Credit risk modeling - predicting customer loan defaults with machine learning models
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/140582 |
Resumo: | The assessment of financial credit risk constitutes an important, yet challenging research topic across multiple disciplines. This paper evaluates the risk of customers not being able to repay their obligation on time by utilizing a variety of both parametric and non-parametric (supervised) machine learning models. These methods include Decision Tree, Random Forest, Ada Boost, XG Boost, and Support Vector Machine. In addition, as a benchmark classifier, the traditional credit-risk method, Logistic Regression, was used to perform a comparison. Random Forest and XG Boost outperformed the other methods constantly, provided that thorough data analysis, pre-processing, and model-training are performed. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Credit risk modeling - predicting customer loan defaults with machine learning modelsCredit risk predictionCredit defaultCredit scoringSupervised machine learningBinary classificationModel validationGraphical user interfaceDomínio/Área Científica::Ciências Sociais::Economia e GestãoThe assessment of financial credit risk constitutes an important, yet challenging research topic across multiple disciplines. This paper evaluates the risk of customers not being able to repay their obligation on time by utilizing a variety of both parametric and non-parametric (supervised) machine learning models. These methods include Decision Tree, Random Forest, Ada Boost, XG Boost, and Support Vector Machine. In addition, as a benchmark classifier, the traditional credit-risk method, Logistic Regression, was used to perform a comparison. Random Forest and XG Boost outperformed the other methods constantly, provided that thorough data analysis, pre-processing, and model-training are performed.Pereira, João PedroRUNDornigg, Thomas2022-06-23T14:09:38Z2022-01-122021-12-152022-01-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/140582TID:202973271enginfo: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:RCAAP2024-03-11T05:17:45Zoai:run.unl.pt:10362/140582Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:49:43.707777Repositório 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 |
Credit risk modeling - predicting customer loan defaults with machine learning models |
title |
Credit risk modeling - predicting customer loan defaults with machine learning models |
spellingShingle |
Credit risk modeling - predicting customer loan defaults with machine learning models Dornigg, Thomas Credit risk prediction Credit default Credit scoring Supervised machine learning Binary classification Model validation Graphical user interface Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
Credit risk modeling - predicting customer loan defaults with machine learning models |
title_full |
Credit risk modeling - predicting customer loan defaults with machine learning models |
title_fullStr |
Credit risk modeling - predicting customer loan defaults with machine learning models |
title_full_unstemmed |
Credit risk modeling - predicting customer loan defaults with machine learning models |
title_sort |
Credit risk modeling - predicting customer loan defaults with machine learning models |
author |
Dornigg, Thomas |
author_facet |
Dornigg, Thomas |
author_role |
author |
dc.contributor.none.fl_str_mv |
Pereira, João Pedro RUN |
dc.contributor.author.fl_str_mv |
Dornigg, Thomas |
dc.subject.por.fl_str_mv |
Credit risk prediction Credit default Credit scoring Supervised machine learning Binary classification Model validation Graphical user interface Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
Credit risk prediction Credit default Credit scoring Supervised machine learning Binary classification Model validation Graphical user interface Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
The assessment of financial credit risk constitutes an important, yet challenging research topic across multiple disciplines. This paper evaluates the risk of customers not being able to repay their obligation on time by utilizing a variety of both parametric and non-parametric (supervised) machine learning models. These methods include Decision Tree, Random Forest, Ada Boost, XG Boost, and Support Vector Machine. In addition, as a benchmark classifier, the traditional credit-risk method, Logistic Regression, was used to perform a comparison. Random Forest and XG Boost outperformed the other methods constantly, provided that thorough data analysis, pre-processing, and model-training are performed. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-15 2022-06-23T14:09:38Z 2022-01-12 2022-01-12T00:00:00Z |
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/10362/140582 TID:202973271 |
url |
http://hdl.handle.net/10362/140582 |
identifier_str_mv |
TID:202973271 |
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 |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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1799138095186051072 |