DeepCredit: Exploiting User Cickstream for Loan Risk Prediction in P2P Lending

Peer-to-peer (P2P) lending or crowdlending,is a recent innovation allows a group of individual or institutional lenders tolend funds to individuals or businesses in return for interest payment on top of capital repayments.The rapid growth of P2P lending marketplaces has heightenedthe need to develop a support system to help lenders make sound lending decisions.But realizing such system is challenging in the absence of formal credit data used by the banking sector.In this paper, we attempt toexplore the possible connections between user credit riskand how users behave in the lendingsites.Wepresent the first analysis of user detailed clickstream datafrom a large P2P lending provider.Our analysisreveals that the users' sequences of repayment histories and financial activities in the lending site,have significant predictive value for their future loan repayments.In the light of this, we propose a deep architecture named DeepCredit, toautomatically acquire theknowledge of credit risk from the sequences of activities that users conduct on the site.Experiments on our large-scale real-world datasetshow that our model generates a high accuracy in predicting both loan delinquency and default, and significantly outperformsa numberof baselines and competitive alternatives.