.3.1 Fraud Recognition
2.3.2 Regulatory Compliance Management
2.3.3 Credit Risk Modeling
2.3.4 Insider Threat Prevention
2.4 General Banking Regulations Pertaining to Artificial Intelligence
2.5 Methodology
2.5.1 Bibliometric Analysis
2.5.2 Co-Occurrence Analysis
2.6 Theoretical Implications
2.7 Managerial Implications
2.8 Future Scope
2.9 Conclusion
References
3. Artificial Intelligence and Financial Risk Mitigation
Raja Rehan, Auwal Adam Sa’ad and Razali Haron
3.1 Introduction
3.2 Artificial Intelligence, Financial Sector, and Risk Mitigation
3.2.1 AI and Financial Risk Detection Processes
3.2.2 AI and Financial Risk Recognition Techniques
3.2.2.1 Risk Assessment and Prediction
3.2.2.2 Fraud Detection and Anticipation
3.2.2.3 Risk Modeling and Stress Testing
3.2.2.4 Portfolio Optimization and Asset Allocation
3.2.2.5 Regulatory Compliance
3.2.2.6 Cybersecurity and Data Privacy
3.2.2.7 Chatbots and Customer Service
3.2.2.8 Loan Underwriting and Processing
3.3 Financial Risks and AI Mitigation Practices
3.3.1 Credit Risk and Artificial Intelligence
3.3.2 Market Risk and Artificial Intelligence
3.3.3 Liquidity Risk and Artificial Intelligence
3.3.4 Operation Risk and Artificial Intelligence
3.3.5 Compliance Risk and Artificial Intelligence
3.4 AI and Financial Risk Mitigation Procedures
3.4.1 Identification and Assessment of Risks
3.4.2 Risk Prioritization
3.4.3 Developing Risk Mitigation Policies
3.4.4 Implementation of Risk Control Policies
3.4.5 Monitor and Evaluate Risk Mitigation Procedures
3.4.6 Testing and Validation
3.4.7 Continuously Improving the Risk Mitigation Process
3.5 Conclusion
References
4. Artificial Intelligence Adoption in the Indian Banking and Financial Industry: Current Status and Future Opportunities
Deepthi B. and Vikram Bansal
4.1 Introduction
4.2 Literature Review
4.2.1 Introduction to AI
4.2.2 Applications of Artificial Intelligence
4.2.2.1 AI Applications in e-Commerce
4.2.2.2 Applications of AI in Education
4.2.2.3 AI Applications in Agriculture
4.2.2.4 Artificial Intelligence in the Banking and Financial Industry
4.2.2.5 Utilization of AI in the Indian Banking and Financial Industry
4.3 Research Methodology
4.4 Findings of the Study
4.4.1 Current Status of AI-Based Application Adoption in the Indian Banking and Financial Industry
4.4.2 Future Opportunities in the Adoption of AI-Based Applications in the Indian Banking and Financial Industry
4.4.3 Challenges to the Deployment of AI in the Indian Banking and Financial Services Industry
4.5 Conclusion
References
5. Impact of AI Adoption in Current Trends of the Financial Industry
S. C. Vetrivel, T. Mohanasundaram, T. P. Saravanan and R. Maheswari
5.1 Introduction
5.1.1 Brief Overview of AI Technology
5.1.2 Importance of AI Adoption in the Financial Industry
5.1.3 Impact of AI on Traditional Financial Services
5.2 AI-Based Trading and Investment Management
5.2.1 Role of AI in Trading and Investment Management
5.2.2 AI-Powered Robot-Advisory Services
5.2.3 AI-Based Risk Management and Portfolio Optimization
5.3 Fraud Detection and Prevention
5.3.1 Role of AI in Fraud Detection and Prevention
5.3.2 Real-Time Fraud Monitoring Using AI
5.3.3 Machine Learning-Based Fraud Prevention Techniques
5.4 Customer Service and Personalization
5.4.1 AI-Powered Chatbots for Customer Service
5.4.2 Personalized Recommendations and Offerings Using AI
5.5 Compliance and Regulatory Reporting
5.5.1 Streamlining Regulatory Reporting with AI
5.5.2 Risk Assessment and Compliance Monitoring Using AI
5.6 Impact of AI on Employment in the Financial Industry
5.6.1 Potential Job Displacement Due to AI Adoption
5.6.2 Opportunities for New Roles and Skills in the Industry
5.6.3 The Need for Reskilling and Upskilling the Workforce
5.7 Ethical and Social Implications of AI Adoption
5.7.1 Ensuring Transparency and Accountability in AI Decision-Making
5.7.2 Ethical Concerns Around AI Adoption in Finance
5.7.3 Addressing Potential Biases and Discrimination in AI-Based Financial Services
5.8 Future of AI Adoption in the Financial Industry
5.8.1 Emerging Trends and Technologies in AI Adoption in Finance
5.8.2 Opportunities for Innovation and Growth in the Industry
5.8.3 Challenges and Limitations to Widespread Adoption of AI
5.9 Case Studies on AI Adoption in the Financial Industry
5.9.1 ICICI Bank
5.9.2 HDFC Bank
5.9.3 Bank of America
