Journal of Management Information Systems

Volume 34 Number 2 2017 pp. 401-424

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

Ge, Ruyi, Feng, Juan, Gu, Bin, and Zhang, Pengzhu


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 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 difference-in-differences 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.

Key words and phrases: default probability, difference-in-differences, fintech, peer-to-peer lending, online P2P lending, lending industry, propensity score matching, online self-disclosure, social media, soft information, Weibo