A Dynamic Credit Risk Assessment Model Based on Deep Reinforcement Learning

This study presents a new credit risk assessment model based on deep learning (DRL), addressing the limitations of static models in adapting to financial changes. The model leverages the Deep Q-Network architecture developed with the importance of replication, DQN distribution, and ring noise to capture the correlations and non-linearities in the credit data and transition to financial reform. An analysis of the extensive data of 1,250,000 credit applications shows the best performance model, achieving an AUC-ROC of 0.92 and a 15% improvement in the ratio for state-of-the-art machine learning methods. The DRL model exhibits remarkable adaptability in simulated dynamic scenarios, maintaining an average AUC-ROC of 0.89 across various economic shocks and market shifts. An ablation study reveals the significant contributions of each model component, with prioritized experience replay emerging as the most impactful for adaptability. The proposed approach offers promising implications for enhancing credit risk management practices, potentially enabling more inclusive lending while mitigating default risks. However, challenges remain in model interpretability and potential bias perpetuation, necessitating further research into transparent and fair AI-driven credit assessment systems. This study contributes to the growing body of research applying advanced machine-learning techniques to critical financial decision-making processes.