【学术报告】 Predicting and Deterring Default with Social Media Information in P2P Lending
【报告人】 Prof. Juan Feng, College of Business at the City University of Hong Kong
【摘要】 This study examines the predictive power of self-disclosed social media information on borrowers' default in P2P lending, and identifies social deterrence as a new underlying mechanism that explain the predictive power. Using a unique dataset 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 increase the credibility of the P2P platform and lenders disclosing loan default information on borrowers’ social media accounts as a natural experiment, a 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.