Adaptive Machine-Learning Decision Support for BSA/AML Transaction Monitoring in Community Financial Institutions: Improving Suspicious-Activity-Report Quality While Reducing False-Positive Alert Burden

Abstract

Suspicious-activity monitoring in community and mid-sized financial institutions still leans heavily on static, rule-based alerting engines that generate overwhelming volumes of false-positive alerts, straining investigative teams and slowing the production of high-quality Suspicious Activity Reports (SARs). This article proposes Tab-AML, an adaptive, transformer-based decision-support model designed to sit alongside existing rule-based Bank Secrecy Act / Anti-Money-Laundering (BSA/AML) transaction-monitoring systems. Tab-AML uses a dual-masked transformer encoder architecture with a residual-attention mechanism and a shared-embedding scheme designed to learn micro-level (sender-receiver) and macro-level (whole-transaction) patterns simultaneously. Building on published evidence that classical ensemble methods such as XGBoost and random forest already outperform simpler baselines for transaction-monitoring classification, and that transformer architectures have shown early promise in adjacent financial-crime domains, this article sets out Tab-AML's architecture in detail, proposes an evaluation methodology using a large-scale synthetic transaction-monitoring benchmark such as AMLSim, and details the specific hyperparameter search space, baseline comparisons (against TabTransformer, TabNet, and five classical machine-learning models), and false-positive-rate-at-fixed-true-positive-rate evaluation protocol that such a study would need to follow to test whether Tab-AML's architectural innovations translate into a measurable reduction in false-positive alert burden relative to existing deep-learning and classical approaches. The findings from published research on related architectures suggest that adaptive ML decision-support layers hold real promise for materially reducing alert burden without weakening detection coverage, offering a potentially practical, deployable path for resource-constrained community financial institutions to modernise their transaction-monitoring programmes without discarding existing rule-based controls, pending the empirical validation this article proposes.