Real-Time Anomaly Detection in Distributed Financial Transaction Streams Using Machine Learning: From Statistical Detection to Operational Reliability

Abstract

Financial transaction monitoring is increasingly expected to identify anomalous and potentially fraudulent activity while transactions are still operationally actionable. This requirement is more demanding than conventional offline classification because the detector is embedded in an unbounded, distributed stream in which class prevalence is extreme, labels are delayed or selectively verified, behaviour changes over time, and feature state may be distributed across machines. This critical narrative review synthesises evidence on machine-learning methods, data-stream adaptation, distributed stream processing, operational evaluation, privacy, explainability and adversarial reliability. Literature published from 1 January 2000 to 19 July 2026 was considered, with emphasis on peer-reviewed studies that illuminate realistic transaction-stream conditions. The evidence indicates that effective real-time detection depends less on a single algorithmic family than on alignment among behavioural representation, temporal validation, adaptive learning, stateful stream semantics, decision thresholds and investigation capacity. Supervised tree and ensemble approaches remain strong operational baselines when labels and engineered behavioural features are available; sequential, unsupervised and graph-based methods add value when temporal or relational context is material, but their deployment benefits are often less well established than benchmark gains. Concept drift and verification latency create a persistent mismatch between current transactions and historical labels, while distributed execution introduces correctness risks through late events, replay, stale features and recovery. Accuracy-centred evaluation is therefore inadequate: precision-recall behaviour, calibration, cost, alert capacity, end-to-end latency, throughput and recovery characteristics should be assessed together under temporal replay. Privacy-preserving collaboration and explainability can support governance, but neither federated learning nor post-hoc explanation resolves the underlying problems of heterogeneity, security and decision accountability. The review concludes that the main research priority is integrated statistical-systems evaluation using realistic, replayable streams with delayed feedback and explicit service-level constraints. Such evaluation would make apparent whether an apparent modelling improvement survives the conditions that determine operational value.