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

Abstract

In the complicated and data-driven financial climate of today, financial stress testing is essential. Analytical, 
technical, and heuristic models are examples of traditional risk assessment techniques that often fall short of capturing 
the complex interdependencies seen in financial systems. In order to improve risk prediction accuracy, this study 
presents a deep learning-based framework for financial stress testing that makes use of correlation-based 
Convolutional Neural Networks (CCNN) and Long Short-Term Memory (LSTM) networks. The suggested approach 
improves stability and accuracy by combining quantitative and qualitative financial indicators. With final values of 
0.0013 and 0.003, respectively, the experimental findings show a considerable decrease in training and testing loss. 
Key financial indicators including sales, net income, and earnings per share (EPS) are efficiently estimated by the 
model with a high degree of alignment to actual values. Additionally, by lowering 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, the framework 
significantly reduces financial hazards. These results imply that financial stress testing powered by deep learning 
improves risk assessment, fortifies financial resilience, and facilitates more informed decision-making in 
contemporary financial markets.