ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (12): 2708-2720.doi: 10.7544/issn1000-1239.2016.20160608

• 其他应用技术 • 上一篇    下一篇

基于风险和剩余价值的在线P2P借贷投资推荐方法

朱梦莹,郑小林,王朝晖   

  1. (浙江大学计算机科学与技术学院 杭州 310027) (mengyingzhu@zju.edu.cn)
  • 出版日期: 2016-12-01
  • 基金资助: 
    国家自然科学基金项目(61379034,U1509221);国家科技支撑计划基金项目(2014BAH28F05,2015BAH07F01)

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

摘要: 在线P2P(peer-to-pear)借贷是一种新兴的在线个人财富分配和管理系统,它允许投资人直接对借款人创建的借款标的进行竞标和投资.在P2P借贷平台中,存在一个重要的问题即如何合理分配投资人的投资金额给合适的借款人.针对该问题,提出了一种基于风险和剩余价值最大化的投资推荐框架RTSM(risk total surplus maximize).RTSM首先对借款标的进行风险评估,然后基于经济学中的剩余价值理论,使用投资人和借款人在有风险情况下的剩余价值假设,将风险评估与投资推荐结合在一起,为投资人推荐高收益低风险的投资决策.实验在风险评估和投资推荐2个阶段对美国和中国知名的P2P借贷平台(Prosper、拍拍贷)的真实数据进行分析和验证.从实验结果可以看出:RTSM可以更好地降低风险和提高投资人与借款人的整体利益.

关键词: 在线P2P借贷, 风险评估, 最大化剩余价值, 推荐系统, 投资推荐

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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