The Role of Supervised Learning Algorithms in Fraud Detection for Financial Risk Management: A literature review

Hasna El Mekki () and Si Mohamed Bouaziz ()
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Hasna El Mekki: The Faculty of Legal, Economic and Social Sciences of Agadir, PhD Candidate of Management Sciences
Si Mohamed Bouaziz: The Faculty of Legal, Economic and Social Sciences of Agadir, Higher Education Professor

A chapter in Proceedings of the International Conference on Multidisciplinary Research in Management and Economics (ICMRME 2025), 2025, pp 7-19 from Springer

Abstract: Abstract Fraud detection is a major challenge in financial risk management, with direct implications for corporate stability and profitability. The application of supervised machine learning algorithms has significantly improved the efficiency of this process. This article examines the roles of various supervised learning methods, such as random forests (RF), support vector machines (SVMs) and artificial neural networks (ANN), on the identification and management of financial risks and the prevention of fraudulent activities. By mining a range of data and identifying complex patterns, these algorithms not only enable faster and more accurate fraud detection, but also significantly reduce financial losses. The article also discusses the challenges of integrating these models into existing systems, and highlights the potential for continuous improvement through machine learning. In conclusion, the adoption of these supervised learning algorithms for fraud detection represents an important step towards proactive and intelligent management, improving organizations’ ability to anticipate and manage risk.

Keywords: Fraud Detection; Financial Risk Management; Supervised Learning Algorithms (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-892-9_2

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DOI: 10.2991/978-94-6463-892-9_2

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