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With the rapid development of P2P (peer-to-peer) online lending industry, how to effectively evaluate the borrowers’ credit risk in the platform has drawn more and more attention. In this paper, we propose a borrower credit risk assessment index system that includes basic information, work information, credit information, asset information, loan information and audit certification information, and come up with a credit risk assessment model that combines Gradient Boosting Decision Trees (GBDT) and support vector machine (SVM). Then, we select the data of P2P lending platform to carry out the empirical analysis of the credit risk assessment, and compare with the common four kinds of single prediction models such as logic regression (LR), artificial neural network (ANN), SVM and clustering algorithm. The results show that the increase of audit certification information helps to improve the forecasting effect of the model, and the credit risk assessment model of P2P lending platform based on GBDT and SVM has higher prediction accuracy and stability.

GBDT-SVM Credit Risk Assessment Model and Empirical Analysis of Peer-to-Peer Borrowers under Consideration of Audit Information