Research on Credit Risk Assessment of P2P Network Platform: Based on the Logistic Regression Model of Evidence Weight

  • Zhang Yuan Colleage of Economics and Management Nanjing University of Aeronautics and Astronautics.
Keywords: Network Credit, Credit Risk Assessment, Logistic Regression, Weight of Evidence.

Abstract

As an emerging credit model, P2P network credit has been developing rapidly in recent years. At the same time, it also faces many credit risk problems. This paper focuses on the credit risk of borrowers, and constructs a model of WOE and logistic regression to evaluate the risk assessment of China’s P2P network platform, Hong ling Venture. The research results show that the main factors that affect the loan success rate of P2P lending platform include loan amount, annual interest rate, bidding transaction amount and proportion of repayment on time and so on. By constructing the model of combination of the logistic regression with weight of evidence, this paper provides an appropriate method to manipulate the borrowing information of loan borrowers and evaluates the borrowing behavior of borrowers simultaneously, so that P2P credit platform can reduce the credit risk caused by borrower.

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Published
2018-02-14
How to Cite
Yuan, Z. (2018). Research on Credit Risk Assessment of P2P Network Platform: Based on the Logistic Regression Model of Evidence Weight. Journal of Research in Business, Economics and Management, 10(2), 1874-1881. Retrieved from http://scitecresearch.com/journals/index.php/jrbem/article/view/1415
Issue
Vol 10 No 2: JRBEM
Section
Articles

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