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Federated Learning Models for AML/CFT

Our Federated Models

Introducing federated learning models for anti-money laundering (AML).

Consilient models are crafted to significantly enhance efficiency and effectiveness in identifying risks through the exchange of behavioral patterns and insights, all while preserving data privacy.

Designed to address the challenges with core AML processes, federated learning introduces industry collaboration to fight financial crime.

Core AML/CFT <br />Model

Core AML/CFT
Model

Improve and enhance
Transaction Monitoring alerts
for retail and business
banking customers
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Correspondent Banking Model

Correspondent Banking Model

Designed to address
the unique risks associated
with correspondent
banking customer
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High-Risk <br />Typology Models

High-Risk
Typology Models

Identify and uncover
hidden high-risk
typologies
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High-Risk <br />Jurisdictions Model

High-Risk
Jurisdictions Model

Identifying High-Risk
transactions from
high risk countries
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KYC/AML Risk<br />Rating Model

KYC/AML Risk
Rating Model

New copy
New copy
New copy
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Latest posts

July 19, 2025 | Blog

The gaps criminals exploit: Why global AML collabo..

Criminals exploit gaps in both banks’ monitoring systems and also take advantage of jurisdictional gaps...

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July 16, 2025 | Blog

Why smaller FIs face structural disadvantages in A..

Smaller financial institutions face structural disadvantages in meeting anti-money laundering (AML) compliance..

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June 17, 2025 | Blog

Smarter AML triage: How ranked scoring boosts risk..

The volume problem isn’t going away. But how you triage it can change. By the end of 2024, financial institu..

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