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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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    Cited by:

    1. Pankaj C. Patel, 2024. "Requisite variety theory and third‐party guarantee loans: How local bank branches can reduce loan defaults," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(1), pages 226-246, January.

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