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Submit a ProposalGenerative Artificial Intelligence in Finance
 | Large Language Models, Interfaces, and Industry Use Cases to Transform Accounting and Finance Processes Edited by Pethuru Raj Chelliah, Pushan Kumar Dutta, Abhishek Kumar, Ernesto D.R. Santibanez Gonzalez, Mohit Mittal and Sachin Gupta Series: Fintech in a Sustainable Digital Society Copyright: 2025 | Status: Published ISBN: 9781394271047 | Hardcover | 498 pages Price: $225 USD  |
One Line Description
This comprehensive volume delves deep into the diverse applications and implications of generative AI across accounting, finance, economics, business, and management, providing readers with a holistic understanding of this rapidly evolving landscape.
Audience
The audience for this book is quite diverse, ranging from financial and accounting experts across banking, insurance, consultancies, regulatory agencies, and corporations seeking to enhance productivity and efficiency; business leaders want to implement ethical and compliant AI practices; researchers exploring the domain of AI and finance.
The audience for this book is quite diverse, ranging from financial and accounting experts across banking, insurance, consultancies, regulatory agencies, and corporations seeking to enhance productivity and efficiency; business leaders want to implement ethical and compliant AI practices; researchers exploring the domain of AI and finance.
Description
Generative AI and Finance: Unleashing the Potential of Large Language Models, Frameworks, Interfaces, and Industry Cases to Transform Accounting and Finance Processes provides a comprehensive guide to ethically harnessing generative AI systems to reshape financial management. Generative AI is a key theme across the accounting and finance sectors to drive significant optimizations leading to sustainability. Across 22 chapters, leading researchers supply innovative applications of large language models across the economic realm. Through detailed frameworks, real-world case studies, and governance recommendations, this book highlights applied research for generative AI in finance functions. Several chapters demonstrate how data-driven insights from AI systems can optimize complex financial processes to reduce resource usage, lower costs, and drive positive environmental impact over the long term. In addition, chapters on AI-enabled risk assessment, fraud analytics, and regulatory technology highlight applied research for generative AI in finance. The book also explores emerging applications like leveraging blockchain and metaverse interfaces to create generative AI models that can revolutionize areas from carbon credit trading to virtual audits. Overall, with in-depth applied research at the nexus of sustainability and optimization enabled by data science and generative AI, the book offers a compilation of best practices in leveraging AI for optimal, ethical, and future-oriented financial management.
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Generative AI and Finance: Unleashing the Potential of Large Language Models, Frameworks, Interfaces, and Industry Cases to Transform Accounting and Finance Processes provides a comprehensive guide to ethically harnessing generative AI systems to reshape financial management. Generative AI is a key theme across the accounting and finance sectors to drive significant optimizations leading to sustainability. Across 22 chapters, leading researchers supply innovative applications of large language models across the economic realm. Through detailed frameworks, real-world case studies, and governance recommendations, this book highlights applied research for generative AI in finance functions. Several chapters demonstrate how data-driven insights from AI systems can optimize complex financial processes to reduce resource usage, lower costs, and drive positive environmental impact over the long term. In addition, chapters on AI-enabled risk assessment, fraud analytics, and regulatory technology highlight applied research for generative AI in finance. The book also explores emerging applications like leveraging blockchain and metaverse interfaces to create generative AI models that can revolutionize areas from carbon credit trading to virtual audits. Overall, with in-depth applied research at the nexus of sustainability and optimization enabled by data science and generative AI, the book offers a compilation of best practices in leveraging AI for optimal, ethical, and future-oriented financial management.
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Author / Editor Details
Pethuru Raj Chelliah, PhD, is a chief architect at Reliance Jio Platforms Ltd. (JPL), Bangalore, India. He has also worked at the IBM global cloud center and Robert Bosch Corporate Research.
Pethuru Raj Chelliah, PhD, is a chief architect at Reliance Jio Platforms Ltd. (JPL), Bangalore, India. He has also worked at the IBM global cloud center and Robert Bosch Corporate Research.
Pushan Kumar Dutta, PhD, is an assistant professor in the Electronics and Communication Engineering Department at ASETKL, Amity University Kolkata, West Bengal, India. He has edited multiple books, published about 50 articles, and completed 10 book editorials. He was honored with the Young Faculty in Engineering Award in 2018.
Abhishek Kumar, PhD, is the assistant director and an associate professor in the Computer Science & Engineering Department, Chandigarh University, Punjab, India. He has more than 100 publications in reputed peer-reviewed national and international journals, books, and conferences.
Ernesto D.R. Santibanez Gonzalez, PhD, is a professor at the University of Talca, Chile, and a professor at Paulista University, Brazil. He has published more than 60 research articles and has developed numerous projects.
Mohit Mittal, PhD, is a data scientist at Knowtion GmbH in Karlsruhe, Germany. He completed a post-doctorate at Kyoto Sangyo University in Japan. He has authored numerous papers published in prestigious journals and at top-tier conferences.
