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Chapter 13 Leveraging Artificial Intelligence for Enhanced Risk Management in Banking: A Systematic Literature Review

  • Narayanage Jayantha Dewasiri , Dunusinghe G. Dharmarathna und Mrinalini Choudhary
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Artificial Intelligence Enabled Management
Ein Kapitel aus dem Buch Artificial Intelligence Enabled Management

Abstract

This systematic review delves into the transformative role of Artificial Intelligence (AI) in the banking industry’s risk management practices. AI, encompassing machine learning, data analytics, and natural language processing, has enhanced risk assessment, mitigation, and decision-making processes. The findings emphasise AI’s capacity to identify and assess risks, enabling proactive risk management effectively. Applications like credit scoring models, fraud detection systems, and stress testing tools play instrumental roles in optimising risk management processes. At the same time, the importance of data quality, governance, and transparency cannot be overstated in successfully implementing AI-driven risk management strategies. The implications of AI in banking are profound, offering data-driven procedures, equitable lending practices, and enhanced operational efficiency. However, data privacy concerns, model interpretability issues, and regulatory compliance complexities must be addressed carefully. Emerging trends in AI for risk management encompass Explainable AI, AI-enabled regulatory Compliance, AI for Cybersecurity Risk Management, and Natural Language Processing for Unstructured Data Analysis, along with the optimisation of efficiency through Robotic Process Automation in Risk Operations. Future research should focus on ethical considerations, dynamic stress testing models, AI’s role in climate-related risk analysis, human-AI collaboration, cybersecurity risk prediction, and the development of robust regulatory frameworks for AI integration in risk management. AI stands poised to revolutionise banking risk management. Still, responsible and ethical integration is paramount, necessitating collaborative efforts to harness its full potential while ensuring trust and stability within the sector.

Abstract

This systematic review delves into the transformative role of Artificial Intelligence (AI) in the banking industry’s risk management practices. AI, encompassing machine learning, data analytics, and natural language processing, has enhanced risk assessment, mitigation, and decision-making processes. The findings emphasise AI’s capacity to identify and assess risks, enabling proactive risk management effectively. Applications like credit scoring models, fraud detection systems, and stress testing tools play instrumental roles in optimising risk management processes. At the same time, the importance of data quality, governance, and transparency cannot be overstated in successfully implementing AI-driven risk management strategies. The implications of AI in banking are profound, offering data-driven procedures, equitable lending practices, and enhanced operational efficiency. However, data privacy concerns, model interpretability issues, and regulatory compliance complexities must be addressed carefully. Emerging trends in AI for risk management encompass Explainable AI, AI-enabled regulatory Compliance, AI for Cybersecurity Risk Management, and Natural Language Processing for Unstructured Data Analysis, along with the optimisation of efficiency through Robotic Process Automation in Risk Operations. Future research should focus on ethical considerations, dynamic stress testing models, AI’s role in climate-related risk analysis, human-AI collaboration, cybersecurity risk prediction, and the development of robust regulatory frameworks for AI integration in risk management. AI stands poised to revolutionise banking risk management. Still, responsible and ethical integration is paramount, necessitating collaborative efforts to harness its full potential while ensuring trust and stability within the sector.

