Modernizing Anti-money Laundering (AML) Transaction Monitoring (TM) Systems

As financial crimes grow more sophisticated and regulatory scrutiny intensifies, financial institutions must fundamentally rethink how they detect and prevent money laundering. Legacy rule-based Anti-money Laundering (AML) Transaction Monitoring (TM) systems, once sufficient for compliance, are now strained by the volume, speed, and complexity of modern transactions. With false positive rates exceeding 85%, fragmented data, and rising compliance costs, the need for transformation is urgent.

This report defines a structured path for modernizing AML TM systems – from static, threshold-based monitoring to adaptive, AI/ML-powered intelligence. It introduces Everest Group’s four-stage AML TM modernization framework, guiding institutions through the evolution from optimized rule-based models to fully AI/ML-driven monitoring. The framework integrates innovations such as graph analytics for entity resolution, agentic AI for investigations, and external data integration for enhanced risk detection.

Beyond technology, the study highlights data, operational, and governance barriers that hinder AI/ML scalability and provides pragmatic solutions to overcome them. It includes an enterprise maturity assessment model spanning seven dimensions – from data infrastructure and technology readiness to typology coverage and ecosystem collaboration – helping institutions benchmark progress and design their modernization roadmap.

The report also details a six-step execution roadmap for banks to operationalize AI/ML adoption responsibly, from strategic alignment and data integration to cloud migration, workflow automation, and regulatory engagement. The findings are based on Everest Group’s survey of 50 global banks across North America, Europe, the UK, and APAC, reflecting diverse priorities, spending patterns, and modernization challenges.

Ultimately, the Viewpoint enables compliance leaders to move from fragmented, rule-based processes toward unified, intelligent monitoring ecosystems that reduce false positives, strengthen regulatory trust, and enhance operational agility.