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Instance-based credit risk assessment for investment decisions in P2P lending

Author

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  • Guo, Yanhong
  • Zhou, Wenjun
  • Luo, Chunyu
  • Liu, Chuanren
  • Xiong, Hui

Abstract

Recent years have witnessed increased attention on peer-to-peer (P2P) lending, which provides an alternative way of financing without the involvement of traditional financial institutions. A key challenge for personal investors in P2P lending marketplaces is the effective allocation of their money across different loans by accurately assessing the credit risk of each loan. Traditional rating-based assessment models cannot meet the needs of individual investors in P2P lending, since they do not provide an explicit mechanism for asset allocation. In this study, we propose a data-driven investment decision-making framework for this emerging market. We designed an instance-based credit risk assessment model, which has the ability of evaluating the return and risk of each individual loan. Moreover, we formulated the investment decision in P2P lending as a portfolio optimization problem with boundary constraints. To validate the proposed model, we performed extensive experiments on real-world datasets from two notable P2P lending marketplaces. Experimental results revealed that the proposed model can effectively improve investment performances compared with existing methods in P2P lending.

Suggested Citation

  • Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
  • Handle: RePEc:eee:ejores:v:249:y:2016:i:2:p:417-426
    DOI: 10.1016/j.ejor.2015.05.050
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