Borrower Risk Identification of P2P Platform Based on Support Vector Machine
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DOI: 10.25236/icess.2019.203
Corresponding Author
Xiaozhen Yu
Abstract
In order to improve the risk control ability of the P2P online lending platform, the paper applies the support vector machine to the identification of borrower default risk. The paper chooses nonlinear support vector machine to analyze the data of borrowers of a China online lending platform, in which the kernel function selects the radial basis function. The results show that the SVM algorithm can effectively help the P2P network lending platform to improve the ability of identify the default risk of borrowers.
Keywords
P2P online lending, Support vector machine, Radial basis function, Default risk identification