The Evolution of Monte Carlo Simulation: From Traditional Methods to AI-Driven Financial Risk Modeling

Abstract

Monte Carlo Simulation has served as a fundamental methodology in financial risk management for decades, particularly in applications such as Interest Rate Risk in the Banking Book, market risk assessment, and regulatory stress testing frameworks. While traditional Monte Carlo approaches have proven valuable for probabilistic risk modeling, they face significant limitations, including reliance on static distributions, fixed correlation matrices, a lack of macroeconomic coherence in scenario generation, and substantial computational demands. The emergence of artificial intelligence and generative AI technologies presents a transformative opportunity to address these shortcomings while preserving the core probabilistic framework that makes Monte Carlo simulation effective. This article examines the comprehensive evolution of Monte Carlo simulation through AI integration, exploring how technologies such as Variational Autoencoders, Graph Neural Networks, Generative Adversarial Networks, and diffusion models enhance each phase of the simulation lifecycle from data preparation through governance. The transformation encompasses richer data ingestion through vector databases and natural language processing, neural density estimation for capturing complex distributions, dynamic correlation modeling that adapts to market regimes, generation of macroeconomically consistent stress scenarios, accelerated valuation through neural surrogate models, and enhanced explainability via SHAP values and large language model-generated reports. Through detailed examination of Interest Rate Risk in the Banking Book applications, particularly Economic Value of Equity analysis, this article demonstrates how AI-enhanced simulation produces results that are simultaneously more accurate, comprehensive, explainable, and aligned with regulatory expectations for model risk management and stress testing transparency, representing not a replacement but an intelligent augmentation of proven quantitative methods.