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Table of Contents
Preface
Part I: Foundations and Applications of AI in Finance
1. Artificial Intelligence Application and Research in Accounting, Finance, Economics, Business, and Management
Peterson K. Ozili
1.1 Introduction
1.2 Literature Review
1.3 Artificial Intelligence Applications in Accounting
1.4 Artificial Intelligence Applications in Finance
1.5 Artificial Intelligence Applications in Economics
1.6 Artificial Intelligence Applications in Business and Management
1.7 Risks of AI
1.8 Conclusion
References
2. Automating Data Entry in the Indian Banking Industry Through Generative AI
Srividya Prathiba, Rahul Pandey, Yashwant Patel and Manjinder Singh
2.1 Introduction
2.2 Literature Review
2.3 Methodology
2.4 Data Entry Automation with Generative AI
2.5 Results and Analysis
2.6 Discussion
2.7 Conclusion
References
3. Future Approach Generative AI, Stylized Architecture, and its Potential in Finance
Abhinna Baxi Bhatnagar, Abhaya Nanad, Anshul Kumar and Rakesh Kumar
3.1 Introduction
3.2 Risk Considerations
3.2.1 Data Privacy
3.2.2 Embedded Bias
3.2.3 Sample Generative AI Applications in the Financial Sector
3.3 Risk Considerations in AI Application
3.3.1 Data Privacy
3.3.2 Embedded Bias
3.3.3 Robustness
3.4 Significant Challenge
3.4.1 Synthetic Data in AI
3.4.2 Explain Ability
3.4.3 Cybersecurity
3.4.4 Financial Stability
3.5 Generative AI and its Architecture
3.6 Conclusion
References
4. Generative Artificial Intelligence (GAI) for Accurate Financial Forecasting
Tajinder Kumar, Sachin Lalar, Vishal Garg, Pooja Sharma and Ravi Dutt Mishra
4.1 Introduction
4.2 Literature Review
4.2.1 Traditional Financial Forecasting Methods
4.2.2 The Advent of Artificial Intelligence (AI) in Finance
4.2.3 Generative Artificial Intelligence (GAI) in Finance
4.2.4 Research on GAI for Financial Forecasting
4.2.5 Gaps in the Current Literature
4.3 Methodology
4.3.1 Data Collection and Preprocessing
4.3.2 Generative Artificial Intelligence Models
4.3.2.1 Selection of GAI Models
4.3.2.2 Model Architecture
4.3.2.3 Model Training and Validation
4.3.3 Performance Metrics
4.3.3.1 Accuracy Metrics
4.3.3.2 Risk Metrics
4.3.3.3 Comparing Conventional Approaches
4.3.4 Algorithm for Financial Forecasting Using GANs
4.4 Analysis of the Research Results
4.4.1 Improved Forecasting Accuracy
4.5 Conclusion
4.5.1 Key Findings
4.5.2 Implications
4.5.3 Future Directions
References
5. The Far-Reaching Impacts of Emerging Technologies in Accounting and Finance
Sudhansu Sekhar Nanda
5.1 Introduction
5.2 Objectives of the Study
5.3 Artificial Intelligence (AI): Meaning and Definition
5.3.1 Elements of Artificial Intelligence
5.4 Accounting and Finance Applications for Artificial Intelligence
5.4.1 Benefits of AI Implementation in Accountancy
5.4.2 The Obstacles to Using AI in Accounting
5.5 Applications for Blockchain Technology in the Financial Sector
5.5.1 The Upsides of Using Blockchain Technology
5.5.2 Challenges of Blockchain Adoption
5.6 Accounting and Financial Robotic Process Automation
5.6.1 Uses of Robotic Process Automation in the Financial Sector
5.6.2 The Upsides of Robotic Process Automation
5.6.3 Challenges of RPA Implementation
5.7 Accounting and Financial Analytics Using Big Data
5.7.1 Financial and Accounting Uses for Data Analytics
5.8 Combining AI with Blockchain, Robotic Process Automation, and Data Science
5.9 Ethical Considerations and Data Privacy Concerns
5.10 Potential Impact and Emerging Trends
5.11 Conclusion
Bibliography
Part II: Generative AI in Risk Management and Fraud Detection
6. Deep Diving into Financial Frauds via Ad Click, Credit Card Management and Document Dispensation in E-Commerce Transactions
Bhupinder Singh, Pushan Kumar Dutta and Christian Kaunert
6.1 Introduction and Background
6.1.1 Objectives of the Chapter
6.1.2 Significance of the Chapter
6.2 Ad-Click Fraud Detection in the Banking and Financial Sectors
6.2.1 Definition and Types of Ad-Click Fraud
6.2.2 Data Sources and Features
6.2.3 AI and ML Algorithms for Ad-Click Fraud Detection
6.3 Credit Card Management Fraud Detection
6.3.1 Data Sources and Features Concerning Credit Card Management Fraud Detection
6.3.2 AI and ML Algorithms for Credit Card Fraud Detection
6.4 Document Dispensation Fraud Detection in E-Commerce Transactions
6.4.1 AI and ML Algorithms for Document Dispensation Fraud Detection
6.5 Cross-Domain Analysis: Frauds in Banking and Financial Industry
6.5.1 Commonalities and Differences in Fraud Detection Techniques
6.5.2 Transfer Learning and Knowledge Sharing in Fraud Detection
6.5.3 Building a Unified Fraud Detection Model
6.6 Ethical and Privacy Considerations: Frauds in Banking and Financial Industry
6.6.1 Data Privacy and Security
6.6.2 Bias and Fairness in AI/ML Models
6.6.3 Regulatory Compliance
6.7 Advancements in AI/ML Techniques
6.7.1 Blockchain and Distributed Ledger Technology
6.7.2 Explainable AI for Fraud Detection
6.8 Challenges and Risks
6.8.1 Model Robustness and Adversarial Attacks
6.8.2 Scalability and Computational Resources
6.8.3 Evolving Nature of Fraud
6.9 Conclusion and Future Scope
References
7. Generative AI: A Transformative Tool for Mitigating Risks for Financial Frauds
Rahul Joshi, Krishna Pandey and Suman Kumari
7.1 Introduction
7.2 Generative AI and Its Characteristics
7.2.1 Characteristics of Generative AI
7.3 Various Types of AI Used in Financial Assets
7.4 Fears in the Financial Sector
7.5 Risk Mitigation in the Finance Industry
7.6 Risk of Financial Fraud
7.7 Requirement for Employee Training
7.8 Regulatory Bodies and Industry Associations
7.9 Hallucination Concern in the Present Times
7.9.1 The Dataset Used for Training
7.10 Proper Training Requirements
7.11 Future Research Directions
7.12 Conclusion
References
8. Innovation Unleashed Charting a New Course in Risk Evaluation with Generative AI
Shabeena Shah W., Khadeeja Bilquees A. and M. Jamal Mohamed Zubair
8.1 Introduction
8.2 New Challenges and Roles
8.3 Reviews
8.4 Findings
8.5 Conclusion
Direction for Future Research
References
9. The Significance of Generative AI in Enhancing Fraud Detection and Prevention Within the Banking Industry
Roshni Rawal, Priya Sachdeva and Aabha S. Singhvi
9.1 Introduction
9.1.1 Background
9.1.2 Problem Statement
9.1.3 Purpose of the Study
9.1.4 Research Questions
9.1.5 Methodology
9.2 Literature Review
9.2.1 Traditional Methods of Fraud Detection
9.2.2 Generative Artificial Intelligence
9.2.3 Applications of Generative AI in Banking Fraud Detection
9.2.4 Benefits of Generative AI in Banking Fraud Prevention
9.2.5 Challenges and Ethical Considerations
9.3 Generative AI in Banking Fraud Detection
9.3.1 Data Preprocessing and Feature Engineering
9.3.2 Anomaly Detection
9.3.3 Behavior Analysis
9.3.4 Natural Language Processing in Fraud Detection
