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Regulatory attention is shifting from technical AML compliance toward demonstrable, risk-based effectiveness, and every claim of effectiveness now has to be evidenced with traceable data. Most programs cannot produce that evidence, because AML data sits in ten or more systems with ten or more identity models.

This white paper treats the problem as architectural rather than procedural. It maps the laundering typologies that account-level detection misses, then details a four-layer blueprint on one governed platform: a data foundation that federates legacy warehouses before migrating them, entity resolution applied before detection logic runs, graph analytics and machine learning risk scoring layered on top of existing rules, and a human accountable agentic layer for investigations. It closes with a control to evidence mapping for examiners and a phased roadmap that delivers a measured outcome before any full migration.