利用社交媒体信息预测和阻止P2P借贷中的违约

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

Journal of Management Information Systems · 2017
被引 231
FT 50ABS 4

中文导读

研究发现借款人主动披露的社交媒体信息能预测其违约概率,且社会威慑机制可降低违约率、提高违约后还款概率,对P2P平台风控和催收有参考价值。

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 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.

P2P借贷社交媒体信用评估违约预测社会威慑