Journal article
Instance-based credit risk assessment for investment decisions in P2P lending
European journal of operational research, v 249(2), pp 417-426
01 Mar 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
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. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
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Details
- Title
- Instance-based credit risk assessment for investment decisions in P2P lending
- Creators
- Yanhong Guo - Dalian University of TechnologyWenjun Zhou - University of Tennessee at KnoxvilleChunyu Luo - Dalian University of TechnologyChuanren Liu - Drexel UniversityHui Xiong - Rutgers, The State University of New JerseyYixin Guo - Mathematics
- Publication Details
- European journal of operational research, v 249(2), pp 417-426
- Publisher
- Elsevier
- Number of pages
- 10
- Grant note
- DUT15RW116 / Fundamental Research Funds for the Central Universities 14YJCZH044 / Humanities and Social Science Program of the Ministry of Education in China 71028002; 71372083; 71402014 / Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000366951100004
- Scopus ID
- 2-s2.0-84952978996
- Other Identifier
- 991019173449504721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Management
- Operations Research & Management Science