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

摘要

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.