Study on Effect of Consumer Information in Personal Credit Risk Evaluation

This study selects Chinese borrowers’ information from a platform that has both online shopping and consumer loan service as sample, studies the effect of consumer information in personal credit risk evaluation, and uses the lLogistic regression model, light gradient boosting machine (LightGBM) algorithm, and Shapley Additive Explanation (SHAP). The results show that the information of all consumer loan groups cannot be covered by traditional credit information. Consumer information can help predict the behavior of borrower’s repayment and provide support for personal credit risk evaluation effective. Adding consumption information to the personal credit risk evaluation model can improve the accuracy of the model effectively. The model variables are ranked by feature importance, and there are 5 consumption indicators in the first 5 indicators of feature importance, which further verifies the value and effect of consumption information in personal credit risk evaluation. This study not only reveals the effect and value of consumer information in personal credit risk evaluation effectively, but also provides new ideas for the development of consumer financial market.