This paper explores how borrowers’ financial and personal information, loan characteristics and lending models affect peer-to-peer (P2P) loan funding outcomes. Using a large sample of listings from one of the largest Chinese online P2P lending platforms, we find that those borrowers earning a higher income or who own a car are more likely to receive a loan, pay lower interest rates, and are less likely to default. The credit grade assigned by the lending platform may not represent the creditworthiness of potential borrowers. We also find that the unique offline process in the Chinese P2P online lending platform exerts significant influence on the lending decision. We discuss the implications of our results for the design of big data-based lending markets.
Bibliographical noteWe thank the Editors, Ram Ramesh and Raghav Rao, the guest editors, Douglas Cumming, Sofia Johan, and Denis Schweizer for their helpful and valuable suggestions. We are grateful for useful comments from the participants of the 2nd microfinance and rural finance conference in Aberystwyth. Qizhi Tao acknowledges support from the Fundamental Research Funds for the Central Universities (Grant No. JBK160921).
- peer-to-peer (P2P) lending
- offline authentication
- listing outcomes
- information asymmetry