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

February 28, 2025 | Blog

Visibility gaps in Correspondent Banking: why trad..

Financial institutions face an uncomfortable truth: despite investing billions in anti-money laundering contro..

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February 20, 2025 | Blog

Solving Correspondent Banking compliance: Consilie..

Correspondent banking is essential for global trade and cross-border payments, but it comes with significant r..

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February 18, 2025 | Blog

What banks did wrong in 2024: Critical AML failure..

Financial institutions paid more than $3.2 billion in AML-related fines last year. From major banks to emergin..

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