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Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network
- Source :
- PLoS ONE, Vol 16, Iss 12 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
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.
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.3744ea59e7ae4f08a119b0e746c448fe
- Document Type :
- article