فهرست مطالب

• Part I: Foundations and Applications of AI in Finance
• Chapter 1: Artificial Intelligence Application and Research in Accounting, Finance, Economics, Business, and Management
• 1.1 Introduction
• 1.2 Literature Review
• 1.3 Applications in Accounting
• 1.4 Applications in Finance
• 1.5 Applications in Economics
• 1.6 Applications in Business and Management
• 1.7 Risks of AI
• 1.8 Conclusion
• 1.9 References
• Chapter 2: Automating Data Entry in the Indian Banking Industry Through Generative AI
• 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
• 2.8 References
• Chapter 3: Future Approach: Generative AI, Stylized Architecture, and Its Potential in Finance
• 3.1 Introduction
• 3.2 Risk Considerations
• 3.3 Generative AI Applications in Finance
• 3.4 Significant Challenges
• 3.5 Generative AI Architecture
• 3.6 Conclusion
• 3.7 References
• Chapter 4: Generative Artificial Intelligence (GAI) for Accurate Financial Forecasting
• 4.1 Introduction
• 4.2 Literature Review
• 4.3 Methodology
• 4.4 Performance Metrics
• 4.5 Forecasting Algorithm
• 4.6 Analysis of Results
• 4.7 Conclusion
• 4.8 References
• Chapter 5: The Far-Reaching Impacts of Emerging Technologies in Accounting and Finance
• 5.1 Introduction
• 5.2 Study Objectives
• 5.3 Definition and Elements of AI
• 5.4 Applications in Accounting and Finance
• 5.5 Blockchain in Finance
• 5.6 Robotic Process Automation
• 5.7 Big Data Analytics
• 5.8 Integration of Technologies
• 5.9 Ethical and Privacy Considerations
• 5.10 Emerging Trends
• 5.11 Conclusion
• 5.12 Bibliography
• Part II: Generative AI in Risk Management and Fraud Detection
• Chapter 6: Deep Diving into Financial Frauds
• 6.1 Introduction and Background
• 6.2 Ad-Click Fraud Detection
• 6.3 Credit Card Fraud Detection
• 6.4 Document Dispensation Fraud Detection
• 6.5 Cross-Domain Analysis
• 6.6 Ethical and Privacy Considerations
• 6.7 Advancements in AI/ML
• 6.8 Challenges and Risks
• 6.9 Conclusion and Future Scope
• 6.10 References
• Chapter 7: Generative AI: A Transformative Tool for Mitigating Risks for Financial Frauds
• 7.1 Introduction
• 7.2 Characteristics of Generative AI
• 7.3 AI Types in Financial Assets
• 7.4 Risks and Mitigation Strategies
• 7.5 Employee Training and Regulation
• 7.6 Hallucination and Training Issues
• 7.7 Future Research
• 7.8 Conclusion
• 7.9 References
• Chapter 8: Innovation Unleashed: Generative AI in Risk Evaluation
• 8.1 Introduction
• 8.2 New Challenges and Roles
• 8.3 Reviews and Findings
• 8.4 Conclusion
• 8.5 Future Research
• 8.6 References
• Chapter 9: The Significance of Generative AI in Fraud Detection Within Banking
• 9.1 Introduction
• 9.2 Literature Review
• 9.3 Applications in Banking Fraud
• 9.4 Case Studies
• 9.5 Ethical Considerations
• 9.6 Future Directions
• 9.7 Conclusion
• 9.8 Recommendations
• 9.9 References
• Chapter 10: Role of Generative AI for Fraud Detection and Prevention
• 10.1 Introduction
• 10.2 Understanding Fraud
• 10.3 Generative AI Fundamentals
• 10.4 Applications and Use Cases
• 10.5 Benefits and Challenges
• 10.6 Conclusion
• 10.7 References
• Part III: Ethical, Legal, and Regulatory Considerations
• Chapter 1: Ethical and Regulatory Compliance in Human Resources
• 1.1 Introduction
• 1.2 Importance of Compliance
• 1.3 Literature Review
• 1.4 Methodology
• 1.5 Ethical Implications in HR
• 1.6 Ensuring Legal Standards
• 1.7 Best Practices and Strategies
• 1.8 Discussion
• 1.9 Conclusion
• 1.10 References
• Chapter 2: Navigating the Frontier of Finance
• 2.1 Introduction and Background
• 2.2 Generative AI Concepts
• 2.3 Risks and Challenges
• 2.4 Methodology and Data Analysis
• 2.5 Results and Discussion
• 2.6 Conclusion
• 2.7 References
• Chapter 3: Ensuring Compliance and Ethical Standards in Fintech
• 3.1 Introduction
• 3.2 Literature Review
• 3.3 Methodology
• 3.4 Case Study and Findings
• 3.5 Conclusion
• 3.6 References
• Chapter 4: Privacy Laws and Leak of Financial Data
• 4.1 Introduction
• 4.2 Case Study
• 4.3 Background and Resolution
• 4.4 Conclusion
• 4.5 References
• Chapter 5: Ethics and Laws Governing AI in Financial Systems
• 5.1 Introduction
• 5.2 AI Applications in Finance
• 5.3 Ethical Challenges
• 5.4 Indian Case Studies
• 5.5 Conclusion
• 5.6 References
• Part IV: Industry-Specific Applications and Innovations
• Chapter 1: Generative AI Tools for Product Design and Engineering
• 1.1 Introduction
• 1.2 Ideation and Optimization
• 1.3 Customization and Prototyping
• 1.4 Collaboration and Simulation
• 1.5 Regulatory and Cost Aspects
• 1.6 Market Insights
• 1.7 Conclusion
• 1.8 References
• Chapter 2: AI-Driven Generative Design in Engineering
• 2.1 Introduction
• 2.2 Evolution of AI in Design
• 2.3 Fundamentals of Generative AI
• 2.4 Generative Design Applications
• 2.5 Case Studies
• 2.6 Ethical Issues and Future Trends
• 2.7 Conclusion
• 2.8 References
• Chapter 3: Blockchain Transforming Indian Insurance Industry
• 3.1 Introduction
