AI-Powered Predictive Risk Assessment: A Review of Machine Learning Approaches and Applications

Abstract

Predictive risk assessment — the use of historical and real-time data to forecast the likelihood and severity of future adverse events — has been transformed by advances in machine learning (ML). This review examines the principal ML approaches used for predictive risk assessment and their applications across financial, cybersecurity, supply chain, and enterprise-operations domains. Reviewed methods include ensemble and gradient-boosting classifiers, artificial neural networks, and increasingly, large language model (LLM)-based and agentic AI systems that extend prediction into continuous, autonomous monitoring. Evidence reviewed includes financial-sector applications in which ensemble models such as XGBoost and Random Forest outperform traditional prediction techniques (Ahmed & Iqbal, 2025), cybersecurity applications in which supply-chain network features measurably improve out-of-sample prediction of enterprise data-breach risk (Hu et al., 2022), and operational-risk applications in which neural-network models trained on audit and risk-survey data reduce the subjectivity of human risk assessment (Burstein & Zuckerman, 2023). The review also considers applied enterprise-architecture research on configurable and metadata-driven platforms that operationalize predictive risk models at scale (Basireddy, 2022a, 2022b, 2023) and recent work on reliable LLM output generation and autonomous AI agents that extend predictive assessment toward continuous, self-directed risk monitoring. The review concludes with a discussion of persistent challenges — data quality and labeling scarcity for rare, high-impact events; model interpretability; and the governance of increasingly autonomous predictive systems — and outlines a research agenda for advancing predictive risk assessment as a discipline that integrates statistical rigor with enterprise-scale deployment architecture.