Credit Risk Scoring

Bibliographic Details
Main Author: Raimundo, Bernardo
Publication Date: 2024
Other Authors: Bravo, Jorge M.
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/163705
Summary: Raimundo, B., & Bravo, J. M. (2024). Credit Risk Scoring: A Stacking Generalization Approach. In Á. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & V. Colla (Eds.), Information Systems and Technologies: WorldCIST 2023, Volume 1 (Vol. 1, pp. 382-396). (Lecture Notes in Networks and Systems; Vol. 799). Springer. https://doi.org/10.1007/978-3-031-45642-8_38 --- This work has been supported by Fundação para a Ciência e a Tecnologia, grants UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC) and UIDB/00315/2020 (BRU-ISCTE-IUL)
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spelling Credit Risk ScoringA Stacking Generalization ApproachCredit scoringEnsemble learningProbability of defaultStacking generalizationRisk managementControl and Systems EngineeringSignal ProcessingComputer Networks and CommunicationsSDG 9 - Industry, Innovation, and InfrastructureRaimundo, B., & Bravo, J. M. (2024). Credit Risk Scoring: A Stacking Generalization Approach. In Á. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & V. Colla (Eds.), Information Systems and Technologies: WorldCIST 2023, Volume 1 (Vol. 1, pp. 382-396). (Lecture Notes in Networks and Systems; Vol. 799). Springer. https://doi.org/10.1007/978-3-031-45642-8_38 --- This work has been supported by Fundação para a Ciência e a Tecnologia, grants UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC) and UIDB/00315/2020 (BRU-ISCTE-IUL)Forecasting the creditworthiness of customers in new and existing loan contracts is a central issue of lenders’ activity. Credit scoring involves the use of analytical methods to transform historical loan application and loan performance data into credit scores that signal creditworthiness, inform, and determine credit decisions, determine credit limits, and loan rates, and assist in fraud detection, delinquency intervention, or loss mitigation. The standard approach to credit scoring is to pursue a “winner-take-all” perspective by which, for each dataset, a single believed to be the “best” statistical learning or machine learning classifier is selected from a set of candidate approaches using some method or criteria often neglecting model uncertainty. This paper empirically investigates the predictive accuracy of single-based classifiers against the stacking generalization approach in credit risk modelling using real-world peer-to-peer lending data. The findings show that stacking ensembles consistently outperform most traditional individual credit scoring models in predicting the default probability. Moreover, the findings show that adopting a feature selection process and hyperparameter tuning contributes to improving the performance of individual credit risk models and the super-learner scoring algorithm, helping models to be simpler, more comprehensive, and with lower classification error rates. Improving credit scoring models to better identify loan delinquency can substantially contribute to reducing loan impairments and losses leading to an improvement in the financial performance of credit institutions.SpringerNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNRaimundo, BernardoBravo, Jorge M.2025-04-24T00:31:51Z2024-02-162024-02-16T00:00:00Zbook partinfo:eu-repo/semantics/publishedVersion15application/pdfhttp://hdl.handle.net/10362/163705eng978-3-031-45641-12367-3370PURE: 83535790https://doi.org/10.1007/978-3-031-45642-8_38info: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:RCAAP2025-04-28T01:33:14Zoai:run.unl.pt:10362/163705Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:49:22.170993Repositórios 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
spellingShingle Credit Risk Scoring
Raimundo, Bernardo
Credit scoring
Ensemble learning
Probability of default
Stacking generalization
Risk management
Control and Systems Engineering
Signal Processing
Computer Networks and Communications
SDG 9 - Industry, Innovation, and Infrastructure
title_short Credit Risk Scoring
title_full Credit Risk Scoring
title_fullStr Credit Risk Scoring
title_full_unstemmed Credit Risk Scoring
title_sort Credit Risk Scoring
author Raimundo, Bernardo
author_facet Raimundo, Bernardo
Bravo, Jorge M.
author_role author
author2 Bravo, Jorge M.
author2_role author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Raimundo, Bernardo
Bravo, Jorge M.
dc.subject.por.fl_str_mv Credit scoring
Ensemble learning
Probability of default
Stacking generalization
Risk management
Control and Systems Engineering
Signal Processing
Computer Networks and Communications
SDG 9 - Industry, Innovation, and Infrastructure
topic Credit scoring
Ensemble learning
Probability of default
Stacking generalization
Risk management
Control and Systems Engineering
Signal Processing
Computer Networks and Communications
SDG 9 - Industry, Innovation, and Infrastructure
description Raimundo, B., & Bravo, J. M. (2024). Credit Risk Scoring: A Stacking Generalization Approach. In Á. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & V. Colla (Eds.), Information Systems and Technologies: WorldCIST 2023, Volume 1 (Vol. 1, pp. 382-396). (Lecture Notes in Networks and Systems; Vol. 799). Springer. https://doi.org/10.1007/978-3-031-45642-8_38 --- This work has been supported by Fundação para a Ciência e a Tecnologia, grants UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC) and UIDB/00315/2020 (BRU-ISCTE-IUL)
publishDate 2024
dc.date.none.fl_str_mv 2024-02-16
2024-02-16T00:00:00Z
2025-04-24T00:31:51Z
dc.type.driver.fl_str_mv book part
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/163705
url http://hdl.handle.net/10362/163705
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-031-45641-1
2367-3370
PURE: 83535790
https://doi.org/10.1007/978-3-031-45642-8_38
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 15
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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
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