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Artificial Intelligence for Risk Mitigation in the Financial Industry
| Artificial Intelligence for Risk Mitigation in the Financial Industry |
| Autore | Mishra Ambrish Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (376 pages) |
| Disciplina | 332.0285/63 |
| Altri autori (Persone) |
AnandShweta
DebnathNarayan C PokhariyalPurvi PatelArchana |
| Soggetto topico |
Finance - Technological innovations
Artificial intelligence - Economic aspects |
| ISBN |
1-394-17557-4
1-394-17556-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Artificial Intelligence in Risk Management -- 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 -- Chapter 2 Application of Artificial Intelligence in Risk Assessment and Mitigation in Banks -- 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.
Chapter 3 Artificial Intelligence and Financial Risk Mitigation -- 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 -- Chapter 4 Artificial Intelligence Adoption in the Indian Banking and Financial Industry: Current Status and Future Opportunities -- 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 -- Chapter 5 Impact of AI Adoption in Current Trends of the Financial Industry -- 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 -- Chapter 6 Artificial Intelligence Applications in the Indian Financial Ecosystem -- 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 -- Chapter 7 The Extraction of Features That Characterize Financial Fraud Behavior by Machine Learning Algorithms -- 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 -- Chapter 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 -- 8.1 Introduction -- 8.1.1 Cyber Hygiene -- 8.2 VIP Clients -- 8.3 Cyber Defense Against Simple Threats -- 8.4 Conclusion -- References -- Chapter 9 Determinants of Financial Distress in Select Indian Asset Reconstruction Companies Using Artificial Neural Networks -- 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. Chapter 10 The Framework of Feature Extraction for Financial Fraud Behavior and Applications. |
| Record Nr. | UNINA-9910877463803321 |
Mishra Ambrish Kumar
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Science with Semantic Technologies : Theory, Practice and Application
| Data Science with Semantic Technologies : Theory, Practice and Application |
| Autore | Patel Archana |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (456 pages) |
| Altri autori (Persone) |
DebnathNarayan C
BhusanBharat |
| Collana | Advances in Intelligent and Scientific Computing Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-86533-6
1-119-86532-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910623984503321 |
|
Patel Archana
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent Data Analytics in Business : Select Proceedings of ICDABM 2022 / / edited by Kiran Chaudhary, Mansaf Alam, Narayan C. Debnath
| Intelligent Data Analytics in Business : Select Proceedings of ICDABM 2022 / / edited by Kiran Chaudhary, Mansaf Alam, Narayan C. Debnath |
| Autore | Chaudhary Kiran |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (196 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
AlamMansaf
DebnathNarayan C |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Computational intelligence
Quantitative research Artificial intelligence Business information services Computational Intelligence Data Analysis and Big Data Artificial Intelligence IT in Business |
| ISBN | 981-9953-58-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Examining Socially Responsible Investing Behaviour of Individuals based on Expert Opinion using DEMATEL -- Chapter 2. Research on the Ecological Environment Evaluation of Technological Talents in the Shuangcheng Economic Circle of Chengdu Region -- Chapter 3. Current Landscape & Future Prospects of Community Healthcare Information System: A Conceptual study w.r.t Indian Healthcare -- Chapter 4. Fake News Detection Using Machine Learning -- Chapter 5. Status of basic Digital Tools Awareness among Women Vegetable Vendors and consequences-Hyderabad local markets -- Chapter 6. Influence of COVID-19 on Punjab Textile Industry -- Chapter 7. A New Transformation for Manufacturing Industries with Big Data Analytics and Industry 4.0 -- Chapter 8. Analysis for Detection in MANETs: Security Perspective -- Chapter 9. Exploring the Role of Business Intelligence Systems in IoT-Cloud Environment -- Chapter 10. Leading a Culturally Diverse Team in the Workplace -- Chapter 11. An Integrated Secure Blockchain and Deep Neural Network Framework for Better Agricultural Business Outcomes -- Chapter 12. ASCII Strip Based Novel Approach of Information Hiding in Frequency Domain -- Chapter 13. Investigating the Mediating Effect of EWOM While Exploring a Destination in Uttarakhand: A Tourist Perspective -- Chapter 14. Investigating the Factors that lead towards Intention to use Mobile commerce among Higher Education students -- Chapter 15. Strategic Plans for Market Campaign Using Machine Learning Algorithms -- Chapter 16. Gap Analysis of Electricity Demand and Supply in perspective of PROSUMERS of 5 KW Solar Rooftop Plants in Uttarakhand. |
| Record Nr. | UNINA-9910744510503321 |
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Chaudhary Kiran
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of International Conference on Computational Intelligence and Data Engineering : Proceedings of ICCIDE 2018 / / edited by Nabendu Chaki, Nagaraju Devarakonda, Anirban Sarkar, Narayan C. Debnath
| Proceedings of International Conference on Computational Intelligence and Data Engineering : Proceedings of ICCIDE 2018 / / edited by Nabendu Chaki, Nagaraju Devarakonda, Anirban Sarkar, Narayan C. Debnath |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XII, 258 p. 138 illus., 87 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes on Data Engineering and Communications Technologies |
| Soggetto topico |
Computational intelligence
Data mining Big data Computational Intelligence Data Mining and Knowledge Discovery Big Data |
| ISBN | 981-13-6459-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Performance Evaluation of Guest Operating System Following Obliteration of Pre-Installed Features -- Formalization of SOA Design Patterns Using Model-Based Specication Technique -- Computational Analysis of Differences in Indian and American Poetry -- Feature Selection for Driver Drowsiness Detection -- Monitoring and Recommendations of Public Transport Using GPS Data -- An Efficient Automatic Brain Tumor Classification using LBP Features and SVM based Classifier -- Analyzing Student Performance in Engineering Placement Using Data Mining -- Cepstrum Based Road Surface Recognition Using Long Range Automotive Radar -- Market Data Analysis by Using Support Vector Machine Learning Technique -- Pre-processing Techniques for Detection of Blurred Images -- Automatic Edge Detection and Growth Prediction of Pleural Effusion Using Raster Scan Algorithm -- Automatic Evaluation of Programming Assignments Using Information Retrieval Techniques. |
| Record Nr. | UNINA-9910483871703321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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