Our Federated solution

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Core AML/CFT model

Enhance transaction monitoring and customer due diligence

Correspondent Banking

Federated Learning model for enhanced risk management

High-Risk Typology models

 Uncover hidden high-risk typology

High-Risk Jurisdictions model

Identifying high-risk transactions 

 

In the news

Anti-money laundering (AML) risk management reimagined

The first Federated Machine Learning technology for AML and financial crime detection.

Federated learning shares suspicious behavioral patterns without ever moving data

Dramatically improves efficiency & effectiveness of AML controls

Reduces false positive alerts by

75%

300%

Improved identification of high-risk customers

Proactively pinpoint new and potential risks

Latest posts

January 20, 2026 | Blog

The future of AML effectiveness: The metrics regul..

Coverage, precision, prioritization, and case aging reveal an AML program’s true operational behavior un..

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January 15, 2026 | Blog

Introduction to Crypto AML compliance and DeFi act..

More than $2 billion in crypto was stolen in 2025, with stablecoins accounting for over 60% of identified illi..

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January 15, 2026 | Blog

pKYC vs. periodic reviews: The future of Enhanced ..

Banks don’t fail at KYC because they lack data. They fail because customer risk stops learning once onboardi..

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