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Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network
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 d...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719753/ https://www.ncbi.nlm.nih.gov/pubmed/34972129 http://dx.doi.org/10.1371/journal.pone.0261737 |
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author | Lee, Jong Wook Sohn, So Young |
author_facet | Lee, Jong Wook Sohn, So Young |
author_sort | Lee, Jong Wook |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8719753 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-87197532022-01-01 Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network Lee, Jong Wook Sohn, So Young PLoS One Research Article 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. Public Library of Science 2021-12-31 /pmc/articles/PMC8719753/ /pubmed/34972129 http://dx.doi.org/10.1371/journal.pone.0261737 Text en © 2021 Lee, Sohn https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Lee, Jong Wook Sohn, So Young Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network |
title | Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network |
title_full | Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network |
title_fullStr | Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network |
title_full_unstemmed | Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network |
title_short | Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network |
title_sort | evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719753/ https://www.ncbi.nlm.nih.gov/pubmed/34972129 http://dx.doi.org/10.1371/journal.pone.0261737 |
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