AI-Driven Risk Management: A Systematic Review of Predictive Analytics, Decision Support, and Risk Mitigation

Abstract

Artificial intelligence (AI) is changing how organizations identify, assess, and respond to risk. Traditional risk management relied on periodic assessments, static rules, and expert judgment. AI-driven approaches support continuous monitoring, probabilistic forecasting, and adaptive response across financial, operational, cyber, and enterprise domains. This review synthesizes literature on three connected pillars of AI-driven risk management: (1) predictive analytics for early risk detection and forecasting, (2) decision support systems that turn model outputs into actionable, explainable guidance, and (3) risk mitigation through automated, workflow-integrated, and increasingly autonomous responses. Drawing on studies of machine learning in credit and financial risk, explainable AI (XAI), cyber threat detection, generative AI and retrieval-augmented generation (RAG), configurable enterprise workflow architectures, and intelligent cloud resource scheduling, the review finds that AI consistently improves detection accuracy and response speed. Its value in practice, however, depends on architectural readiness, data quality, interpretability, and governance. The review proposes an integrated framework linking data and platform foundations, analytical intelligence, decision support, and mitigation execution. It also identifies open challenges, including model risk, hallucination in generative systems, adversarial manipulation, regulatory compliance, and the human–AI division of accountability.