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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

December 17, 2024 | Blog

Anomaly detection at scale: Consilient’s app..

Financial crime has never been more sophisticated. Bad actors constantly evolve their tactics, exploiting gaps..

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December 17, 2024 | Blog

Banking on collaboration: How to overcome the stal..

Money laundering activities in the United States account for an estimated $300 billion annually alone. Over th..

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November 19, 2024 | Blog

Anti-money laundering (AML) risk management reimag..

AML approaches rely heavily on manual processes, just to prepare Suspicious Activity Reports is estimated to c..

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