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

In: Proceedings of the International Conference on Multidisciplinary Research in Management and Economics (ICMRME 2025)

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hasna El Mekki & Si Mohamed Bouaziz, 2025. "The Role of Supervised Learning Algorithms in Fraud Detection for Financial Risk Management: A literature review," Advances in Economics, Business and Management Research, in: Ait Oufkir Abdellah & Younes Ben Zaied & Mohamed Charif El Harrane & Lalla Touhfa Belgnaoui (ed.), Proceedings of the International Conference on Multidisciplinary Research in Management and Economics (ICMRME 2025), pages 7-19, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-892-9_2
    DOI: 10.2991/978-94-6463-892-9_2
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