Keywords:
Machine learning
Deep learning
Fraud detection
Credit scoring
Explainable AI (XAI)
Stress testing

  • Abstract

    This article reviews the evolution of financial risk management by comparing established statistical models with emerging artificial intelligence (AI) tools. Traditional methods, such as Modern Portfolio Theory and Value at Risk (VaR), remain essential because they are transparent and widely accepted by regulators. However, they struggle with today’s complexity, especially nonlinear shocks and rare events. AI techniques such as machine learning, deep learning, and natural language processing bring speed and flexibility, enabling the handling of large volumes of diverse data in real-time. To capture recent developments, we screened over 600 studies and analysed 81 published between 2010 and 2024. Results show that AI is being applied to credit scoring, fraud detection, market risk forecasting, and systemic risk modelling. At the same time, concerns about fairness, interpretability, and compliance remain unresolved. By mapping these opportunities and limitations, the review reveals where AI can enhance existing practices and where hybrid models may offer the most practical path forward for risk professionals.

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Jouihri, M., Ayouche, S., & Ayouche, A. (2026). When does AI improve financial risk decisions? A SLR and comparative synthesis . Multidisciplinary Reviews, (| Accepted Articles). Retrieved from https://malque.pub/ojs/index.php/mr/article/view/16574
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