The peer-to-peer lending industry has experienced recent turmoil,posing risks to fintech companies and banks.Based on a random sample of 33,669 borrowers who had downloaded peer-to-peer lending platforms prior to subm...The peer-to-peer lending industry has experienced recent turmoil,posing risks to fintech companies and banks.Based on a random sample of 33,669 borrowers who had downloaded peer-to-peer lending platforms prior to submitting loan applications to a wellknown fintech company,Du Xiaoman Financial(formerly Baidu Finance),this article evaluates the predictive power of borrowers’internet behaviours on credit default risk.After controlling for borrowers’basic characteristics that are widely used in academic research and enterprise practices,the coefficients of key factors selected from 3,100 variables are economically and statistically significant.The average Kolmogorov-Smirnov value of the prediction model calculated using the hold-out method is approximately 37.09%.The results remain robust in several additional analyses.This study indicates the importance of non-credit information,particularly borrowers’internet behaviours,in supplementing borrowers’credit records for both fintech companies and banks.展开更多
基金The study is supported by the National Natural Science Foundation(China)[Nos.71631004(Key Project)and 71871216]the Social Science Foundation of Beijing[No.17GLB022]the Fundamental Research Funds for the Central Universities,and the Research Funds of Renmin University of China[No.16XNB025].
文摘The peer-to-peer lending industry has experienced recent turmoil,posing risks to fintech companies and banks.Based on a random sample of 33,669 borrowers who had downloaded peer-to-peer lending platforms prior to submitting loan applications to a wellknown fintech company,Du Xiaoman Financial(formerly Baidu Finance),this article evaluates the predictive power of borrowers’internet behaviours on credit default risk.After controlling for borrowers’basic characteristics that are widely used in academic research and enterprise practices,the coefficients of key factors selected from 3,100 variables are economically and statistically significant.The average Kolmogorov-Smirnov value of the prediction model calculated using the hold-out method is approximately 37.09%.The results remain robust in several additional analyses.This study indicates the importance of non-credit information,particularly borrowers’internet behaviours,in supplementing borrowers’credit records for both fintech companies and banks.
