Hummingbird, the AI-powered orchestration platform for financial crime compliance, announced the acquisition of Quantimon, the machine learning platform for AML risk detection. The acquisition deepens Hummingbird’s investment in transaction monitoring, bringing machine learning models that learn from alert outcomes and federated learning technology onto the same platform where compliance teams resolve those alerts.

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Most anti-money laundering (AML) transaction monitoring still runs on rules, and most of the alerts those rules generate turn out to be nothing. Machine learning has been slow to help because models need labeled outcomes and, at most institutions, those outcomes sit in case notes that are too expensive to turn into training data. That’s changing with AI. When AI agents and compliance analysts work alerts in Hummingbird, each disposition is recorded in a structured form a model can learn from. With Quantimon integrated into the platform, those dispositions feed back into detection and improve it.

“AI has changed how compliance teams investigate financial crime, but the systems generating the alerts have stayed largely the same,” said Joe Robinson, co-founder and CEO of Hummingbird. “Today our customers can clear alerts faster than ever, but they shouldn’t have to spend that capacity on noise. With Quantimon, every alert worked by an AI agent or analyst sharpens detection, so more of the alerts that reach our customers will be ones worth their time.”

Quantimon’s machine learning models learn from how alerts are resolved, leveraging an institution’s own data in a closed feedback loop.

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