Artificial Intelligence in Financial Risk Management: A Systematic Review
Abstract
Artificial Intelligence (AI) has transformed the landscape of financial risk management by enabling predictive analytics, real-time monitoring, and intelligent decision-making across banking, insurance, and investment sectors. This review paper critically examines the application of AI technologies such as machine learning, deep learning, natural language processing, and reinforcement learning in credit risk assessment, fraud detection, market risk forecasting, and operational risk management. The study synthesizes findings from recent scholarly literature published between 2018 and 2026 to identify major trends, methodologies, datasets, and evaluation frameworks used in AI-driven financial risk systems. The review further discusses ethical concerns including algorithmic bias, explainability, regulatory compliance, and cybersecurity vulnerabilities associated with AI deployment in finance. The paper concludes by highlighting future research directions involving explainable AI, federated learning, and hybrid financial intelligence systems for robust and transparent risk management.
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