Understanding Default Behavior in Online Lending

Microcredit, very small loans given out without any collaterals, is a new form of financial instrument that serves the segment of population that are typically underserved by traditional financial services. When microcredit takes the form of lending over the internet, it has the advantage of easy online application process and fast funding for borrowers, as well as attractive rate of return for individual lenders. For platforms that facilitate such activities, the key challenge lies in risk management, i.e. adequately pricing each loan's risk so as to balance borrowers' lending cost and lenders' risk-adjusted return. In fact, identifying default borrowers is of critical importance for the ecosystem. Traditionally, credit risk depends heavily on borrowers' historical loan records. However, most borrowers do not have any bureau history, and therefore cannot provide sufficient loan records. In this paper, we study default prediction in online lending by using social behavior. Specifically, we based our work on a dataset provided by PPDai, one of the leading platforms in China. Our dataset consists of over 11 million users and more than 1.5 billion call logs between them. We establish a mobile network and explore social factors that predict borrowers' default. Based on this, we focused on cheating agents, who recruit and teach borrowers to cheat by providing false information and faking application materials. Cheating agents represent a type of default, especially detrimental to the system. We propose a novel probabilistic framework to identify default borrowers and cheating agents simultaneously. Experimental results on production dataset demonstrate significant improvement over several baseline methods. Moreover, our model can effectively identify cheating agents without any labels.