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Beyond the Rules: Ensemble Strategy for AML in the Age of AI
How agentic AI, supervised, unsupervised machine learning, and rules are coming together to reshape AML programs
This report explores how forward-thinking organizations are adopting an ensemble AML strategy - one that combines the clarity of rules, the discovery power of unsupervised machine learning (UML), and the precision of supervised machine learning (SML), all enhanced by intelligent AI agents.
What’s Inside?
- Rules as a foundation
Why rules remain essential for compliance alignment—and how AI agents can tune them intelligently without sacrificing transparency. - Unsupervised ML in action
How UML reveals previously undetectable risks, including mule rings and nested laundering networks, using advanced behavioral clustering. - Scaling with supervised ML
How SML models streamline triage, enable auto-decisioning, and reduce analyst workload—while maintaining full typology coverage. - The ensemble advantage
A best-practices framework for orchestrating rules, ML, and AI into a unified, continuously improving AML strategy.
About DataVisor
DataVisor is the AI-native real-time decisioning engine for fraud and financial crime prevention.
As AI transforms both fraud attacks and fraud defense, DataVisor helps financial institutions, payment providers, and digital businesses detect, investigate, and stop sophisticated and previously unseen threats in milliseconds across billions of transactions. Combining adaptive machine intelligence, consortium intelligence, and emerging agentic AI capabilities, DataVisor enables organizations to modernize fraud operations, improve customer experience, and stay ahead of rapidly evolving financial crime. DataVisor is trusted by leading financial institutions, payment innovators, Fortune 500 enterprises, and digital businesses worldwide.