Machine learning in banking risk management

Bibliographic Details
Main Author: Heß, Valentin Lennart
Publication Date: 2025
Other Authors: Damásio, Bruno
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/178769
Summary: Heß, V. L., & Damásio, B. (2025). Machine learning in banking risk management: Mapping a decade of evolution. International Journal of Information Management Data Insights, 5(1), 1-17. Article 100324. https://doi.org/10.1016/j.jjimei.2025.100324 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020
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spelling Machine learning in banking risk managementMapping a decade of evolutionAlgorithmArtificial intelligenceBankMachine learningRisk managementManagement Information SystemsInformation SystemsIndustrial and Manufacturing EngineeringLibrary and Information SciencesInformation Systems and ManagementArtificial IntelligenceSDG 8 - Decent Work and Economic GrowthSDG 9 - Industry, Innovation, and InfrastructureHeß, V. L., & Damásio, B. (2025). Machine learning in banking risk management: Mapping a decade of evolution. International Journal of Information Management Data Insights, 5(1), 1-17. Article 100324. https://doi.org/10.1016/j.jjimei.2025.100324 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020The techniques used in banks' risk management are evolving as opposed to the process of risk management. It is necessary to respond to these market- and technology-driven changes appropriately. Innovative approaches are needed to overcome the limitations of traditional methods. Machine learning (ML) algorithms are suitable for dealing with the various risk types banks face. Academic literature focuses on applying ML in credit risk management. This article addresses market, operational, liquidity, and other risk types, with the objective to examine how ML algorithms predict, assess, and mitigate these risks and identify both their advantages and challenges. This article systematically reviews 46 recent studies and highlights the expanding role of ML in enhancing risk management strategies. The article has revealed that ML is adequately covered in the context of market and operational risk. The learning ability and predictive capabilities of artificial neural networks and other algorithms are promising for risk management. Our findings offer a concise overview of current ML applications for multiple risk types in banking, identifying research gaps, highlighting opportunities and challenges and providing actionable directions for further studies. By providing a focused overview of the expanding role of ML in banking risk management, we underscore the potential to strengthen the robustness of banks’ strategies and practices.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNHeß, Valentin LennartDamásio, Bruno2025-02-10T21:18:27Z2025-062025-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttp://hdl.handle.net/10362/178769eng2667-0968PURE: 108623819https://doi.org/10.1016/j.jjimei.2025.100324info: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:RCAAP2025-02-24T01:41:47Zoai:run.unl.pt:10362/178769Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T20:39:31.234007Repositórios 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 Machine learning in banking risk management
Mapping a decade of evolution
title Machine learning in banking risk management
spellingShingle Machine learning in banking risk management
Heß, Valentin Lennart
Algorithm
Artificial intelligence
Bank
Machine learning
Risk management
Management Information Systems
Information Systems
Industrial and Manufacturing Engineering
Library and Information Sciences
Information Systems and Management
Artificial Intelligence
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
title_short Machine learning in banking risk management
title_full Machine learning in banking risk management
title_fullStr Machine learning in banking risk management
title_full_unstemmed Machine learning in banking risk management
title_sort Machine learning in banking risk management
author Heß, Valentin Lennart
author_facet Heß, Valentin Lennart
Damásio, Bruno
author_role author
author2 Damásio, Bruno
author2_role author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Heß, Valentin Lennart
Damásio, Bruno
dc.subject.por.fl_str_mv Algorithm
Artificial intelligence
Bank
Machine learning
Risk management
Management Information Systems
Information Systems
Industrial and Manufacturing Engineering
Library and Information Sciences
Information Systems and Management
Artificial Intelligence
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
topic Algorithm
Artificial intelligence
Bank
Machine learning
Risk management
Management Information Systems
Information Systems
Industrial and Manufacturing Engineering
Library and Information Sciences
Information Systems and Management
Artificial Intelligence
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
description Heß, V. L., & Damásio, B. (2025). Machine learning in banking risk management: Mapping a decade of evolution. International Journal of Information Management Data Insights, 5(1), 1-17. Article 100324. https://doi.org/10.1016/j.jjimei.2025.100324 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020
publishDate 2025
dc.date.none.fl_str_mv 2025-02-10T21:18:27Z
2025-06
2025-06-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/178769
url http://hdl.handle.net/10362/178769
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2667-0968
PURE: 108623819
https://doi.org/10.1016/j.jjimei.2025.100324
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 17
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
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