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

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

Research output: Contribution to journalArticlepeer-review

149 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)417-426
Number of pages10
JournalEuropean Journal of Operational Research
Volume249
Issue number2
DOIs
StatePublished - Mar 1 2016

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Keywords

  • Credit risk assessment
  • Data mining
  • Instance-based method
  • Investment decisions
  • P2P lending

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