Loan Default Prediction Method Based on Sample Optimisation and Bagging Integration With CatBoost‐GRU

Currently, with the development of the financial market and the increase of personal credit products, the phenomenon of personal loan default has also attracted widespread attention. How to make effective predictions of loan default has become a research hotspot and difficulty in this field. This article addresses the problems in existing loan default prediction methods. Based on the domain knowledge of consumer loan defaults and machine learning theory, it is proposed to use a hybrid sampling method based on Mahalanobis distance SMOTEENN to balance the dataset. The sampling method is further extended from the original credit card fraud research to the study of loan default prediction. The balanced dataset is used to integrate categorical boosting (CatBoost)‐GRU model with Bagging for consumer loan default prediction. First, the loan dataset of Lending Club platform is selected for default prediction experiments. Then, to further validate the effectiveness of the method, the method is applied to the loan default dataset of Kiva for further validation. Finally, through the comparison of the experimental results, it is proved that the method based on Mahalanobis Distance SMOTEENN hybrid sampling and Bagging integrating CatBoost‐GRU can be very effective in loan default prediction. The method has strong application potential and practical effects in loan default prediction research, which not only improves the accuracy and efficiency of default prediction but also provides a wide range of insights and methodological references for the solution of similar problems.