Stress Testing Financial Systems– Simulating economic disruption using AI-driven risk models

Abstract

The increasing interconnectedness and complexity of global financial markets have increased the stakes for advanced risk assessment methods. Traditional financial stress testing based on static rule-based models, historic datasets, and past crisis data which is poorly suited to address the nonlinear relationships and rapidly evolving risk factors characteristic of modern economies. This paper explores the use of Artificial Intelligence (AI) in financial stress testing with machine learning (ML), deep reinforcement learning (DRL), and generative AI to simulate systemic economic shocks and predict financial instability better. It also considers social media activities, geopolitical situations, climate change, pandemics, global financial markets, emerging technologies. This study provides a mechanized AI-based implementation plan for financial stress testing, data engineering pipeline profiling, model selection methodologies (LSTMs, GANs, and XGBoost), and real-time risk monitoring approaches. Financial institution case studies such as the Federal Reserve, Bank of England, and hedge funds such as BlackRock show how AI enhances prediction accuracy, reduces risk assessment cycles, and provides real-time financial crisis management approaches. In addition, this paper also opens up the prospects of Quantum AI, DeFi risk modeling, and digital twins powered by AI to revolutionize systemic risk analysis and crisis forecasting in finance. Our findings show that AI-powered financial stress tests are capable of significantly enhancing risk resilience, early warning, and global financial stability. Studies on XAI methods, audit architectures under regulatory directives, and the combination of quantum computing with AI-powered financial modeling for enhancing financial sector risk assessment even further is a direction that should be explored in future research.