IDEAS home Printed from https://ideas.repec.org/a/mes/emfitr/v55y2019i1p118-132.html
   My bibliography  Save this article

Predicting Prepayment and Default Risks of Unsecured Consumer Loans in Online Lending

Author

Listed:
  • Zhiyong Li
  • Ke Li
  • Xiao Yao
  • Qing Wen

Abstract

Online lending provides a means of fast financing for borrowers based on their creditworthiness. However, borrowers may undermine this agreement due to early repayment or default, which are two major concerns for the platform and lenders, since both affect the profitability of a loan. While default risk is frequently focused on credit scoring literature, prepayment has received much less attention, despite a higher prepayment rate being observed in online lending when compared with default. This article uses multivariate logistic regression to predict the probability of both the underlying prepayment and default risks. Real consumer lending data of 140,605 unsecured loans provides evidence that these two events have their own distinct patterns. We consider systemic risk by incorporating macroeconomic factors in modeling and address the influence of economic conditions, which are lessons learnt from the last financial crisis. The out-of-sample validation has shown that both prepayment and default can be accurately predicted. This article highlights the necessity of regulations on prepayment given the fast growing online lending market.

Suggested Citation

  • Zhiyong Li & Ke Li & Xiao Yao & Qing Wen, 2019. "Predicting Prepayment and Default Risks of Unsecured Consumer Loans in Online Lending," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(1), pages 118-132, January.
    • Handle: RePEc:mes:emfitr:v:55:y:2019:i:1:p:118-132
    DOI: 10.1080/1540496X.2018.1479251
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1540496X.2018.1479251
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/1540496X.2018.1479251?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Pei-Fen & Lo, Shihmin & Tang, Hai-Yuan, 2022. "What if borrowers stop paying their loans? Investors’ rates of return on a peer-to-peer lending platform," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 359-377.
    2. Dongwoo Kim, 2023. "Can investors’ collective decision-making evolve? Evidence from peer-to-peer lending markets," Electronic Commerce Research, Springer, vol. 23(2), pages 1323-1358, June.
    3. 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).
    4. Chen, Shou & Jiang, Xiangqian & He, Hongbo & Zhou, Xi, 2020. "A pricing model with dynamic repayment flows for guaranteed consumer loans," Economic Modelling, Elsevier, vol. 91(C), pages 1-11.
    5. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    6. Jiaming Liu & Jiajia Liu & Chong Wu & Shouyang Wang, 2024. "Enhancing credit risk prediction based on ensemble tree‐based feature transformation and logistic regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 429-455, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mes:emfitr:v:55:y:2019:i:1:p:118-132. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/MREE20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.