ENHANCING STRESS TESTING AND RISK MANAGEMENT USING DEEP LEARNING: THE ROLE OF ENTERPRISE RISK MANAGEMENT IN US COMMERCIAL BANKS

This article investigates the application of deep learning techniques in enhancing stress testing frameworks for US commercial banks, particularly during economic downturns, and evaluates the role of enterprise risk management (ERM) in improving bank resilience.Stress testing is a critical tool for measuring a bank's ability to withstand financial and economic shocks, but traditional models often lack precision and adaptability in rapidly changing economic conditions.Deep learning models, with their ability to process vast amounts of data and identify complex patterns, offer an innovative approach to improving the accuracy and sensitivity of stress tests.This study explores how deep learning can simulate diverse economic downturns, assess the impact of these stressors on bank performance, and reveal vulnerabilities.Furthermore, the article analyses how ERM frameworks mitigate risks identified through deep learning-enhanced stress tests, providing a comparative analysis between banks with effective ERM systems and those without.By developing predictive models for assessing liquidity, credit, and market risks during downturn scenarios, this research highlights the interplay between ERM-driven decision-making and stress test outcomes.Ultimately, the findings underscore the potential of integrating deep learning with robust ERM strategies to strengthen risk management practices and enhance the stability of commercial banks during periods of economic uncertainty.