Who Can Get Money? Evidence from the Chinese Peer-to-Peer Lending Platform

Qizhi Tao, Yizhe Dong, Ziming Lin

Research output: Contribution to journalArticle

11 Citations (Scopus)
9 Downloads (Pure)

Abstract

This paper explores how borrowers’ financial and personal information, loan characteristics and lending models affect peer-to-peer (P2P) loan funding outcomes. Using a large sample of listings from one of the largest Chinese online P2P lending platforms, we find that those borrowers earning a higher income or who own a car are more likely to receive a loan, pay lower interest rates, and are less likely to default. The credit grade assigned by the lending platform may not represent the creditworthiness of potential borrowers. We also find that the unique offline process in the Chinese P2P online lending platform exerts significant influence on the lending decision. We discuss the implications of our results for the design of big data-based lending markets.
Original languageEnglish
Pages (from-to)425-441
Number of pages17
JournalInformation Systems Frontiers
Volume19
Issue number3
Early online date28 Mar 2017
DOIs
Publication statusPublished - Jun 2017

Keywords

  • peer-to-peer (P2P) lending
  • Fintech
  • offline authentication
  • listing outcomes
  • information asymmetry
  • China

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