Automated AML Systems, False Positives, and the Risk of Institutional Disproportion: A Structural Assessment for the U.S. Financial System

This independent analytical report examines structural risks arising from the increasing reliance on automated anti-money laundering (AML) systems within the United States financial sector, with particular focus on false positive signals and their institutional consequences. As financial institutions deploy advanced algorithmic tools for transaction monitoring, customer screening, and risk scoring, the scale and complexity of automated decision-making continue to grow. While these systems enhance detection capabilities, they also generate high volumes of false positives that may produce disproportionate compliance responses, operational inefficiencies, and secondary systemic effects. The report analyzes how automated AML screening interacts with defensive compliance practices, reputational risk management, and regulatory reporting obligations, including the impact of increased Suspicious Activity Report (SAR) volumes and information overload within supervisory frameworks. It further explores risks of institutional imbalance, declining proportionality, and long-term implications for trust in the financial system. A comparative overview of European Union and United Kingdom approaches to algorithmic supervision and proportionality is provided, alongside policy-oriented recommendations addressing explainable AI, recalibration of risk models, and enhanced dialogue between regulators and financial institutions. Prepared by ARGA Observatory, this report is intended for regulators, financial institutions, compliance professionals, policymakers, and academic researchers engaged in AML governance, financial regulation, and algorithmic risk management.