Analyses of default predicted models for a single family loan

Detalhes bibliográficos
Autor(a) principal: Preto, Ana Figueiredo Costa
Data de Publicação: 2016
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/18615
Resumo: This study aims to explore the possibility of a financial entity to produce a predicted model of default. The study aims to compares the performance of an existing model, the FICO and an alternative model, based on cluster analysis method with dataset available. A third option is presented for the analyses of default, which it is the junction of both models. This third method can be implemented in two different ways: the two models agreeing with acceptance of the loan or the two models approving the rejection of the loan.
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spelling Analyses of default predicted models for a single family loanCredit ScoringClustering AnalysesMortgage defaultBehaviourStatistical ModelDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis study aims to explore the possibility of a financial entity to produce a predicted model of default. The study aims to compares the performance of an existing model, the FICO and an alternative model, based on cluster analysis method with dataset available. A third option is presented for the analyses of default, which it is the junction of both models. This third method can be implemented in two different ways: the two models agreeing with acceptance of the loan or the two models approving the rejection of the loan.Rocha, GonçaloRUNPreto, Ana Figueiredo Costa2016-08-04T09:27:01Z2016-062016-062016-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/18615TID:201525976enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T03:57:58Zoai:run.unl.pt:10362/18615Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:24:52.706936Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Analyses of default predicted models for a single family loan
title Analyses of default predicted models for a single family loan
spellingShingle Analyses of default predicted models for a single family loan
Preto, Ana Figueiredo Costa
Credit Scoring
Clustering Analyses
Mortgage default
Behaviour
Statistical Model
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Analyses of default predicted models for a single family loan
title_full Analyses of default predicted models for a single family loan
title_fullStr Analyses of default predicted models for a single family loan
title_full_unstemmed Analyses of default predicted models for a single family loan
title_sort Analyses of default predicted models for a single family loan
author Preto, Ana Figueiredo Costa
author_facet Preto, Ana Figueiredo Costa
author_role author
dc.contributor.none.fl_str_mv Rocha, Gonçalo
RUN
dc.contributor.author.fl_str_mv Preto, Ana Figueiredo Costa
dc.subject.por.fl_str_mv Credit Scoring
Clustering Analyses
Mortgage default
Behaviour
Statistical Model
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Credit Scoring
Clustering Analyses
Mortgage default
Behaviour
Statistical Model
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description This study aims to explore the possibility of a financial entity to produce a predicted model of default. The study aims to compares the performance of an existing model, the FICO and an alternative model, based on cluster analysis method with dataset available. A third option is presented for the analyses of default, which it is the junction of both models. This third method can be implemented in two different ways: the two models agreeing with acceptance of the loan or the two models approving the rejection of the loan.
publishDate 2016
dc.date.none.fl_str_mv 2016-08-04T09:27:01Z
2016-06
2016-06
2016-06-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/18615
TID:201525976
url http://hdl.handle.net/10362/18615
identifier_str_mv TID:201525976
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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