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%.
Highlights
With the development of Internet technology, private lending has developed from offline to online
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
Taking the user data published by lending club as the research object, this paper uses the method of recursive feature elimination combined with classification algorithm to identify and predict the credit default of borrowers
Summary
With the development of Internet technology, private lending has developed from offline to online. In February 2019, the Beijing Internet finance industry association published “a notice on the list of borrowers and institutions that evaded and abandoned their debts by online lending institutions in Beijing”, which published 300 borrowers who evaded and abandoned their debts. Among these 300 borrowers, more than 100 borrowers’ overdue time occurred during the “storm” of P2P platforms in 2018. Only 66 of the 300 borrowers have not lost contact, with a loss ratio of 78% and overdue amount of 164,100 yuan, which has brought huge losses to investors and platforms It is urgent for the current P2P industry to identify borrowers’ default behaviors. The recursive feature elimination method is used to select the key information of the borrower, eliminate the multicollinearity of the data, and predict the default behavior of P2P borrowers by combining Logistic regression model, CART decision tree and BP neural network model
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