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 the default rate in the P2P domain is crucial. In this paper, we comprehensively study the changing trend of the 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_2yrs, 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 \textit{  can improve the LSTM performance by decreasing RMSE on both training and testing datasets. Our study can broaden the applications of LSTM approach in the P2P market .
In the peer to peer (P2P) lending platform, investors hope to maximize their return while minimizing the risk through a comprehensive understanding of the P2P market. A low and stable average default rate across all the borrowers denotes a healthy P2P market and provides investors more confidence in a promising investment . Therefore, having a powerful model to describe the trend of the default rate in the P2P market is crucial. Different from previous studies that focus on modeling the default rate at the individual level, in this paper, we are the first to comprehensively explore the monthly trend of the default rate at the aggregative level for the P2P data from October 2007 to January 2016 in the US. We use the long short term memory (LSTM) approach to sequentially predict the default risk of the borrowers in Lending Club, which is the largest P2P lending platform in the US. Although being first applied in modeling the P2P sequential data, the LSTM approach shows its great potential by outperforming traditionally utilized time series models in our experiments . Furthermore, incorporating the macroeconomic feature \textit{unemp\_rate (i.e., unemployment rate) can improve the LSTM performance by decreasing RMSE on both the training and the testing datasets. Our study can broaden the applications of the LSTM algorithm by using it on the sequential P2P data and guide the investors in making investment strategies .
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