9.3.5 Deep Learning for Fraud Detection
9.4 Case Studies
9.4.1 Real-Time Examples of Generative AI Implementation in Indian Banking
9.4.2 Outcomes and Success Stories
9.5 Challenges and Ethical Considerations
9.5.1 Data Privacy and Security
9.5.2 Bias and Fairness
9.5.3 Transparency and Explainability
9.5.4 Human-AI Collaboration
9.6 Future Directions
9.6.1 Advancements in Generative AI
9.6.2 Regulatory Changes and Compliance
9.6.3 Emerging Threats and Fraud Techniques
9.7 Conclusion
9.7.1 Summary of Findings
9.7.2 Implications for the Banking Industry
9.7.3 The Future of Generative AI in Banking Fraud Prevention
9.8 Recommendations
9.8.1 Best Practices for Implementing Generative AI
9.8.2 Investment Strategies for Banks
9.8.3 Research and Development Directions
References
10. Role of Generative AI for Fraud Detection and Prevention
Prasanna Kulkarni, Pankaj Pathak, Samaya Pillai and Vishal Tigga
10.1 Introduction
10.1.1 Background and Context
10.1.2 Key Focus Areas
10.2 Understanding Fraud
10.2.1 Types of Fraud
10.2.2 The Dynamic Nature of Fraud
10.3 Generative AI Fundamentals
10.3.1 Introduction to Generative AI
10.3.2 Generating Synthetic Data
10.3.3 Anomaly Detection with VAEs
10.4 Applications of Generative AI in Fraud Detection
10.4.1 Case Studies and Use-Cases
10.4.2 Review of Existing Work
10.4.3 Benefits and Limitations
10.4.4 Implementation Challenges and Best Practices
10.5 Conclusion
References
Part III: Ethical, Legal, and Regulatory Considerations
11. Ethical and Regulatory Compliance Challenges of Generative AI in Human Resources
Leena Singh, Ankur Randhelia, Ashish Jain and Akash Kumar Choudhary
11.1 Introduction
11.2 Importance of Compliance and Ethical Considerations
11.3 Research Objectives and Methodology
11.4 Literature Review
11.4.1 The Role of AI in HR: Automation, Decision-Making, and Augmentation
11.4.2 Ethical Concerns in AI and HR: Bias, Discrimination, and Fairness
11.4.3 Legal Frameworks and Regulations: GDPR, EEOC, and Other Relevant Laws
11.4.4 Transparency, Explainability, and Accountability in AI
11.5 Methodology
11.5.1 Explanation of the Secondary Data Analysis Approach
11.5.2 Data Sources: Existing Research Papers, Case Studies, Reports, and Relevant Databases
11.5.3 Data Collection and Selection Criteria
11.5.4 Data Analysis Techniques (Content Analysis, Thematic Analysis, etc.)
11.6 Ethical Implications of Generative AI in HR
11.6.1 Bias and Discrimination in Hiring and Employee Management
11.6.2 Privacy Concerns and Data Protection
11.6.3 The Impact of AI on Diversity and Inclusion Efforts
11.6.4 Stakeholder Perspectives on AI Ethics
11.7 Ensuring Compliance with Legal Standards
11.7.1 GDPR and Data Privacy Requirements
11.7.2 Equal Employment Opportunity (EEO) Laws and Regulations
11.7.3 Auditing and Reporting Mechanisms
11.7.4 The Role of HR Professionals and Legal Advisors
11.8 Best Practices and Strategies
11.8.1 Regular Auditing and Bias Mitigation
11.8.2 Employee Training and Awareness Programs
11.8.3 Collaborative Efforts Between HR and IT Teams
11.9 Discussion
11.9.1 Synthesis of Findings
11.9.2 Identification of Key Challenges and Opportunities
11.9.3 Recommendations for HR Practitioners and Policymakers
11.10 Conclusion
11.11 Summary of Main Findings
11.12 Significance of Ethical AI in HR Practices
11.13 Future Research Directions and Potential Advancements
References
12. Navigating the Frontier of Finance: A Scoping Review of Generative AI Applications and Implications
Ahmad Haidar and Ahmad Abbass
Introduction
Background of the Study
Generative AI: Concept and Evolution
Risks of Generative AI within the Financial Context
Methodology
Identifying the Research Question
Identifying Relevant Studies
Selecting the Studies to be Included
Charting the Data
Results
Regulatory, Ethical, and User-Centric Perspectives in AI-Driven Finance
Technological Innovations and Applications of AI in Finance
Generative AI’s Role in Financial Analysis, Management, and Strategy
Discussion
Conclusion
References
Appendix 1
13. Ensuring Compliance and Ethical Standards with Generative AI in Fintech: A Multi-Dimensional Approach
Vishal Jain and Archan Mitra
13.1 Introduction to Generative AI in Fintech
13.2 Literature Review
13.3 Methodology
13.4 Case Study
13.5 Findings
13.6 Conclusion
References
14. Privacy Laws and Leak of Financial Data in the Era of Generative AI
Nitish Kumar Ojha and Sanjeev Thakur
Introduction
Case Study
Background
Issue
Impact
Response
Resolution
Conclusion
References
15. Ethics and Laws: Governing Generative AI’s Role in Financial Systems
Prakriti Dixit Porwal
Introduction
Applications of AI in Financial Systems
Ethical Challenges
Ethical AI in Indian Finance: Case Studies and Insights
Conclusion
References
Part IV: Industry-Specific Applications and Innovations
16. Generative AI Tools for Product Design and Engineering
Manoj Singh Adhikari, Yogesh Kumar Verma, Manoj Sindhwani and Shippu Sachdeva
16.1 Introduction
16.2 Concept Generation and Ideation
16.3 Topology Optimization
16.4 Design Customization
16.5 Rapid Prototyping and Iteration
16.6 Multi-Objective Optimization
16.7 AI-Powered Collaboration
16.8 Material Selection and Integration
16.9 Generative Simulations and Testing
16.10 Generative Design for Additive Manufacturing
16.11 Sustainability and Environmental Impact
16.12 Regulatory Compliance and Standards
16.13 Cost Optimization
16.14 Market Trends and Consumer Insights
16.15 Conclusion
References
17. AI-Driven Generative Design Redefines the Engineering Process
Harpreet Kaur Channi, Amritjot Kaur and Surinder Kaur
17.1 Introduction
17.1.1 Overview of Generative AI
17.1.2 Evolution of AI in Product Design and Engineering
17.1.2.1 Emergence of Computational Tools
17.1.2.2 Rule-Based Expert Systems
17.1.2.3 Rise of Machine Learning
17.1.2.4 Neural Networks and Deep Learning
17.1.2.5 Generative AI in Design
17.1.2.6 Integrating AI Across the Product Lifecycle
17.1.3 Scope and Objectives
17.1.3.1 Objectives
17.2 Literature Survey
17.3 Fundamentals of Generative AI
17.3.1 Basics of Machine Learning
17.3.2 Deep Learning and Neural Networks
17.3.2.1 Architecture of Neural Networks
17.3.2.2 Training Neural Networks
17.3.3 Generative Models
17.4 Generative Design in Product Development
17.4.1 Design Space Exploration
17.4.1.1 Rapid Iteration
17.4.1.2 Diverse Concept Generation
17.4.2 Customization and Personalization
17.4.2.1 Optimization and Performance Enhancement
17.4.2.2 Optimization Techniques
17.4.3 AI-Driven Simulation and Prototyping
17.5 Case Studies
17.5.1 Ethical and Legal Considerations
17.5.2 Future Trends and Emerging Technologies
17.6 Conclusions
References
18. Insurance Disruption: Analytics on Blockchain Transforming Indian Insurance Industry
Swati Gupta and Ruchika Rastogi
Introduction
Blockchain Technology
Why is Blockchain Important?
