Using multi-state markov models to identify credit card risk

Detalhes bibliográficos
Autor(a) principal: Régis,Daniel Evangelista
Data de Publicação: 2016
Outros Autores: Artes,Rinaldo
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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spelling 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

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