DEEP LEARNING FOR FINANCIAL STRESS TESTING: A DATA-DRIVEN APPROACH TO RISK MANAGEMENT

Abstract

Financial stress testing is crucial in today's complex and data-intensive financial environment. Traditional risk assessment methods, including analytical, technical, and heuristic models, often fail to capture the intricate dependencies within financial systems. This research introduces a deep learning-based framework for financial stress testing, leveraging Correlation based Convolutional Neural Networks (CCNN) and Long Short-Term Memory (LSTM) networks to enhance risk prediction accuracy. By integrating quantitative and qualitative financial indicators, the proposed model delivers improved stability and precision. Experimental results indicate a significant reduction in training and testing loss, with final values of 0.0013 and 0.003, respectively. The model effectively estimates key financial metrics such as revenue, net income, and earnings per share (EPS) with high alignment to actual values. Moreover, the framework substantially mitigates financial risks, reducing credit risk from 0.75 to 0.20, liquidity risk from 0.70 to 0.25, market risk from 0.65 to 0.30, and operational risk from 0.80 to 0.35. These findings suggest that deep learning-driven financial stress testing enhances risk assessment, strengthens financial resilience, and supports more informed decision-making in modern financial markets.