Enabling Industry Collaboration
Blockchain and Insurance
What is it?
Where it is Applicable?
How will it Benefit?
Insurance Sector: India
Challenges
Blockchain and the Insurance Regulatory Framework
Prospects
Conclusion
References
19. Application of Explainable Artificial Intelligence in Fintech
Raunak Kumar, Priya Gupta and Bhawna
19.1 Introduction
19.2 The Current Landscape of Explainable Artificial Intelligence (XAI)
19.2.1 Explainable Artificial Intelligence
19.2.2 Working of Various Kinds of XAI Models
19.2.3 Advancement in Fintech
19.3 Advancing Financial Predictive Analysis: Integrating Explainable AI and Machine Learning in Finance
19.3.1 Bankruptcy Prediction and Credit Risk Prediction
19.4 Advancements of Explainable AI in Financial Predictions: Methodologies, Regulatory Compliance, and Machine Learning Techniques
19.4.1 Bankruptcy Prediction
19.4.2 Credit Card Approval Prediction
19.5 Conclusion and Future Scope
19.5.1 Theoretical Implications
19.5.2 Implications for Other Researchers
19.5.3 Future Scope
References
20. Empowering Financial Efficiency in India: Harnessing Artificial Intelligence (AI) for Streamlining Accounting and Finance
Bhawna and Priya Gupta
20.1 Introduction
20.1.1 Background of AI in the Accounting and Financial Context
20.1.2 Significance of AI Adoption in Indian Finance
20.1.2.1 Enhanced Customer Experience
20.1.2.2 Fraud Detection and Prevention
20.1.2.3 Credit Scoring and Risk Management
20.1.2.4 Algorithmic Trading and Investment Management
20.1.2.5 Regulatory Compliance
20.2 Integrating AI into Accounting and Finance
20.2.1 Application of AI in Accounting and Finance
20.2.2 Impact of AI in Accounting and Finance
20.2.2.1 To Avoid the Possibility of Financial Fraud
20.2.2.2 To Promote the Reform of Traditional Accounting and Auditing
20.2.2.3 To Improve the Quality of Accounting Information
20.3 Benefits of Using AI to Simplify Tasks in Accounting and Finance
20.3.1 Enhanced Efficiency and Automation
20.3.2 Deeper Data Analysis and Insights
20.3.3 Improved Client Experience and Value
20.3.4 Additional Potential Advantages
20.4 Challenges in Implementing AI in Accounting and Finance
20.4.1 Thorough Analysis and Strategic Planning for Complications in Implementing AI in Accounting and Finance
20.5 Future Prospects and Trends
20.5.1 Anticipated Developments in AI and Finance
20.5.2 Emerging Trends Shaping the Landscape
20.5.3 Long-Term Prospects and Sustainable Practices
20.6 Valuable Insights for Businesses, Policymakers, and Stakeholders
20.6.1 For Businesses
20.6.2 For Policymakers
20.6.3 For Stakeholders
20.7 Conclusion
References
21. Framework and Interface: The Backbone of AI Systems in Banking in India
Priya Sachdeva, Priti Goswami, Sumona Bhattacharya and Mohd. Ashfaq Siddiqui
21.1 Introduction
21.1.1 Background
21.1.2 Objectives
21.1.3 Scope
21.2 Literature Review
21.2.1 Evolution of AI in Banking
21.2.2 AI Applications in Indian Banking
21.2.3 Challenges and Opportunities
21.3 Framework of AI Systems in Banking
21.3.1 Data Acquisition and Management
21.3.2 Machine Learning Models
21.3.3 Natural Language Processing (NLP) Integration
21.3.4 Robotic Process Automation (RPA)
21.3.5 Security and Compliance
21.4 Interface Design for AI Systems
21.4.1 User-Friendly Interfaces
21.4.2 Personalization and Customer Experience
21.4.3 Customer Service Chatbots
21.4.4 Data Visualization
21.5 Impact of AI in Indian Banking
21.5.1 Improved Efficiency and Productivity
21.5.2 Enhanced Customer Experiences
21.5.3 Risk Management
21.5.4 Fraud Detection and Prevention
21.6 Regulatory Environment
21.6.1 RBI Guidelines on AI in Banking
21.6.2 Data Privacy and Security Regulations
21.6.3 Ethical Considerations
21.7 Case Studies
21.7.1 HDFC Bank
21.7.2 ICICI Bank
21.7.3 State Bank of India (SBI)
21.8 Future Trends
21.8.1 AI Adoption in Rural Banking
21.8.2 Integration of Blockchain and AI
21.8.3 AI in Wealth Management
21.9 Conclusion
21.9.1 Summary of Key Findings
21.9.2 Implications for the Banking Industry
21.9.3 Recommendations for Future Research
References
22. Harnessing Generative AI for Engineering and Product Design: Conceptualization, Techniques, Advancements and Challenges
Sakshi, Chetan Sharma, Gunjan Verma and Nisha Chanana
22.1 Introduction to Generative AI
22.2 Working on Generative AI
22.3 Benefits of Generative AI
22.3.1 Rapid Conceptualization
22.3.2 Enhanced Creativity
22.3.3 Optimization and Simulation
22.3.4 Time and Cost Efficiency
22.3.5 Iterative Improvement
22.4 Generative AI Technique
22.4.1 Generative Adversarial Networks (GANs)
22.4.2 Variational Autoencoders (VAEs)
22.4.3 Recurrent Neural Networks (RNNs)
22.4.4 Long Short-Term Memory Networks (LSTMs)
22.5 Data Requirements
22.5.1 Diverse Data Sources
22.5.2 High-Quality Training Data
