Guo, Y.H., Zhou, W.J., Luo, C.Y., Liu, C.R. and Xiong, H. (2016) Instance-Based Credit Risk Assessment for Investment Decisions in P2P Lending. European Journal of Operational Research, 249, 417-426.
https://doi.org/10.1016/j.ejor.2015.05.050

has been cited by the following article:

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

  AUTHORS: Xianyan Hou

  KEYWORDS: P2P Networks Lending, Recursive Feature Elimination, Classification Model, Credit Default Risk

  JOURNAL NAME: Open Journal of Business and Management, Vol.8 No.2, March 24, 2020

  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%.