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The integration of artificial intelligence (AI) in financial risk management systems has revolutionized traditional approaches, providing enhanced predictive capabilities and operational efficiency. This paper explores the various applications of AI in credit risk assessment, market risk analysis, operational risk management, and regulatory compliance. AI-driven systems leverage advanced machine learning algorithms to analyze vast datasets, including real-time market data and non-traditional sources, improving risk predictions and enabling proactive risk management. Scenario simulations, predictive modeling, real-time data analysis, and automated decision-making are discussed as core components of AI-driven systems. The paper also highlights the benefits of AI in automating routine tasks, enhancing data analytics, and ensuring regulatory compliance. By continuously learning and adapting to new data, AI systems offer dynamic risk management solutions that address evolving market conditions and regulatory requirements. This comprehensive analysis demonstrates how AI-driven financial risk management systems can significantly reduce the incidence of loan defaults, enhance portfolio quality, and improve the overall resilience of financial institutions.
Qi Shen (2024) studied this question.