22.5.3 Data Labeling and Annotation
22.5.4 Metadata Integration
22.5.5 Data Security and Privacy
22.5.6 Scalability
22.6 Applications in Concept Generation
22.6.1 Automated Design Drafting
22.6.2 Material Selection
22.6.3 User-Centric Design
22.6.4 Performance Optimization
22.6.5 Applications of Generative AI in Phases of Product Design and Engineering
22.7 Prototyping and Iteration
22.7.1 Streamlined Prototyping
22.7.2 Design Exploration
22.7.3 Iterative Refinement
22.7.4 Cost-Efficient Development
22.8 Optimization and Simulation
22.8.1 Design Optimization
22.8.2 Material Optimization
22.8.3 Performance Simulation
22.8.4 Environmental Simulation
22.9 Significance of Human-AI Collaboration
22.9.1 Complementary Expertise
22.9.2 Efficiency and Ideation
22.9.3 Iterative Design
22.9.4 Design Validation
22.10 Challenges and Limitations
22.10.1 Challenges
22.10.2 Limitations
22.11 Future Trends and Developments
22.11.1 Advancements in Algorithmic Complexity
22.11.2 Multidisciplinary Integration
22.11.3 Human-AI Collaboration
22.11.4 Ethical and Regulatory Considerations
References
Index
Back to Top
Preface
Part I: Foundations and Applications of AI in Finance
1. Artificial Intelligence Application and Research in Accounting, Finance, Economics, Business, and Management
Peterson K. Ozili
1.1 Introduction
1.2 Literature Review
1.3 Artificial Intelligence Applications in Accounting
1.4 Artificial Intelligence Applications in Finance
1.5 Artificial Intelligence Applications in Economics
1.6 Artificial Intelligence Applications in Business and Management
1.7 Risks of AI
1.8 Conclusion
References
2. Automating Data Entry in the Indian Banking Industry Through Generative AI
Srividya Prathiba, Rahul Pandey, Yashwant Patel and Manjinder Singh
2.1 Introduction
2.2 Literature Review
2.3 Methodology
2.4 Data Entry Automation with Generative AI
2.5 Results and Analysis
2.6 Discussion
2.7 Conclusion
References
3. Future Approach Generative AI, Stylized Architecture, and its Potential in Finance
Abhinna Baxi Bhatnagar, Abhaya Nanad, Anshul Kumar and Rakesh Kumar
3.1 Introduction
3.2 Risk Considerations
3.2.1 Data Privacy
3.2.2 Embedded Bias
3.2.3 Sample Generative AI Applications in the Financial Sector
3.3 Risk Considerations in AI Application
3.3.1 Data Privacy
3.3.2 Embedded Bias
3.3.3 Robustness
3.4 Significant Challenge
3.4.1 Synthetic Data in AI
3.4.2 Explain Ability
3.4.3 Cybersecurity
3.4.4 Financial Stability
3.5 Generative AI and its Architecture
3.6 Conclusion
References
4. Generative Artificial Intelligence (GAI) for Accurate Financial Forecasting
Tajinder Kumar, Sachin Lalar, Vishal Garg, Pooja Sharma and Ravi Dutt Mishra
4.1 Introduction
4.2 Literature Review
4.2.1 Traditional Financial Forecasting Methods
4.2.2 The Advent of Artificial Intelligence (AI) in Finance
4.2.3 Generative Artificial Intelligence (GAI) in Finance
4.2.4 Research on GAI for Financial Forecasting
4.2.5 Gaps in the Current Literature
4.3 Methodology
4.3.1 Data Collection and Preprocessing
4.3.2 Generative Artificial Intelligence Models
4.3.2.1 Selection of GAI Models
4.3.2.2 Model Architecture
4.3.2.3 Model Training and Validation
4.3.3 Performance Metrics
4.3.3.1 Accuracy Metrics
4.3.3.2 Risk Metrics
4.3.3.3 Comparing Conventional Approaches
4.3.4 Algorithm for Financial Forecasting Using GANs
4.4 Analysis of the Research Results
4.4.1 Improved Forecasting Accuracy
4.5 Conclusion
4.5.1 Key Findings
4.5.2 Implications
4.5.3 Future Directions
References
5. The Far-Reaching Impacts of Emerging Technologies in Accounting and Finance
Sudhansu Sekhar Nanda
5.1 Introduction
5.2 Objectives of the Study
5.3 Artificial Intelligence (AI): Meaning and Definition
5.3.1 Elements of Artificial Intelligence
5.4 Accounting and Finance Applications for Artificial Intelligence
5.4.1 Benefits of AI Implementation in Accountancy
5.4.2 The Obstacles to Using AI in Accounting
5.5 Applications for Blockchain Technology in the Financial Sector
5.5.1 The Upsides of Using Blockchain Technology
5.5.2 Challenges of Blockchain Adoption
5.6 Accounting and Financial Robotic Process Automation
5.6.1 Uses of Robotic Process Automation in the Financial Sector
5.6.2 The Upsides of Robotic Process Automation
5.6.3 Challenges of RPA Implementation
5.7 Accounting and Financial Analytics Using Big Data
5.7.1 Financial and Accounting Uses for Data Analytics
5.8 Combining AI with Blockchain, Robotic Process Automation, and Data Science
5.9 Ethical Considerations and Data Privacy Concerns
5.10 Potential Impact and Emerging Trends
5.11 Conclusion
Bibliography
Part II: Generative AI in Risk Management and Fraud Detection
6. Deep Diving into Financial Frauds via Ad Click, Credit Card Management and Document Dispensation in E-Commerce Transactions
