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P2P lending scoring models: Do they predict default?

Author

Listed:
  • Giudici, Paolo
  • Misheva, Branka Hadji

Abstract

Due to technological advancement, peer-to-peer (P2P) platforms have allowed significant cost reductions in lending. This improved allocation, however, comes at a higher credit risk. In this paper, the authors investigate the effectiveness of credit scoring models employed by P2P platforms with respect to loan default prediction. They argue that because of differences in risk ownership with respect to traditional lenders, the rating grades obtained from P2P scoring models may not be the best predictors of loan default.

Suggested Citation

  • Giudici, Paolo & Misheva, Branka Hadji, 2018. "P2P lending scoring models: Do they predict default?," Journal of Digital Banking, Henry Stewart Publications, vol. 2(4), pages 353-368, May.
    • Handle: RePEc:aza:jdb000:y:2018:v:2:i:4:p:353-368
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    More about this item

    Keywords

    credit ratings; default prediction; logistic regression models; consumer credit;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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