Machine Learning and AI in Dynamic Financial Risk Management: A Review of Quantitative Models and Applications

Abstract

The integration of Machine Learning (ML) and Artificial Intelligence (AI) into financial risk management has revolutionised the methodologies that institutions employ to assess, monitor, and mitigate various forms of risk. Traditional risk models, which often rely on static assumptions and historical data, are increasingly being supplanted by dynamic, data-driven frameworks capable of processing large volumes of high-frequency and unstructured data from diverse sources, including market feeds, transactional data, social media sentiment, and alternative datasets. This review systematically examines the application of ML and AI-based
quantitative models across critical financial risk domains, including credit risk, market risk, liquidity risk, and operational risk, highlighting how these technologies enhance predictive accuracy, risk sensitivity, and early warning capabilities. Key advances in supervised learning, unsupervised learning, deep learning, reinforcement learning, and explainable AI (XAI) are discussed, emphasising their potential to provide interpretable insights while maintaining regulatory compliance. Furthermore, the study explores the architectural frameworks for implementing these models, addressing integration with existing risk management
infrastructures, data governance, model validation, and ethical considerations. The review also identifies current challenges, such as model overfitting, data bias, explainability limitations, and operational complexities, while proposing future research directions aimed at developing adaptive, transparent, and resilient financial risk systems. By bridging the gap between cutting- edge AI methodologies and practical risk management applications, this work provides a comprehensive roadmap for leveraging intelligent technologies to enhance institutional risk oversight, strategic decision-making, and regulatory alignment.