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 integrate market, credit, and liquidity risk perspectives, exploring a range of mathematical models including stochastic processes, Monte Carlo simulations, and Extreme Value Theory to quantify and manage these risks. 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. 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.