Bhupinder Singh, Pushan Kumar Dutta and Christian Kaunert
6.1 Introduction and Background
6.1.1 Objectives of the Chapter
6.1.2 Significance of the Chapter
6.2 Ad-Click Fraud Detection in the Banking and Financial Sectors
6.2.1 Definition and Types of Ad-Click Fraud
6.2.2 Data Sources and Features
6.2.3 AI and ML Algorithms for Ad-Click Fraud Detection
6.3 Credit Card Management Fraud Detection
6.3.1 Data Sources and Features Concerning Credit Card Management Fraud Detection
6.3.2 AI and ML Algorithms for Credit Card Fraud Detection
6.4 Document Dispensation Fraud Detection in E-Commerce Transactions
6.4.1 AI and ML Algorithms for Document Dispensation Fraud Detection
6.5 Cross-Domain Analysis: Frauds in Banking and Financial Industry
6.5.1 Commonalities and Differences in Fraud Detection Techniques
6.5.2 Transfer Learning and Knowledge Sharing in Fraud Detection
6.5.3 Building a Unified Fraud Detection Model
6.6 Ethical and Privacy Considerations: Frauds in Banking and Financial Industry
6.6.1 Data Privacy and Security
6.6.2 Bias and Fairness in AI/ML Models
6.6.3 Regulatory Compliance
6.7 Advancements in AI/ML Techniques
6.7.1 Blockchain and Distributed Ledger Technology
6.7.2 Explainable AI for Fraud Detection
6.8 Challenges and Risks
6.8.1 Model Robustness and Adversarial Attacks
6.8.2 Scalability and Computational Resources
6.8.3 Evolving Nature of Fraud
6.9 Conclusion and Future Scope
References
7. Generative AI: A Transformative Tool for Mitigating Risks for Financial Frauds
Rahul Joshi, Krishna Pandey and Suman Kumari
7.1 Introduction
7.2 Generative AI and Its Characteristics
7.2.1 Characteristics of Generative AI
7.3 Various Types of AI Used in Financial Assets
7.4 Fears in the Financial Sector
7.5 Risk Mitigation in the Finance Industry
7.6 Risk of Financial Fraud
7.7 Requirement for Employee Training
7.8 Regulatory Bodies and Industry Associations
7.9 Hallucination Concern in the Present Times
7.9.1 The Dataset Used for Training
7.10 Proper Training Requirements
7.11 Future Research Directions
7.12 Conclusion
References
8. Innovation Unleashed Charting a New Course in Risk Evaluation with Generative AI
Shabeena Shah W., Khadeeja Bilquees A. and M. Jamal Mohamed Zubair
8.1 Introduction
8.2 New Challenges and Roles
8.3 Reviews
8.4 Findings
8.5 Conclusion
Direction for Future Research
References
9. The Significance of Generative AI in Enhancing Fraud Detection and Prevention Within the Banking Industry
Roshni Rawal, Priya Sachdeva and Aabha S. Singhvi
9.1 Introduction
9.1.1 Background
9.1.2 Problem Statement
9.1.3 Purpose of the Study
9.1.4 Research Questions
9.1.5 Methodology
9.2 Literature Review
9.2.1 Traditional Methods of Fraud Detection
9.2.2 Generative Artificial Intelligence
9.2.3 Applications of Generative AI in Banking Fraud Detection
9.2.4 Benefits of Generative AI in Banking Fraud Prevention
9.2.5 Challenges and Ethical Considerations
9.3 Generative AI in Banking Fraud Detection
9.3.1 Data Preprocessing and Feature Engineering
9.3.2 Anomaly Detection
9.3.3 Behavior Analysis
9.3.4 Natural Language Processing in Fraud Detection
9.3.5 Deep Learning for Fraud Detection
9.4 Case Studies
9.4.1 Real-Time Examples of Generative AI Implementation in Indian Banking
9.4.2 Outcomes and Success Stories
9.5 Challenges and Ethical Considerations
9.5.1 Data Privacy and Security
9.5.2 Bias and Fairness
9.5.3 Transparency and Explainability
9.5.4 Human-AI Collaboration
9.6 Future Directions
9.6.1 Advancements in Generative AI
9.6.2 Regulatory Changes and Compliance
9.6.3 Emerging Threats and Fraud Techniques
9.7 Conclusion
9.7.1 Summary of Findings
9.7.2 Implications for the Banking Industry
9.7.3 The Future of Generative AI in Banking Fraud Prevention
9.8 Recommendations
9.8.1 Best Practices for Implementing Generative AI
9.8.2 Investment Strategies for Banks
9.8.3 Research and Development Directions
References
10. Role of Generative AI for Fraud Detection and Prevention
Prasanna Kulkarni, Pankaj Pathak, Samaya Pillai and Vishal Tigga
10.1 Introduction
10.1.1 Background and Context
10.1.2 Key Focus Areas
10.2 Understanding Fraud
10.2.1 Types of Fraud
10.2.2 The Dynamic Nature of Fraud
10.3 Generative AI Fundamentals
10.3.1 Introduction to Generative AI
10.3.2 Generating Synthetic Data
10.3.3 Anomaly Detection with VAEs
10.4 Applications of Generative AI in Fraud Detection
10.4.1 Case Studies and Use-Cases
10.4.2 Review of Existing Work
10.4.3 Benefits and Limitations
10.4.4 Implementation Challenges and Best Practices
10.5 Conclusion
References
Part III: Ethical, Legal, and Regulatory Considerations
11. Ethical and Regulatory Compliance Challenges of Generative AI in Human Resources
Leena Singh, Ankur Randhelia, Ashish Jain and Akash Kumar Choudhary
11.1 Introduction
11.2 Importance of Compliance and Ethical Considerations
11.3 Research Objectives and Methodology
11.4 Literature Review
11.4.1 The Role of AI in HR: Automation, Decision-Making, and Augmentation
11.4.2 Ethical Concerns in AI and HR: Bias, Discrimination, and Fairness
