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The mechanism and effectiveness of credit scoring of P2P lending platform

Author

Listed:
  • Qiang Li
  • Liwen Chen
  • Yong Zeng

Abstract

Purpose - The purpose of this paper is to investigate the mechanism how the platform obtains and uses undisclosed information to determine individual borrowers’ credit score and to examine the effectiveness of credit scoring in predicting default. The motivation stems from the fact that there is little evidence about the role of P2P platform, which has been positioned as a kind of information intermediary. Design/methodology/approach - Using a sample of 5,176 unsecured P2P loans having expired before December 31, 2015 on Renrendai.com and an approach of two-stage regression, the paper first estimates the undisclosed information embedded in credit score by regressing credit score on four types of public information about a borrower’s creditworthiness. Then, the authors use a Logit regression to examine the role of the excess information in predicting the default probability. Findings - The certification information provided by the platform is the most important determinant for a borrower’s credit score and the undisclosed information embedded in credit score can predict the loan performance better than the public information of posted listings. Moreover, the predictive ability of the undisclosed information is better for high-risk borrowers than for low-risk ones. Research limitations/implications - Providing a credit score for each individual is a way for P2P platforms to play an information intermediary role. More evidence about whether or how a platform plays its role are worthy to be exploited by investigating a platform’s operating policies in detail and doing cross-platform comparative studies. Practical implications - The results about the effect of various types of information on loan performance can provide an insightful guidance for P2P platforms to optimize their mechanism on information disclosure and credit scoring. Originality/value - The existing literature mainly focuses on the effects of information voluntarily disclosed by borrowers and the behaviors of investors on P2P lending outcomes. The paper highlights the information intermediary role played by the platform and presents empirical evidence that credit scoring for individual borrowers is a way for P2P platforms to promote the direct lending for individual.

Suggested Citation

  • Qiang Li & Liwen Chen & Yong Zeng, 2018. "The mechanism and effectiveness of credit scoring of P2P lending platform," China Finance Review International, Emerald Group Publishing Limited, vol. 8(3), pages 256-274, May.
  • Handle: RePEc:eme:cfripp:cfri-06-2017-0156
    DOI: 10.1108/CFRI-06-2017-0156
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    Citations

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    Cited by:

    1. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    2. Ruochen Xiao & Yingying Feng & Lei Yan & Yihan Ma, 2022. "Predict stock prices with ARIMA and LSTM," Papers 2209.02407, arXiv.org.

    More about this item

    Keywords

    Credit scoring; Default probability; Information certification; Peer-to-peer lending; G23; G24;
    All these keywords.

    JEL classification:

    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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