ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.224
Analysis and Prediction of P2P Online Lending Platform—Based on Binary Logistic Regression Model
Abstract— This paper uses 17 indicators to analyze the critical factors that affect the P2P online lending
platforms based on a binary logistic regression model. It shows that the problematic P2P online lending
platforms are closely related to four indicators: the average interest rate, the top ten borrowers in terms of the
repayment amount, the operating duration, and the top ten investors to receive the repayments. The accuracy
of the regression model is up to 73% to predict whether the P2P network lending platform will be in a
problem.
Index Terms— binary logistic regression model, problematic platform, online loan, peer-topeer
Yuanyuan Zhang
School of Management, Shanghai University, CHINA
Yuelin Shen
College of Business Administration, Zhejiang University of Finance and Economics, CHINA
ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.17Xsrc="http://www.wcse.org/uploadfile/2019/0823/20190823055609629.png" style="width: 120px; height: 68px;" />[Download]
Cite: Yuanyuan Zhang, Yuelin Shen, "Analysis and Prediction of P2P Online Lending Platform—Based on Binary Logistic Regression Model," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 1289-1295, Beijing, 25-27 June, 2017.