11.4.3 Legal Frameworks and Regulations: GDPR, EEOC, and Other Relevant Laws
11.4.4 Transparency, Explainability, and Accountability in AI
11.5 Methodology
11.5.1 Explanation of the Secondary Data Analysis Approach
11.5.2 Data Sources: Existing Research Papers, Case Studies, Reports, and Relevant Databases
11.5.3 Data Collection and Selection Criteria
11.5.4 Data Analysis Techniques (Content Analysis, Thematic Analysis, etc.)
11.6 Ethical Implications of Generative AI in HR
11.6.1 Bias and Discrimination in Hiring and Employee Management
11.6.2 Privacy Concerns and Data Protection
11.6.3 The Impact of AI on Diversity and Inclusion Efforts
11.6.4 Stakeholder Perspectives on AI Ethics
11.7 Ensuring Compliance with Legal Standards
11.7.1 GDPR and Data Privacy Requirements
11.7.2 Equal Employment Opportunity (EEO) Laws and Regulations
11.7.3 Auditing and Reporting Mechanisms
11.7.4 The Role of HR Professionals and Legal Advisors
11.8 Best Practices and Strategies
11.8.1 Regular Auditing and Bias Mitigation
11.8.2 Employee Training and Awareness Programs
11.8.3 Collaborative Efforts Between HR and IT Teams
11.9 Discussion
11.9.1 Synthesis of Findings
11.9.2 Identification of Key Challenges and Opportunities
11.9.3 Recommendations for HR Practitioners and Policymakers
11.10 Conclusion
11.11 Summary of Main Findings
11.12 Significance of Ethical AI in HR Practices
11.13 Future Research Directions and Potential Advancements
References
12. Navigating the Frontier of Finance: A Scoping Review of Generative AI Applications and Implications
Ahmad Haidar and Ahmad Abbass
Introduction
Background of the Study
Generative AI: Concept and Evolution
Risks of Generative AI within the Financial Context
Methodology
Identifying the Research Question
Identifying Relevant Studies
Selecting the Studies to be Included
Charting the Data
Results
Regulatory, Ethical, and User-Centric Perspectives in AI-Driven Finance
Technological Innovations and Applications of AI in Finance
Generative AI’s Role in Financial Analysis, Management, and Strategy
Discussion
Conclusion
References
Appendix 1
13. Ensuring Compliance and Ethical Standards with Generative AI in Fintech: A Multi-Dimensional Approach
Vishal Jain and Archan Mitra
13.1 Introduction to Generative AI in Fintech
13.2 Literature Review
13.3 Methodology
13.4 Case Study
13.5 Findings
13.6 Conclusion
References
14. Privacy Laws and Leak of Financial Data in the Era of Generative AI
Nitish Kumar Ojha and Sanjeev Thakur
Introduction
Case Study
Background
Issue
Impact
Response
Resolution
Conclusion
References
15. Ethics and Laws: Governing Generative AI’s Role in Financial Systems
Prakriti Dixit Porwal
Introduction
Applications of AI in Financial Systems
Ethical Challenges
Ethical AI in Indian Finance: Case Studies and Insights
Conclusion
References
Part IV: Industry-Specific Applications and Innovations
16. Generative AI Tools for Product Design and Engineering
Manoj Singh Adhikari, Yogesh Kumar Verma, Manoj Sindhwani and Shippu Sachdeva
16.1 Introduction
16.2 Concept Generation and Ideation
16.3 Topology Optimization
16.4 Design Customization
16.5 Rapid Prototyping and Iteration
16.6 Multi-Objective Optimization
16.7 AI-Powered Collaboration
16.8 Material Selection and Integration
16.9 Generative Simulations and Testing
16.10 Generative Design for Additive Manufacturing
16.11 Sustainability and Environmental Impact
16.12 Regulatory Compliance and Standards
16.13 Cost Optimization
16.14 Market Trends and Consumer Insights
16.15 Conclusion
References
17. AI-Driven Generative Design Redefines the Engineering Process
Harpreet Kaur Channi, Amritjot Kaur and Surinder Kaur
17.1 Introduction
17.1.1 Overview of Generative AI
17.1.2 Evolution of AI in Product Design and Engineering
17.1.2.1 Emergence of Computational Tools
17.1.2.2 Rule-Based Expert Systems
17.1.2.3 Rise of Machine Learning
17.1.2.4 Neural Networks and Deep Learning
17.1.2.5 Generative AI in Design
17.1.2.6 Integrating AI Across the Product Lifecycle
17.1.3 Scope and Objectives
17.1.3.1 Objectives
17.2 Literature Survey
17.3 Fundamentals of Generative AI
17.3.1 Basics of Machine Learning
17.3.2 Deep Learning and Neural Networks
17.3.2.1 Architecture of Neural Networks
17.3.2.2 Training Neural Networks
17.3.3 Generative Models
17.4 Generative Design in Product Development
17.4.1 Design Space Exploration
17.4.1.1 Rapid Iteration
17.4.1.2 Diverse Concept Generation
17.4.2 Customization and Personalization
17.4.2.1 Optimization and Performance Enhancement
17.4.2.2 Optimization Techniques
17.4.3 AI-Driven Simulation and Prototyping
17.5 Case Studies
17.5.1 Ethical and Legal Considerations
17.5.2 Future Trends and Emerging Technologies
17.6 Conclusions
References
18. Insurance Disruption: Analytics on Blockchain Transforming Indian Insurance Industry
Swati Gupta and Ruchika Rastogi
Introduction
Blockchain Technology
Why is Blockchain Important?
Enabling Industry Collaboration
Blockchain and Insurance
What is it?
Where it is Applicable?
How will it Benefit?
