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In the peer to peer (P2P) lending platform, investors hope to maximize theirreturn while minimizing the risk through a comprehensive understanding of theP2P market. A low and stable average default rate across all the borrowersdenotes a healthy P2P market and provides investors more confidence in apromising investment. Therefore, having a powerful model to describe the trendof the default rate in the P2P market is crucial. Different from previousstudies that focus on modeling the default rate at the individual level, inthis paper, we are the first to comprehensively explore the monthly trend ofthe default rate at the aggregative level for the P2P data from October 2007 toJanuary 2016 in the US. We use the long short term memory (LSTM) approach tosequentially predict the default risk of the borrowers in Lending Club, whichis the largest P2P lending platform in the US. Although being first applied inmodeling the P2P sequential data, the LSTM approach shows its great potentialby 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 RMSEon both the training and the testing datasets. Our study can broaden theapplications of the LSTM algorithm by using it on the sequential P2P data andguide the investors in making investment strategies.

Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor