P2P Borrower Default Identification and Prediction Based on RFE-Multiple Classification Models

Abstract

P2P network lending, as a new type of lending model for Internet finance, is favored by people because of its fast and low cost. However, borrower default has always been one of the core issues of platform concern. Because borrower characteristic data has the characteristics of high dimensionality and multicollinearity, how to select key features to judge borrowing default behavior has been a hot topic. To solve this problem, this paper uses the data of the lending club lending platform to introduce the recursive feature elimination method (RFE) to select key variables, and combines with the classification model to predict the borrower’s default behavior. The research results show that the recursive feature elimination method can screen the key variables affecting the default of the borrower. After the recursive feature elimination method, the accuracy of the classification model is over 95%.