Deep reinforcement learning-based credit risk prediction model for multi-attribute data

Abstract

This paper aims to develop a multi-attribute data-based credit risk prediction model using the deep reinforcement learning (DRL) approach. In the context of creditworthiness evaluation, a model is needed that is not only accurate but also adaptive to complex data dynamics and risk patterns. This study uses a dataset of 5,000 entries that have gone through a preprocessing process, with features such as credit score, income, age, loan amount, and employment history. Initial correlation analysis through heatmap visualization shows that credit score has a strong negative relationship with credit risk, while other features show weak to moderate correlations. The evaluation of model performance is shown through the area under the curve (AUC) value on the receiver operating characteristic (ROC) curve of 0.88, which indicates excellent discrimination ability. The final evaluation results show that the DRL model has an accuracy of 91%, precision of 89%, recall of 90%, and F1-score of 89.5%, indicating that the model not only has high accuracy but is also balanced in its sensitivity and specificity. Thus, the application of DRL has proven effective in predicting credit risk adaptively and accurately, and can be implemented as an intelligent solution in an automated credit decision-making system based on multi-attribute data.

Keywords

• Adaptive model
• Credit risk
• Deep learning
• Financial risk
• Multi-attribute