Predicting and Deterring Default with Social Media Information in Peer-to-Peer Lending

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

109 Scopus Citations
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Author(s)

  • Ruyi Ge
  • Juan FENG
  • Bin Gu
  • Pengzhu Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)401-424
Journal / PublicationJournal of Management Information Systems
Volume34
Issue number2
Online published17 Aug 2017
Publication statusOnline published - 17 Aug 2017

Abstract

This study examines the predictive power of self-disclosed social media
information on borrowers’ default in peer-to-peer (P2P) lending and identifies social deterrence as a new underlying mechanism that explains the predictive power. Using a unique data set that combines loan data from a large P2P lending platform with 30 social media presence data from a popular social media site, borrowers’ self-disclosure of their social media account and their social media activities are shown to predict borrowers’ default probability. Leveraging a social media marketing campaign that increases the credibility of the P2P platform and lenders disclosing loan default information on borrowers’ social media accounts as a natural experiment, a 35 difference-in-difference analysis finds a significant decrease in loan default rate and increase in default repayment probability after the event, indicating that borrowers are deterred by potential social stigma. The results suggest that borrowers’ social information can be used not only for credit screening but also for default reduction and debt collection.

Citation Format(s)

Predicting and Deterring Default with Social Media Information in Peer-to-Peer Lending. / Ge, Ruyi; FENG, Juan; Gu, Bin; Zhang, Pengzhu.

In: Journal of Management Information Systems, Vol. 34, No. 2, 17.08.2017, p. 401-424.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review