Kapitel in diesem Buch

  1. Frontmatter I
  2. Preface V
  3. Acknowledgments VII
  4. Contents IX
  5. Part I: Introduction to Data Enabled Management
  6. Chapter 1 What Does Artificial Intelligence–Powered ChatGPT Bring to Academia? A Review 1
  7. Chapter 2 Education Policies Through Data Driven Decision Making: Accelerating Inclusive Education for People with Disabilities 15
  8. Chapter 3 The Role of Artificial Intelligence in the Emerging Digital Economy Era 33
  9. Chapter 4 A Review of Machine Learning Methods for Diagnosis and Classification of Thyroid Disease 51
  10. Chapter 5 A Question and Answering System Using Natural Language Processing and Deep Learning 65
  11. Part II: Role of AI and Big Data in Management Functions
  12. Chapter 6 The Reinvention of HRM Practices Through Artificial Intelligence: Opportunities and Challenges in the Digital World of Work 87
  13. Chapter 7 Challenges and Artificial Intelligence–Centered Defensive Strategies for Authentication in Online Banking 105
  14. Chapter 8 Catalyzing Human Potential: The Crucial Role of AI in Modern HR Management 119
  15. Chapter 9 Exploring How Artificial Intelligence is Changing the HRM Landscape: Refuting the Fiction with Reality! 131
  16. Chapter 10 Artificial Intelligence in HR: Employee Engagement Using Chatbots 147
  17. Part III: Application of AI in Different Sectors
  18. Chapter 11 An Empirical Analysis of Artificial Intelligence Applications of Manufacturing Companies in Turkey 165
  19. Chapter 12 A Comprehensive View of Artificial Intelligence (AI)–Based Technologies for Sustainable Development Goals (SDGs) 183
  20. Chapter 13 Leveraging Artificial Intelligence for Enhanced Risk Management in Banking: A Systematic Literature Review 197
  21. Chapter 14 Exploring the Influence of Artificial Intelligence on the Management of Hospitality and Tourism Sectors: A Bibliometric Overview 215
  22. Chapter 15 Artificial Intelligence in Healthcare Sector in India: Application, Challenges and a Way Forward 233
  23. Chapter 16 Application of Artificial Intelligence and Machine-Learning Algorithms for Forecasting Risk: The Case of the Indian Stock Market 249
  24. List of Figures 263
  25. List of Tables 265
  26. About the Editors 267
  27. Index 269
https://doi.org/10.1515/9783111172408-013

Kapitel in diesem Buch

  1. Frontmatter I
  2. Preface V
  3. Acknowledgments VII
  4. Contents IX
  5. Part I: Introduction to Data Enabled Management
  6. Chapter 1 What Does Artificial Intelligence–Powered ChatGPT Bring to Academia? A Review 1
  7. Chapter 2 Education Policies Through Data Driven Decision Making: Accelerating Inclusive Education for People with Disabilities 15
  8. Chapter 3 The Role of Artificial Intelligence in the Emerging Digital Economy Era 33
  9. Chapter 4 A Review of Machine Learning Methods for Diagnosis and Classification of Thyroid Disease 51
  10. Chapter 5 A Question and Answering System Using Natural Language Processing and Deep Learning 65
  11. Part II: Role of AI and Big Data in Management Functions
  12. Chapter 6 The Reinvention of HRM Practices Through Artificial Intelligence: Opportunities and Challenges in the Digital World of Work 87
  13. Chapter 7 Challenges and Artificial Intelligence–Centered Defensive Strategies for Authentication in Online Banking 105
  14. Chapter 8 Catalyzing Human Potential: The Crucial Role of AI in Modern HR Management 119
  15. Chapter 9 Exploring How Artificial Intelligence is Changing the HRM Landscape: Refuting the Fiction with Reality! 131
  16. Chapter 10 Artificial Intelligence in HR: Employee Engagement Using Chatbots 147
  17. Part III: Application of AI in Different Sectors
  18. Chapter 11 An Empirical Analysis of Artificial Intelligence Applications of Manufacturing Companies in Turkey 165
  19. Chapter 12 A Comprehensive View of Artificial Intelligence (AI)–Based Technologies for Sustainable Development Goals (SDGs) 183
  20. Chapter 13 Leveraging Artificial Intelligence for Enhanced Risk Management in Banking: A Systematic Literature Review 197
  21. Chapter 14 Exploring the Influence of Artificial Intelligence on the Management of Hospitality and Tourism Sectors: A Bibliometric Overview 215
  22. Chapter 15 Artificial Intelligence in Healthcare Sector in India: Application, Challenges and a Way Forward 233
  23. Chapter 16 Application of Artificial Intelligence and Machine-Learning Algorithms for Forecasting Risk: The Case of the Indian Stock Market 249
  24. List of Figures 263
  25. List of Tables 265
  26. About the Editors 267
  27. Index 269
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Heruntergeladen am 14.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/9783111172408-013/html
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