User Data Can Tell Defaulters in P2P Lending

Jackson J. Mi (), Tianxiao Hu () and Luke Deer ()
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Jackson J. Mi: Fudan University
Tianxiao Hu: Fudan University
Luke Deer: The University of Sydney

Annals of Data Science, 2018, vol. 5, issue 1, No 6, 59-67

Abstract: Abstract Online peer-to-peer (P2P) lending service is a new type of financial platforms that enables individuals borrow and lend money directly from one to another. As P2P lending service is rapidly developing, a number of rating systems of borrowers’ creditworthiness are published by different P2P lending companies. However, whether these rating systems could truly reflect the creditworthiness and loan risk of borrowers is unconfirmed. In this paper, we analyzed the differences between credit levels and users’ distribution of CPLP to evaluate if the credit levels can truly reflect the borrowers’ credit. We used soft factors to establish a model that can find borrowers who are likely to default. Further, we proposed some strategies to construct and improve the risk-control of P2P lending platforms according to the result of our research.

Keywords: Peer-to-peer lending; Risk rating; Data mining (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s40745-017-0134-z

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