Adaptive Anomaly Detection for Financial Fraud under Evolving Market Regimes

Abstract

Financial fraud detection systems must operate in environments where market regimes, payment infrastructures, adversary tactics, and regulatory expectations change continuously. Static supervised classifiers trained on historical fraud labels tend to degrade when volatility, liquidity conditions, customer behavior, or transactional channels shift. This paper examines adaptive anomaly detection as a socio-technical infrastructure rather than a purely algorithmic task. It argues that effective fraud detection under evolving market regimes requires layered architectures that combine unsupervised anomaly detection, supervised risk scoring, graph analytics, streaming feature computation, human review, and governance feedback loops. The analysis emphasizes structural trade-offs among detection sensitivity, false positive burden, latency, explainability, fairness, and operational sustainability. It considers how concept drift, label delay, adversarial adaptation, and regime discontinuities complicate evaluation and model maintenance. The paper also assesses deployment concerns, including data lineage, model monitoring, technical debt, energy cost, federated learning, and auditability. Fairness and policy implications are treated as central design constraints because fraud systems allocate scrutiny and can produce disparate impacts across customers and institutions. Cross-domain comparisons with cybersecurity anomaly detection and graph-based risk analytics clarify both transferable lessons and limits. The conclusion proposes that adaptive fraud detection should be governed as a continuously calibrated decision infrastructure, where technical adaptation is paired with institutional accountability, transparent change management, and robust human oversight.