• 3.2 Importance of Blockchain
• 3.3 Application in Insurance
• 3.4 Industry Challenges and Regulation
• 3.5 Prospects and Conclusion
• 3.6 References
• Chapter 4: Explainable AI in Fintech
• 4.1 Introduction
• 4.2 Explainable AI Concepts
• 4.3 Financial Predictive Analysis
• 4.4 Regulatory Compliance
• 4.5 Conclusion and Future Scope
• 4.6 References
• Chapter 5: Empowering Financial Efficiency in India with AI
• 5.1 Introduction and Background
• 5.2 AI Integration in Finance
• 5.3 Benefits and Challenges
• 5.4 Future Prospects and Trends
• 5.5 Insights for Stakeholders
• 5.6 Conclusion
• 5.7 References
• Chapter 6: Framework and Interface of AI Systems in Indian Banking
• 6.1 Introduction and Objectives
• 6.2 Literature Review
• 6.3 AI System Framework
• 6.4 Interface Design
• 6.5 Industry Impact
• 6.6 Regulatory Environment
• 6.7 Case Studies
• 6.8 Future Trends
• 6.9 Conclusion
• 6.10 References
• Chapter 7: Generative AI in Engineering and Product Design
• 7.1 Introduction
• 7.2 Working Principles and Benefits
• 7.3 Techniques and Data Needs
• 7.4 Applications and Prototyping
• 7.5 Simulation and Optimization
• 7.6 Human-AI Collaboration
• 7.7 Challenges and Future Developments
• 7.8 References
• Chapter 8: Innovation Unleashed: Generative AI in Risk Evaluation
• 8.1 Introduction
• 8.2 New Challenges and Roles
• 8.2.1 Challenges
• 8.2.2 Roles of AI
• 8.3 Review of Literature and Findings
• 8.3.1 Literature Review
• 8.3.2 Key Findings
• 8.4 Discussion and Synthesis
• 8.5 Conclusion and Future Research Directions
• 8.5.1 Conclusion
• 8.5.2 Future Research
• 8.6 References
• Chapter 9: The Significance of Generative AI in Fraud Detection Within Banking
• 9.1 Introduction
• 9.2 Review of Literature
• 9.3 Applications of Generative AI in Banking Fraud Detection
• 9.3.1 Overview of Generative AI
• 9.3.2 Fraud Scenarios in Banking
• 9.3.3 Applications and Use Cases
• 9.4 Case Studies on Generative AI in Fraud Detection
• 9.5 Ethical and Regulatory Considerations
• 9.6 Future Directions for Research
• 9.7 Conclusion
• 9.8 Recommendations for Practitioners and Policymakers
• 9.9 References
• Chapter 10: Role of Generative AI for Fraud Detection and Prevention
• 10.1 Introduction
• 10.2 Understanding Fraud in the Context of Financial Transactions
• 10.3 Fundamentals of Generative AI and Its Relevance
• 10.4 Applications and Use Cases of Generative AI in Fraud Detection and Prevention
• 10.5 Benefits and Challenges of Implementing Generative AI
• 10.6 Conclusion
• 10.7 References
• Part III: Ethical, Legal, and Regulatory Considerations
• Chapter 11: Ethical and Regulatory Compliance Challenges of Generative AI in Human Resources
• 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
• Chapter 12: Navigating the Frontier of Finance
• 12.1 Introduction and Background
• 12.2 Generative AI Concepts in Finance
• 12.3 Risks and Challenges Associated with Generative AI
• 12.4 Methodology and Data Analysis
• 12.5 Results and Discussion
• 12.6 Conclusion
• 12.7 References
• Chapter 13: Ensuring Compliance and Ethical Standards with Generative AI in Fintech: A Multi-Dimensional Approach
• 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
• Chapter 14: Privacy Laws and Leak of Financial Data in the Era of Generative AI
• Introduction
• Case Study
• Background
• Issue
• Impact
• Response
• Resolution
• Conclusion
• References
• Chapter 15: Ethics and Laws: Governing Generative AI’s Role in Financial Systems
• Introduction
• Applications of AI in Financial Systems
• Ethical Challenges
• Ethical AI in Indian Finance: Case Studies and Insights
• Conclusion
• References
• Chapter 16: Generative AI Tools for Product Design and Engineering
• 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
• Chapter 17: AI-Driven Generative Design Redefines the Engineering Process
• 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
• Chapter 18: Insurance Disruption: Analytics on Blockchain Transforming Indian Insurance Industry
• 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
• Chapter 19: Application of Explainable Artificial Intelligence in Fintech
• 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
• 19.3.1 Bankruptcy and Credit Risk Prediction
• 19.4 Advancements of XAI in Financial Predictions
• 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
• Chapter 20: Empowering Financial Efficiency in India
• 20.1 Introduction
• 20.1.1 Background of AI in Accounting and Finance
• 20.1.2 Significance of AI 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
• Chapter 21: Framework and Interface: The Backbone of AI Systems in Banking in India
• 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
• Chapter 22: Harnessing Generative AI for Engineering and Product Design: Conceptualization, Techniques, Advancements and Challenges
• 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 Product Design Phases
• 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