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Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network

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  • Jong Wook Lee
  • So Young Sohn

Abstract

Potential relationship among loan applicants can provide valuable information for evaluating default risk. However, most of the existing credit scoring models either ignore this relationship or consider a simple connection information. This study assesses the applicants’ relation in terms of their distance estimated based on their characteristics. This information is then utilized in a proposed spatial probit model to reflect the different degree of borrowers’ relation on the default prediction of loan applicant. We apply this method to peer-to-peer Lending Club Loan data. Empirical results show that the consideration of information on the spatial autocorrelation among loan applicants can provide high predictive power for defaults.

Suggested Citation

  • Jong Wook Lee & So Young Sohn, 2021. "Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-11, December.
    • Handle: RePEc:plo:pone00:0261737
    DOI: 10.1371/journal.pone.0261737
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    References listed on IDEAS

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    1. Yanhao Wei & Pinar Yildirim & Christophe Van den Bulte & Chrysanthos Dellarocas, 2016. "Credit Scoring with Social Network Data," Marketing Science, INFORMS, vol. 35(2), pages 234-258, March.
    2. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2015. "Support vector regression for loss given default modelling," European Journal of Operational Research, Elsevier, vol. 240(2), pages 528-538.
    3. Yonghan Ju & So Young Sohn, 2015. "Stress test for a technology credit guarantee fund based on survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 463-475, March.
    4. H J Jeon & S Y Sohn, 2008. "The risk management for technology credit guarantee fund," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(12), pages 1624-1632, December.
    5. Anping Chen & Marlon Boarnet & Mark Partridge & Raffaella Calabrese & Johan A. Elkink, 2014. "Estimators Of Binary Spatial Autoregressive Models: A Monte Carlo Study," Journal of Regional Science, Wiley Blackwell, vol. 54(4), pages 664-687, September.
    6. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
    7. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    8. Xuchen Lin & Xiaolong Li & Zhong Zheng, 2017. "Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China," Applied Economics, Taylor & Francis Journals, vol. 49(35), pages 3538-3545, July.
    9. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    10. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    11. Raffaella Calabrese & Johan A. Elkink & Paolo S. Giudici, 2017. "Measuring bank contagion in Europe using binary spatial regression models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(12), pages 1503-1511, December.
    12. Dorfleitner, Gregor & Priberny, Christopher & Schuster, Stephanie & Stoiber, Johannes & Weber, Martina & de Castro, Ivan & Kammler, Julia, 2016. "Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 169-187.
    13. Angilella, Silvia & Mazzù, Sebastiano, 2015. "The financing of innovative SMEs: A multicriteria credit rating model," European Journal of Operational Research, Elsevier, vol. 244(2), pages 540-554.
    14. S Y Sohn & M K Doo & Y H Ju, 2012. "Pattern recognition for evaluator errors in a credit scoring model for technology-based SMEs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(8), pages 1051-1064, August.
    15. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    16. Y Kim & S Y Sohn, 2007. "Technology scoring model considering rejected applicants and effect of reject inference," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1341-1347, October.
    17. Xiangxiang Zeng & Li Liu & Stephen Leung & Jiangze Du & Xun Wang & Tao Li, 2017. "A decision support model for investment on P2P lending platform," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-18, September.
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