Modeling default rate in P2P lending via LSTM

With the fast development of peer to peer (P2P) lending, financial institutions have a substantial challenge from benefit loss due to the delinquent behaviors of the money borrowers. Therefore, having a comprehensive understanding of the changing trend of default rate in the P2P domain is crucial. In this paper, we comprehensively study the changing trend of default rate of P2P USA market at the aggregative level from August 2007 to January 2016. From the data visualization perspective, we found that three features, including delinq 2 yrs, recoveries and collection recovery fee, could potentially increase the default rate. The long short-term memory (LSTM) approach shows its great potential in modeling the P2P transaction data. Furthermore, incorporating the macroeconomic feature unemp rate can improve the LSTM performance by decreasing RMSE on both training and testing datasets. Our study can broaden the applications of LSTM approache in the P2P market. Keywords: peer to peer lending; default rate; long short-term memory.