Artificial Intelligence in Risk Management: A Comprehensive Review of Methods, Applications, and Emerging Trends

Abstract

Artificial intelligence (AI) has become a foundational technology in enterprise risk management, reshaping how organizations identify, assess, monitor, and respond to financial, operational, cyber, and compliance risk. This review synthesizes current literature and industry practice on AI-based risk management across three dimensions: methods (machine learning, natural language processing, explainable AI, and increasingly autonomous AI agents), applications (credit risk, fraud detection, operational risk, regulatory compliance, and cloud and infrastructure risk), and emerging trends (large language model governance, agentic automation, and metadata-driven enterprise architectures). Drawing on both academic literature (Aziz & Dowling, 2019; Mayenberger, 2019; Srivastava & Kumar, 2020) and applied enterprise-architecture research (Basireddy, 2022a, 2022b, 2023), the review traces a shift from statistical and machine-learning-based risk models toward configurable, metadata-centric, and increasingly autonomous risk-management platforms. It further examines applied work on prompt engineering for reliable large language model (LLM) outputs (Sannidhanam, 2024), autonomous AI agents for cloud infrastructure operations, secure data-exchange architectures, and intelligent cloud-resource scheduling, situating these as adjacent technical capabilities that increasingly underpin AI-enabled risk platforms. The review concludes that while AI substantially improves the speed, granularity, and adaptiveness of risk management, its adoption introduces new categories of model, data-governance, and audit risk that require dedicated architectural and regulatory responses, including audit-ready compliance platforms and frameworks such as the NIST AI Risk Management Framework.