5.9.4 Real-World Examples of Successful AI Adoption in Finance
5.9.5 Impact of AI on Business Operations and Customer Experience
5.9.6 Lessons Learned From AI Implementation in the Financial Industry
5.10 Conclusion and Future Directions
5.10.1 Summary of the Key Findings and Insights from the Chapter
5.10.2 Recommendations for Future Research and Development in AI Adoption in Finance
5.10.3 The Role of Policymakers, Regulators, and Industry Leaders in Shaping the Future of AI in Finance
5.11 Conclusion
References
6. Artificial Intelligence Applications in the Indian Financial Ecosystem
Vijaya Kittu Manda and Khaliq Lubza Nihar
6.1 Introduction
6.2 Literature Review
6.3 Evolution: From Operations to Risk Management
6.4 Banking Services
6.5 Payment Systems
6.6 Digital Lending
6.7 Credit Scoring/Creditworthiness/Direct Lending
6.8 Stockbrokers and Wealth Management
6.9 Mutual Funds and Asset Management
6.10 Insurance Services
6.11 Indian Financial Regulators
6.12 Challenges in Adoption
6.13 Conclusion
References
7. The Extraction of Features That Characterize Financial Fraud Behavior by Machine Learning Algorithms
George X. Yuan, Yuanlei Luo, Lan Di, Yunpeng Zhou, Wen Chen, Yiming Liu and Yudi Gu
7.1 Introduction
7.2 The Framework of Gibbs Sampling Algorithm
7.2.1 The Summary of Gibbs Sampling Algorithm
7.2.2 The Framework of Associative Feature Extraction Method
7.3 The Framework in Screening Features for Corporate Financial Fraud Behaviors
7.3.1 The Framework of Holographic Risk Assessment Based on the CAFÉ System
7.3.2 The Structure of the Company’s Financial Fraud Early Warning Risk System
7.3.3 The Method for Extracting Financial Fraud Characteristics of Listed Companies
7.3.3.1 Static Analysis
7.3.3.2 Dynamic (Trend) Analysis
7.3.3.3 Comparison of Peers
7.3.3.4 The Insight for the Relationship Between Financial Statements and Audits
7.3.4 Feature Extraction Based on AUC and ROC Testing for Financial Frauds
7.3.5 The Corporate Governance Framework of Financial Fraud Indicators
7.4 The Case Study for Financial Frauds from Listed Companies
7.4.1 The Case Study Background
7.4.2 The Case Study by Qualitative Analysis
7.4.3 The Quantitative Analysis Based on the CAFÉ Risk Evaluation System
7.4.4 Case Study Results and Remark
7.5 Conclusion
Appendix A: The Description for Eight Types of Financial Frauds
Appendix B: The Summary of 12 Classes of Data Types in Describing Financial
Fraud Behaviors
References
8. A New Surge of Interest in the Cybersecurity of VIP Clients is the First Step Toward the Return of the Previously Used Positioning Practice in Domestic Private Banking
Gusev Alexey
8.1 Introduction
8.1.1 Cyber Hygiene
8.2 VIP Clients
8.3 Cyber Defense Against Simple Threats
8.4 Conclusion
References
9. Determinants of Financial Distress in Select Indian Asset Reconstruction Companies Using Artificial Neural Networks
Shashank Sharma and Ajay Kumar Kansal
Abbreviations
9.1 Introduction
9.2 Brief Review of Literature
9.3 Research Design
9.4 Data Analysis and Interpretation
9.4.1 Financial Ratio Analysis
9.4.2 Altman’s Z-Score Analysis
9.4.3 Determinants of Financial Distress in Indian ARCs—Analysis Using MLP-ANN
9.4.4 Impact of IBC, 2016, on the Capital Structure of Indian ARCs
9.5 Conclusion
References
Appendices
10. The Framework of Feature Extraction for Financial Fraud Behavior and Applications
George X. Yuan, Shanshan Yang, Lan Di, Yunpeng Zhou, Wen Chen and Yuanlei Luo
10.1 Introduction
10.2 The Feature Extraction for Financial Fraud Behaviors
10.2.1 Risk Characteristics of Financial Frauds from Listed Companies
10.2.2 The Method for the Identification of Financial Fraud Behavior Based on Financial Analysis
10.2.3 The Method for the Feature Extraction
10.2.4 Gibbs Sampling Method Under the MCMC
10.3 The Framework of Feature Extraction for Financial Fraud Behavior
10.3.1 The Initial Set of Characteristics that Characterizes Financial Fraud Behavior
10.3.2 Extracting Features Related to Companies’ Financial Fraud Behaviors
10.3.3 Features Extracted for Financial Fraud Behavior by AI Algorithms and Empirical Analysis
10.4 The Framework of Characterizations for Companies’ Financial Fraud Behavior
10.4.1 The Performance of the Extraction for Companies’ Financial Fraud Behavior Characteristics
10.4.2 The Characterization of Financial Fraud Behavior from Companies’ Governance Structure
