Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loans

Detalhes bibliográficos
Autor(a) principal: Susana, David Manuel Pereira
Data de Publicação: 2024
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/175555
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
id RCAP_76cd1d492385cfbd5f38c4d8aa0ef0e3
oai_identifier_str oai:run.unl.pt:10362/175555
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loansAnalytical ModelsBankingMachine LearningCredit RiskCredit ScoringLoan DefaultsSDG 3 - Good health and well-beingSDG 8 - Decent work and economic growthDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementIn the contemporary financial landscape, influenced by various global events, banks have become increasingly cautious in extending credit, both for housing and for other consumer purposes. In an increasingly uncertain economic world, banks must develop ever more robust and effective models. This topic is of significant importance to explore and update, as banks directly impact a substantial portion of the global population through housing and consumer credit. This thesis aims to contribute to the development of models with current data, and consequently, assist the population. In this research, Python programming language will be employed to utilise a Machine Learning Approach for credit scoring analysis. Methods to be utilised include: Logistic Regression; Random Forest; Gradient Boosting; XGBoost. To determine the best model, the evaluation will be performed on four metrics: Accuracy; AUC Score; Type I Error; Type II Error. The XGBoost method was the best performer on all evaluated metrics. In the course of reviewing the selected literature, previous work were found that explored this subject solely with the objective of identifying the best Machine Learning method to create the optimal model for determining customer defaults. However, several questions emerged: ‘Will machine learning models be better than Logistic Regression?’ ‘Is the model that accepts more credit the safest for the bank, considering the Profit / Risk ratio?’ This thesis aims to answer these questions and determine not only the best model for the bank, but also its profits.Bravo, Jorge Miguel VenturaRUNSusana, David Manuel Pereira2024-11-20T15:08:57Z2024-10-302024-10-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/175555TID:203776143enginfo: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:RCAAP2025-01-13T01:43:45Zoai:run.unl.pt:10362/175555Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:15:54.918256Repositórios 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 Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loans
title Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loans
spellingShingle Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loans
Susana, David Manuel Pereira
Analytical Models
Banking
Machine Learning
Credit Risk
Credit Scoring
Loan Defaults
SDG 3 - Good health and well-being
SDG 8 - Decent work and economic growth
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loans
title_full Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loans
title_fullStr Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loans
title_full_unstemmed Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loans
title_sort Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loans
author Susana, David Manuel Pereira
author_facet Susana, David Manuel Pereira
author_role author
dc.contributor.none.fl_str_mv Bravo, Jorge Miguel Ventura
RUN
dc.contributor.author.fl_str_mv Susana, David Manuel Pereira
dc.subject.por.fl_str_mv Analytical Models
Banking
Machine Learning
Credit Risk
Credit Scoring
Loan Defaults
SDG 3 - Good health and well-being
SDG 8 - Decent work and economic growth
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Analytical Models
Banking
Machine Learning
Credit Risk
Credit Scoring
Loan Defaults
SDG 3 - Good health and well-being
SDG 8 - Decent work and economic growth
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
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 2024
dc.date.none.fl_str_mv 2024-11-20T15:08:57Z
2024-10-30
2024-10-30T00:00:00Z
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/10362/175555
TID:203776143
url http://hdl.handle.net/10362/175555
identifier_str_mv TID:203776143
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
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
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
_version_ 1833597981148839936

Registros relacionados