From Rule-Based Detection to Adaptive Intelligence: A Critical Review of Artificial Intelligence Approaches to Financial Fraud Detection

Financial fraud has evolved from isolated transactional anomalies into organized, adaptive networks that challenge traditional rule-based detection. Deterministic frameworks remain transparent but fail under dynamic adversarial conditions, extreme class imbalance, and rapid behavioral shifts. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), graph analytics, federated learning (FL), and explainable AI (XAI) have transformed fraud prevention from retrospective analysis to continuous, adaptive risk assessment. This review synthesizes literature from 2020-2026 across five methodological paradigms: tree-based ensemble models for transactional scoring, sequence architectures for behavioral monitoring, graph neural networks for network-level fraud rings, federated learning for privacy-preserving collaboration, and explainable AI for regulatory compliance. It identifies persistent challenges including concept drift, false-positive costs, adversarial adaptation, and privacy risks. Evidence suggests that single-algorithm systems are insufficient for modern threat landscapes. Sustainable fraud prevention requires multi-layered, human-in-the-loop architectures integrating transactional, behavioral, relational, collaborative, and interpretive intelligence.