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Updated: Oct 8, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Evaluating borrowers' default risk with a spatial probit model reflecting the distance in their relational network
Jong Wook Lee1, So Young Sohn1
1Department of Information and Industrial Engineering, Yonsei University, Seoul, Republic of Korea.
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.
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