AI-Driven Anomaly Detection and Risk Early-Warning Across Domains: A Systematic Review of Methods, Architectures, and Deployment Challenges

Abstract

Anomaly detection and risk early-warning have become a unifying computational problem that recurs, with remarkably similar structure, across finance, cybersecurity, healthcare, regulatory compliance, recommendation, computer vision, sustainability, and large-scale systems operations. Although these domains differ in vocabulary and consequences, the underlying task is invariably the same: to learn what is normal from heterogeneous and often imbalanced data, to flag deviations with adequate lead time, and to do so in a manner that practitioners can trust and lawfully deploy. This paper presents a systematic review of one hundred and ninety-six recent studies that, taken together, trace the evolution of this field from classical feature engineering and ensemble classifiers to graph neural networks, deep temporal models, foundation models, and agentic architectures. We propose a unifying taxonomy that organizes methods along five layers—data and feature foundations, relational and graph models, temporal and deep models, ensemble and class-imbalance strategies, and foundation models and agents—over a common substrate of cross-cutting concerns comprising explainability, fairness, calibration, privacy preservation, and deployment monitoring. We then synthesize cross-domain evidence to identify which method families have proven most effective for which classes of risk, quantify the distribution of research effort across application areas, and examine recurring deployment obstacles including label scarcity, concept drift, adversarial manipulation, regulatory constraints on data sharing, and the calibration of warning thresholds against operational lead-time requirements. The review concludes that the most durable advances are not isolated algorithmic gains but architectural patterns—multi-source fusion, relational modeling, privacy-preserving collaboration, and explanation-aware scoring—that transfer across domains, and it outlines open challenges in robustness, governance, and the safe use of generative and agentic models for high-stakes detection.