Credit Risk Scoring: A Stacking Generalization Approach

Bibliographic Details
Main Author: Raimundo, Bernardo Dias
Publication Date: 2023
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/149734
Summary: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
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spelling Credit Risk Scoring: A Stacking Generalization ApproachCredit scoringEnsemble learningProbability of defaultStacking generalizationRisk managementDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementCredit risk regulation has been receiving tremendous attention, as a result of the effects of the latest global financial crisis. According to the developments made in the Internal Rating Based approach, under the Basel guidelines, banks are allowed to use internal risk measures as key drivers to assess the possibility to grant a loan to an applicant. Credit scoring is a statistical approach used for evaluating potential loan applications in both financial and banking institutions. When applying for a loan, an applicant must fill out an application form detailing its characteristics (e.g., income, marital status, and loan purpose) that will serve as contributions to a credit scoring model which produces a score that is used to determine whether a loan should be granted or not. This enables faster and consistent credit approvals and the reduction of bad debt. Currently, many machine learning and statistical approaches such as logistic regression and tree-based algorithms have been used individually for credit scoring models. Newer contemporary machine learning techniques can outperform classic methods by simply combining models. This dissertation intends to be an empirical study on a publicly available bank loan dataset to study banking loan default, using ensemble-based techniques to increase model robustness and predictive power. The proposed ensemble method is based on stacking generalization an extension of various preceding studies that used different techniques to further enhance the model predictive capabilities. The results show that combining different models provides a great deal of flexibility to credit scoring models.Bravo, Jorge Miguel VenturaRUNRaimundo, Bernardo Dias2023-02-27T14:17:50Z2023-01-232023-01-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/149734TID:203237471enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:09:30Zoai:run.unl.pt:10362/149734Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:39:54.656602Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Credit Risk Scoring: A Stacking Generalization Approach
title Credit Risk Scoring: A Stacking Generalization Approach
spellingShingle Credit Risk Scoring: A Stacking Generalization Approach
Raimundo, Bernardo Dias
Credit scoring
Ensemble learning
Probability of default
Stacking generalization
Risk management
title_short Credit Risk Scoring: A Stacking Generalization Approach
title_full Credit Risk Scoring: A Stacking Generalization Approach
title_fullStr Credit Risk Scoring: A Stacking Generalization Approach
title_full_unstemmed Credit Risk Scoring: A Stacking Generalization Approach
title_sort Credit Risk Scoring: A Stacking Generalization Approach
author Raimundo, Bernardo Dias
author_facet Raimundo, Bernardo Dias
author_role author
dc.contributor.none.fl_str_mv Bravo, Jorge Miguel Ventura
RUN
dc.contributor.author.fl_str_mv Raimundo, Bernardo Dias
dc.subject.por.fl_str_mv Credit scoring
Ensemble learning
Probability of default
Stacking generalization
Risk management
topic Credit scoring
Ensemble learning
Probability of default
Stacking generalization
Risk management
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
publishDate 2023
dc.date.none.fl_str_mv 2023-02-27T14:17:50Z
2023-01-23
2023-01-23T00:00:00Z
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/10362/149734
TID:203237471
url http://hdl.handle.net/10362/149734
identifier_str_mv TID:203237471
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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