P2P lending scoring models: Do they predict default?

Paolo Giudici and Branka Hadji Misheva

Journal of Digital Banking, 2018, vol. 2, issue 4, 353-368

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.

Keywords: credit ratings; default prediction; logistic regression models; consumer credit (search for similar items in EconPapers)
JEL-codes: E5 G2 (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://hstalks.com/article/792/download/ (application/pdf)
https://hstalks.com/article/792/ (text/html)
Requires a paid subscription for full access.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aza:jdb000:y:2018:v:2:i:4:p:353-368

Access Statistics for this article

More articles in Journal of Digital Banking from Henry Stewart Publications
Bibliographic data for series maintained by Henry Stewart Talks ().