ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (12): 2708-2720.doi: 10.7544/issn1000-1239.2016.20160608

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Investment Recommendation Based on Risk and Surplus in P2P Lending

Zhu Mengying, Zheng Xiaolin,Wang Chaohui   

  1. (College of Computer Science & Technology, Zhejiang University, Hangzhou 310027)
  • Online:2016-12-01

Abstract: Online peer-to-peer (P2P) lending, which is a newly personal wealth distribution and management system, has become a new type of financing mode for Internet users. P2P lending platform allows borrowers to create borrow listing and investors to bid and invest borrowers’ listing directly. In the P2P lending, there is a significant issue that is how to reasonably match borrowers and investors and then allocate the amount of investors, so as to recommend low risk and high rate investment decisions to the investors. This paper proposes a recommendation framework risk based total surplus risk total surplus maximize (RTSM), which can solve the problem of allocating the investment amount into borrowers’ listings. Specifically, we first propose to adapt various methods of regression to evaluate default risk. Then, we give the hypothesis the surplus of investors and borrowers under default risk which is based on the theory of surplus in economics. And based on this hypothesis, we combine the risk assessment and investment recommendation to maximize the total surplus under default risk. We apply the recommendation framework RTSM into two real-world datasets (Prosper and PPDai). Finally, experiments and analysis indicate that RTSM can reduce risk and improve the overall benefits of both investors and borrowers.

Key words: online P2P lending, risk assessment, total surplus maximize, recommendation system, investment recommendation

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