Ensemble methods for credit scoring of Chinese peer-to-peer loans

Risk control is a central issue for Chinese peer-to-peer (P2P) lending services. Although credit scoring has drawn much research interest and the superiority of ensemble models over single machine learning models has been proven, the question of which ensemble model is the best discrimination method for Chinese P2P lending services has received little attention. This study aims to conduct credit scoring by focusing on a Chinese P2P lending platform and selecting the optimal subset of features in order to find the best overall ensemble model. We propose a hybrid system to achieve these goals. Three feature selection algorithms are employed and combined to obtain the top 10 features. Six ensemble models with five base classifiers are then used to conduct comparisons after synthetic minority oversampling technique (SMOTE) treatment of the imbalanced data set. A real-world data set of 33,966 loans from the largest lending platform in China (ie, the Renren lending platform) is used to evaluate performance. The results show that the top 10 selected features can greatly improve performance compared with all features, particularly in terms of discriminating "bad";loans from "good" loans. Moreover, comparing the standard evaluations, robustness tests and statistical tests suggests that the gradient boosting decision tree, random forest and rotation forest methods are the best. Our findings can help risk managers and investors by providing them with correct warning signals and the main factors influencing "bad";loans, so that they can take corrective actions and reduce risk.