AI-DRIVEN RISK MANAGEMENT IN FINANCE: TOWARD PROACTIVE AND PREDICTIVE FRAMEWORKS

Abstract

The rapid evolution of artificial intelligence (AI) is fundamentally reshaping the landscape of financial risk management. Traditional risk models, grounded primarily in historical data and linear assumptions, struggle to keep pace with the complexity, velocity, and interconnectedness of modern financial markets. This article examines how AI—especially machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL)—is enabling a shift from reactive to proactive and predictive risk management. We review emerging methodologies, evaluate benefits and limitations, and outline a forward-looking roadmap for integrating AI into enterprise risk architectures. The findings underscore that while AI offers unparalleled capabilities for risk detection, scenario forecasting, and adaptive decision-making, responsible governance, transparency, and human oversight remain essential for safe and effective deployment.