Using multi-state markov models to identify credit card risk
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Production |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132016000200330 |
Resumo: | Abstract The main interest of this work is to analyze the application of multi-state Markov models to evaluate credit card risk by investigating the characteristics of different state transitions in client-institution relationships over time, thereby generating score models for various purposes. We also used logistic regression models to compare the results with those obtained using multi-state Markov models. The models were applied to an actual database of a Brazilian financial institution. In this application, multi-state Markov models performed better than logistic regression models in predicting default risk, and logistic regression models performed better in predicting cancellation risk. |
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Production |
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Using multi-state markov models to identify credit card riskCredit scoringSurvival analysisMulti-state Markov modelsCredit cardsMarkov processesAbstract The main interest of this work is to analyze the application of multi-state Markov models to evaluate credit card risk by investigating the characteristics of different state transitions in client-institution relationships over time, thereby generating score models for various purposes. We also used logistic regression models to compare the results with those obtained using multi-state Markov models. The models were applied to an actual database of a Brazilian financial institution. In this application, multi-state Markov models performed better than logistic regression models in predicting default risk, and logistic regression models performed better in predicting cancellation risk.Associação Brasileira de Engenharia de Produção2016-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132016000200330Production v.26 n.2 2016reponame:Productioninstname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPRO10.1590/0103-6513.160814info:eu-repo/semantics/openAccessRégis,Daniel EvangelistaArtes,Rinaldoeng2016-05-31T00:00:00Zoai:scielo:S0103-65132016000200330Revistahttps://www.scielo.br/j/prod/https://old.scielo.br/oai/scielo-oai.php||production@editoracubo.com.br1980-54110103-6513opendoar:2016-05-31T00:00Production - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Using multi-state markov models to identify credit card risk |
title |
Using multi-state markov models to identify credit card risk |
spellingShingle |
Using multi-state markov models to identify credit card risk Régis,Daniel Evangelista Credit scoring Survival analysis Multi-state Markov models Credit cards Markov processes |
title_short |
Using multi-state markov models to identify credit card risk |
title_full |
Using multi-state markov models to identify credit card risk |
title_fullStr |
Using multi-state markov models to identify credit card risk |
title_full_unstemmed |
Using multi-state markov models to identify credit card risk |
title_sort |
Using multi-state markov models to identify credit card risk |
author |
Régis,Daniel Evangelista |
author_facet |
Régis,Daniel Evangelista Artes,Rinaldo |
author_role |
author |
author2 |
Artes,Rinaldo |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Régis,Daniel Evangelista Artes,Rinaldo |
dc.subject.por.fl_str_mv |
Credit scoring Survival analysis Multi-state Markov models Credit cards Markov processes |
topic |
Credit scoring Survival analysis Multi-state Markov models Credit cards Markov processes |
description |
Abstract The main interest of this work is to analyze the application of multi-state Markov models to evaluate credit card risk by investigating the characteristics of different state transitions in client-institution relationships over time, thereby generating score models for various purposes. We also used logistic regression models to compare the results with those obtained using multi-state Markov models. The models were applied to an actual database of a Brazilian financial institution. In this application, multi-state Markov models performed better than logistic regression models in predicting default risk, and logistic regression models performed better in predicting cancellation risk. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132016000200330 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132016000200330 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-6513.160814 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia de Produção |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia de Produção |
dc.source.none.fl_str_mv |
Production v.26 n.2 2016 reponame:Production instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Production |
collection |
Production |
repository.name.fl_str_mv |
Production - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
||production@editoracubo.com.br |
_version_ |
1754213154009645056 |