Insurance Sector: India
Challenges
Blockchain and the Insurance Regulatory Framework
Prospects
Conclusion
References
19. Application of Explainable Artificial Intelligence in Fintech
Raunak Kumar, Priya Gupta and Bhawna
19.1 Introduction
19.2 The Current Landscape of Explainable Artificial Intelligence (XAI)
19.2.1 Explainable Artificial Intelligence
19.2.2 Working of Various Kinds of XAI Models
19.2.3 Advancement in Fintech
19.3 Advancing Financial Predictive Analysis: Integrating Explainable AI and Machine Learning in Finance
19.3.1 Bankruptcy Prediction and Credit Risk Prediction
19.4 Advancements of Explainable AI in Financial Predictions: Methodologies, Regulatory Compliance, and Machine Learning Techniques
19.4.1 Bankruptcy Prediction
19.4.2 Credit Card Approval Prediction
19.5 Conclusion and Future Scope
19.5.1 Theoretical Implications
19.5.2 Implications for Other Researchers
19.5.3 Future Scope
References
20. Empowering Financial Efficiency in India: Harnessing Artificial Intelligence (AI) for Streamlining Accounting and Finance
Bhawna and Priya Gupta
20.1 Introduction
20.1.1 Background of AI in the Accounting and Financial Context
20.1.2 Significance of AI Adoption in Indian Finance
20.1.2.1 Enhanced Customer Experience
20.1.2.2 Fraud Detection and Prevention
20.1.2.3 Credit Scoring and Risk Management
20.1.2.4 Algorithmic Trading and Investment Management
20.1.2.5 Regulatory Compliance
20.2 Integrating AI into Accounting and Finance
20.2.1 Application of AI in Accounting and Finance
20.2.2 Impact of AI in Accounting and Finance
20.2.2.1 To Avoid the Possibility of Financial Fraud
20.2.2.2 To Promote the Reform of Traditional Accounting and Auditing
20.2.2.3 To Improve the Quality of Accounting Information
20.3 Benefits of Using AI to Simplify Tasks in Accounting and Finance
20.3.1 Enhanced Efficiency and Automation
20.3.2 Deeper Data Analysis and Insights
20.3.3 Improved Client Experience and Value
20.3.4 Additional Potential Advantages
20.4 Challenges in Implementing AI in Accounting and Finance
20.4.1 Thorough Analysis and Strategic Planning for Complications in Implementing AI in Accounting and Finance
20.5 Future Prospects and Trends
20.5.1 Anticipated Developments in AI and Finance
20.5.2 Emerging Trends Shaping the Landscape
20.5.3 Long-Term Prospects and Sustainable Practices
20.6 Valuable Insights for Businesses, Policymakers, and Stakeholders
20.6.1 For Businesses
20.6.2 For Policymakers
20.6.3 For Stakeholders
20.7 Conclusion
References
21. Framework and Interface: The Backbone of AI Systems in Banking in India
Priya Sachdeva, Priti Goswami, Sumona Bhattacharya and Mohd. Ashfaq Siddiqui
21.1 Introduction
21.1.1 Background
21.1.2 Objectives
21.1.3 Scope
21.2 Literature Review
21.2.1 Evolution of AI in Banking
21.2.2 AI Applications in Indian Banking
21.2.3 Challenges and Opportunities
21.3 Framework of AI Systems in Banking
21.3.1 Data Acquisition and Management
21.3.2 Machine Learning Models
21.3.3 Natural Language Processing (NLP) Integration
21.3.4 Robotic Process Automation (RPA)
21.3.5 Security and Compliance
21.4 Interface Design for AI Systems
21.4.1 User-Friendly Interfaces
21.4.2 Personalization and Customer Experience
21.4.3 Customer Service Chatbots
21.4.4 Data Visualization
21.5 Impact of AI in Indian Banking
21.5.1 Improved Efficiency and Productivity
21.5.2 Enhanced Customer Experiences
21.5.3 Risk Management
21.5.4 Fraud Detection and Prevention
21.6 Regulatory Environment
21.6.1 RBI Guidelines on AI in Banking
21.6.2 Data Privacy and Security Regulations
21.6.3 Ethical Considerations
21.7 Case Studies
21.7.1 HDFC Bank
21.7.2 ICICI Bank
21.7.3 State Bank of India (SBI)
21.8 Future Trends
21.8.1 AI Adoption in Rural Banking
21.8.2 Integration of Blockchain and AI
21.8.3 AI in Wealth Management
21.9 Conclusion
21.9.1 Summary of Key Findings
21.9.2 Implications for the Banking Industry
21.9.3 Recommendations for Future Research
References
22. Harnessing Generative AI for Engineering and Product Design: Conceptualization, Techniques, Advancements and Challenges
Sakshi, Chetan Sharma, Gunjan Verma and Nisha Chanana
22.1 Introduction to Generative AI
22.2 Working on Generative AI
22.3 Benefits of Generative AI
22.3.1 Rapid Conceptualization
22.3.2 Enhanced Creativity
22.3.3 Optimization and Simulation
22.3.4 Time and Cost Efficiency
22.3.5 Iterative Improvement
22.4 Generative AI Technique
22.4.1 Generative Adversarial Networks (GANs)
22.4.2 Variational Autoencoders (VAEs)
22.4.3 Recurrent Neural Networks (RNNs)
22.4.4 Long Short-Term Memory Networks (LSTMs)
22.5 Data Requirements
22.5.1 Diverse Data Sources
22.5.2 High-Quality Training Data
22.5.3 Data Labeling and Annotation
22.5.4 Metadata Integration
22.5.5 Data Security and Privacy
22.5.6 Scalability
22.6 Applications in Concept Generation
22.6.1 Automated Design Drafting
22.6.2 Material Selection
22.6.3 User-Centric Design
22.6.4 Performance Optimization
22.6.5 Applications of Generative AI in Phases of Product Design and Engineering
22.7 Prototyping and Iteration
22.7.1 Streamlined Prototyping
22.7.2 Design Exploration
22.7.3 Iterative Refinement
22.7.4 Cost-Efficient Development
22.8 Optimization and Simulation
22.8.1 Design Optimization
22.8.2 Material Optimization
22.8.3 Performance Simulation
22.8.4 Environmental Simulation
22.9 Significance of Human-AI Collaboration
22.9.1 Complementary Expertise
22.9.2 Efficiency and Ideation
22.9.3 Iterative Design
22.9.4 Design Validation
22.10 Challenges and Limitations
22.10.1 Challenges
22.10.2 Limitations
22.11 Future Trends and Developments
22.11.1 Advancements in Algorithmic Complexity
22.11.2 Multidisciplinary Integration
22.11.3 Human-AI Collaboration
22.11.4 Ethical and Regulatory Considerations
References
Index
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