Consilient provides federated machine learning technology specifically for Anti-Money Laundering (AML) and financial crime detection. This solution enhances transaction monitoring and customer due diligence by sharing suspicious behavioral patterns without moving sensitive data. The platform significantly improves AML control effectiveness, reducing false positives while increasing the identification of high-risk customers and typologies.
Funding
$3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional anti-money laundering (AML) systems struggle to adapt to evolving financial crime tactics, resulting in inefficiencies and a high number of false positive alerts. Financial institutions face challenges in pinpointing new and potential risks due to limitations in sharing suspicious behavioral patterns. This can lead to increased operational costs and potential regulatory compliance issues.
Solution
Consilient offers a federated machine learning technology designed to enhance AML and counter-terrorist financing (CFT) efforts. The platform enables financial institutions to improve the efficiency and effectiveness of their AML controls by sharing suspicious behavioral patterns without direct data sharing. Consilient's approach reduces false positive alerts and improves the identification of high-risk customers, proactively pinpointing new and potential risks. The system overlays existing legacy systems, allowing for modern AML models without requiring a complete overhaul.
Target Audience
Consilient primarily serves financial institutions seeking to improve their AML/CFT risk management, including banks, credit unions, and other financial service providers.
Features
- Federated learning technology that shares suspicious behavioral patterns without moving data
- Core AML/CFT model to enhance transaction monitoring and customer due diligence
- Correspondent Banking model for enhanced risk management
- High-Risk Typology models to uncover hidden high-risk typologies
- High-Risk Jurisdictions model for identifying high-risk transactions
- KYC/AML Risk Rating model
- Reduces false positive alerts by up to 75%
- Improves identification of high-risk customers by up to 300%