Dynamic credit rating evaluation algorithm for customers using reinforcement learning and multi-feature weight analysis

Effective credit rating evaluation is critical for financial institutions to manage risks and ensure sustainable operations. This study proposes a dynamic credit rating evaluation algorithm based on Reinforcement Learning (RL), integrating multi-feature weight analysis to adaptively assess customer creditworthiness. The RL framework models the credit evaluation process as a sequential decision-making problem, where actions correspond to dynamic adjustments in customer credit ratings. A multi-feature weight analysis mechanism is incorporated to capture the relative importance of key factors, such as income stability, transaction history, and debt-to-income ratio, ensuring robust credit assessments. Experimental validation using real-world financial datasets demonstrates the algorithm’s ability to improve classification accuracy by 12% compared to conventional machine learning models, while effectively adapting to changing customer behaviors. The results highlight the potential of Reinforcement Learning in enhancing the accuracy and adaptability of credit rating systems.