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

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

European Journal of Operational Research, 2016, vol. 249, issue 2, 417-426

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.

Keywords: Data mining; P2P lending; Credit risk assessment; Instance-based method; Investment decisions (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (77)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:249:y:2016:i:2:p:417-426

DOI: 10.1016/j.ejor.2015.05.050

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