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Annals of Data Science

, Volume 5, Issue 1, pp 59–67 | Cite as

User Data Can Tell Defaulters in P2P Lending

  • Jackson J. MiEmail author
  • Tianxiao Hu
  • Luke Deer
Article
  • 400 Downloads

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 

Notes

Acknowledgements

The work was supported by National Natural Science Foundation of China Projects No. 71503165, No. 91546105 and the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund. The authors would like to thank Tinghang Liu for his early idea and comments.

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Copyright information

© Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Data Science, School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Department of Government and International RelationsThe University of SydneySydneyAustralia
  3. 3.Cambridge Centre for Alterantive FinanceThe University of CambridgeCambridgeEngland

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