10.5 Conclusion with Remarks
References
11. Real-Time Analysis of Banking Data with AI Technologies
S. C. Vetrivel, T. Mohanasundaram, T. P. Saravanan and R. Maheswari
11.1 Introduction
11.1.1 The Benefits of Real-Time Analysis of Banking Data with AI Technologies
11.1.2 Challenges in Applying AI Technologies to Banking Data
11.2 Data Collection and Preprocessing
11.3 Machine Learning Techniques for Real-Time Analysis
11.3.1 Machine Learning Techniques Used for Real-Time Analysis of Banking Data
11.3.2 Techniques Used to Evaluate the Accuracy of the Algorithms in Machine Learning for Real-Time Analysis
11.3.3 Challenges Associated with Applying Machine Learning Techniques to Banking Data
11.4 Natural Language Processing Techniques for Real-Time Analysis
11.4.1 Natural Language Processing Techniques Used for Real-Time Analysis of Banking Data
11.4.2 Techniques Used to Evaluate the Accuracy of the Algorithms for Real-Time Analysis of Banking Data
11.4.3 Challenges Associated with Applying Natural Language Processing Techniques to Banking Data
11.5 Deep Learning Techniques for Real-Time Analysis
11.6 Real-Time Visualization of Banking Data
11.6.1 Visualization Techniques Used to Visualize Banking Data in Real Time
11.6.2 Techniques Used to Evaluate the Accuracy of the Visualizations of Banking Data in Real Time
11.7 Real-Time Alerting Systems for Banking Data
11.7.1 Alerting Systems Used to Alert Users to Changes in Banking Data in Real Time
11.7.2 Techniques Used to Evaluate the Accuracy of Alerting Systems in Banking Data
11.7.3 Difficulties Associated with Implementing Real-Time Alerting Systems for Banking Data
11.8 Conclusion
References
12. Risks in Amalgamation of Artificial Intelligence with Other Recent Technologies
K. Sathya and A. Hency Juliet
12.1 Introduction
12.2 Risks of Artificial Intelligence in the Healthcare System
12.2.1 Automating Drudgery in Medical Practice
12.2.2 Managing Patients and Medical Resources
12.2.3 Risks and Challenges
12.3 Risks of Artificial Intelligence in Finance
12.3.1 Forecasting
12.3.2 Investment and Banking Services
12.3.3 Risk and Compliance Management
12.3.4 Prudential Supervision
12.3.5 Central Banking
12.3.6 Cybersecurity
12.3.7 Data Privacy
12.3.8 Impact on Financial Stability
12.4 Common Risks of Artificial Intelligence Techniques
12.4.1 Social Manipulation and AI Algorithms
12.4.2 Social Surveillance with AI Technology
12.4.3 Bias in Artificial Intelligence Systems
12.4.4 Expanding Socioeconomic Inequality as a Result of AI
12.4.5 Descending Goodwill and Ethics Because of AI
12.4.6 Autonomous Weapons Powered by Artificial Intelligence
12.4.7 Financial Implications of AI Algorithms
12.5 Risks in Smart Home Systems
12.5.1 Identity Theft
12.5.2 Password Exploitation
12.5.3 Location Tracking
12.5.4 Home Intrusions
12.5.5 Appliance or Property Damage
12.5.6 Unauthorized Recordings and Privacy Violations
12.5.7 External Vulnerabilities
12.5.8 Privacy Breaches and Data Exploitation
12.5.9 Unsupported Software
12.5.10 Hacking Through the Cloud
12.5.11 Batteries Drain Too Fast
12.5.12 The Hidden Risks of Granting Permissions
12.6 Risks of AI in Education
12.6.1 Quality Over Quantity
12.6.2 Stimulating Technology Addiction
12.6.3 The High Cost of Delivery
12.6.4 Unemployment
12.6.5 Power Imbalance and Control
12.6.6 Reducing the Ability to Multitask
12.6.7 No Alternative Teaching Methods
12.6.8 Enlarge the Gap Between the Rich and the Poor
12.6.9 Unclear Ability to Learn with a Virtual Assistant
12.6.10 Diminished Emotional Support and Mentorship
12.6.11 Loss of Information
12.7 Conclusion
References
13. Exploring the Role of ChatGPT in the Law Enforcement and Banking Sectors
Shubham Pandey, Archana Patel and Purvi Pokhariyal
13.1 Introduction
13.2 Leveraging ChatGPT in Law Enforcement and the Banking Sector
13.2.1 Banking Sector
13.2.2 Law Enforcement
13.3 Issues with Regard to ChatGPT
13.3.1 Threats Caused by ChatGPT Architecture and Inherent Flaws
13.3.2 ChatGPT Used as a Weapon of Crime
13.4 Regulations and Nonlegal Solutions to Address Crimes Relating to ChatGPT
13.4.1 Legal Regulations to Address the Misuse of ChatGPT
13.4.2 Cyber Awareness/Cyber Hygiene to Combat the Misuse of ChatGPT
13.5 Road Ahead for ChatGPT and Emerging Technologies
13.6 Conclusion
References
Index
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