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

Jong Wook Lee,So Young Sohn,Elisa Ughetto

doi:10.1371/journal.pone.0261737

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.

Highlights

IntroductionTo predict the probability of default of loan applicant that is essential for credit risk management, machine learning models use two types of borrower information: standard “hard” information and nonstandard “soft” information [1]
Credit risk management is very important for service firms in the lending business
While both hard and soft information has been used in most credit scoring models, what is missing is the potential relation among loan applicants

Summary

Introduction

To predict the probability of default of loan applicant that is essential for credit risk management, machine learning models use two types of borrower information: standard “hard” information and nonstandard “soft” information [1]. The former directly reflects the loan applicants’ financial status or creditworthiness, while the latter includes those that do not have a direct relationship to the credit applicant’s financial status or creditworthiness such as age or residence. Relationship among loan applicants that are at high risk of default can provide valuable information for evaluating default risk [6,7,8]

Methods

Results

Conclusion