Real-Time Risk Management in Finance Using Reinforcement Learning for Stress Testing

This research explores the application of reinforcement learning (RL) in real-time risk management for financial stress testing. The RL agent simulates volatile market conditions, regulatory shocks, and counterparty failures to assess portfolio vulnerabilities. By continuously learning from live economic indicators and extreme events, such as pandemics and geopolitical conflicts, the system adapts and evolves, offering financial institutions a proactive approach to managing risks. This study aims to improve the resilience of financial portfolios, preventing systemic collapses in a constantly changing economic landscape.