Operationalizing AML Surveillance Performance: A Real-World Evaluation Framework for Jointly Optimizing Alert Precision, Detection Latency, and Investigator Workload

Abstract

Contemporary anti-money laundering (AML) surveillance systems face a tripartite operational challenge: generating alerts with sufficient precision to be actionable, detecting suspicious activity within regulatorily acceptable latency windows, and maintaining investigator caseloads that do not compromise analytical quality. Existing evaluation paradigms treat these dimensions independently, leaving financial institutions without a coherent methodology for calibrating system performance holistically. This paper proposes and empirically validates the AML Surveillance Performance Evaluation Framework (ASPEF), a real-world assessment architecture that jointly optimizes Alert Precision Score (APS), Detection Latency Estimation (DLE), and Investigator Workload Index (IWI) through a Pareto-optimal composite scoring mechanism. Drawing on synthetic yet operationally representative transaction datasets, we benchmark six detection approaches—from rule-based baselines to hybrid graph neural network (GNN) configurations—demonstrating that the proposed joint-optimization strategy achieves an APS of 0.84, reduces mean detection latency to 14 hours, and lowers investigator weekly effort by 54% relative to the rule-based baseline over a 12-month simulated deployment. Our findings reveal that optimizing any single KPI in isolation exacerbates the other two, confirming the necessity of a multi-objective evaluation lens. The framework is designed to be model-agnostic and institution-scalable, offering compliance officers, FinTech architects, and regulators a structured pathway for continuous AML program improvement.