Quantitative Foundations for Integrating Market, Credit, and Liquidity Risk with Generative AI

This paper establishes a foundational framework for the application of Generative AI in financial risk management by providing a comprehensive overview and review of essential quantitative techniques. We illustrate the quantitative aspect of these models in equity, fixed income, and mortgage-backed securities markets, emphasizing their proposed Gen AI enhancement in enhancing risk management practices. Furthermore, this paper examines the transformative potential of Generative AI, specifically Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), in reshaping risk modeling and stress testing. We bridge classical quantitative techniques—including Monte Carlo methods, Value-at-Risk, and stochastic processes—with modern generative models like GANs and VAEs. The study first reviews foundational models for market, credit, and liquidity risk, demonstrating their application across fixed income and mortgage-backed securities markets. We then propose novel AI-enhanced methodologies for: (1) liquidity risk quantification through augmented Monte Carlo simulations, (2) dynamic credit risk modeling using agentic frameworks, and (3) stress testing with synthetic scenario generation. A key contribution is the development of hybrid architectures that combine traditional risk metrics with data-driven generative AI, implemented through practical Python workflows. The paper concludes with empirical results showing improved accuracy in Expected Liquidity Shortfall (ELS) estimation and Value-at-Risk calculations, providing a pathway for next-generation risk management systems. By laying this quantitative foundation, we aim to facilitate the adoption of advanced Gen AI techniques, offering a forward-looking perspective on the future of quant-data-driven financial risk management.