Three-stage ensemble model : reinforce predictive capacity without compromising interpretability

Detalhes bibliográficos
Autor(a) principal: Silvestre, Martinho de Matos
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/71588
Resumo: Thesis proposal presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with specialization in Risk Analysis and Management
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spelling Three-stage ensemble model : reinforce predictive capacity without compromising interpretabilityEnsemble ModelingProbability of DefaultCredit ScoringScorecardLogistic RegressionDecision TreeArtificial Neural NetworkMultilayer PerceptronRandom ForestMachine LearningThesis proposal presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with specialization in Risk Analysis and ManagementOver the last decade, several banks have developed models to quantify credit risk. In addition to the monitoring of the credit portfolio, these models also help deciding the acceptance of new contracts, assess customers profitability and define pricing strategy. The objective of this paper is to improve the approach in credit risk modeling, namely in scoring models to predict default events. To this end, we propose the development of a three-stage ensemble model that combines the results interpretability of the Scorecard with the predictive power of machine learning algorithms. The results show that ROC index improves 0.5%-0.7% and Accuracy 0%-1% considering the Scorecard as baseline.Henriques, Roberto André PereiraRUNSilvestre, Martinho de Matos2019-06-03T16:10:23Z2019-04-032019-04-03T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/71588TID:202250768enginfo: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:RCAAP2024-03-11T04:33:40Zoai:run.unl.pt:10362/71588Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:35:12.903637Repositório 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 Three-stage ensemble model : reinforce predictive capacity without compromising interpretability
title Three-stage ensemble model : reinforce predictive capacity without compromising interpretability
spellingShingle Three-stage ensemble model : reinforce predictive capacity without compromising interpretability
Silvestre, Martinho de Matos
Ensemble Modeling
Probability of Default
Credit Scoring
Scorecard
Logistic Regression
Decision Tree
Artificial Neural Network
Multilayer Perceptron
Random Forest
Machine Learning
title_short Three-stage ensemble model : reinforce predictive capacity without compromising interpretability
title_full Three-stage ensemble model : reinforce predictive capacity without compromising interpretability
title_fullStr Three-stage ensemble model : reinforce predictive capacity without compromising interpretability
title_full_unstemmed Three-stage ensemble model : reinforce predictive capacity without compromising interpretability
title_sort Three-stage ensemble model : reinforce predictive capacity without compromising interpretability
author Silvestre, Martinho de Matos
author_facet Silvestre, Martinho de Matos
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Silvestre, Martinho de Matos
dc.subject.por.fl_str_mv Ensemble Modeling
Probability of Default
Credit Scoring
Scorecard
Logistic Regression
Decision Tree
Artificial Neural Network
Multilayer Perceptron
Random Forest
Machine Learning
topic Ensemble Modeling
Probability of Default
Credit Scoring
Scorecard
Logistic Regression
Decision Tree
Artificial Neural Network
Multilayer Perceptron
Random Forest
Machine Learning
description Thesis proposal presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with specialization in Risk Analysis and Management
publishDate 2019
dc.date.none.fl_str_mv 2019-06-03T16:10:23Z
2019-04-03
2019-04-03T00:00:00Z
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/10362/71588
TID:202250768
url http://hdl.handle.net/10362/71588
identifier_str_mv TID:202250768
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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