Author(s):
Heß, Valentin Lennart ; Damásio, Bruno
Date: 2025
Persistent ID: http://hdl.handle.net/10362/178769
Origin: Repositório Institucional da UNL
Project/scholarship:
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT;
Subject(s): 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
The 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.
