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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
Model
Improve and enhance
Transaction Monitoring alerts
for retail and business
banking customers
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Transaction Monitoring alerts
for retail and business
banking customers
Federated Synthetic Data
Turn regulatory intelligence
and financial crime expertise
into realistic, model-ready
behavioral data
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and financial crime expertise
into realistic, model-ready
behavioral data
SAR Analyzer
Examine narrative content
to identify patterns
and make connections
that would remain hidden
in individual case review
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to identify patterns
and make connections
that would remain hidden
in individual case review
Correspondent Banking Model
Designed to address
the unique risks associated
with correspondent
banking customer
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the unique risks associated
with correspondent
banking customer
Latest posts
October 8, 2026 | Blog
Federated Synthetic AML Data: 12x lift, zero real ..
Federated synthetic AML data is the latest federated product launching from Consilient this year, and is built..
READ MOREOctober 8, 2026 | Blog
How much data does an AML model need to catch fina..
Every bank that builds a machine learning model to catch money laundering runs into the same wall: there are n..
READ MOREAugust 20, 2026 | Blog
Human trafficking: The scale, exploitation, and ..
According to The International Labor Organization, nearly 50 million people are trapped in modern slavery, gen..
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