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%.