Open AccessEvaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network
Author(s) -
Jong Wook Lee,
So Young Sohn
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0261737
Subject(s) - loan , default , probit model , econometrics , actuarial science , credit risk , spatial analysis , probability of default , probit , predictive power , autocorrelation , business , computer science , statistics , economics , finance , mathematics , philosophy , epistemology
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.