Consilient

Founded 2020

Detect anti-money laundering risks across financial institutions using federated machine learning without moving data

Pricing model

Demo

You can use Consilient to detect money laundering and counter terrorist financing by training machine learning models across institutions without moving sensitive customer data. It analyzes transaction patterns, reduces false positive alerts, and pinpoints high-risk accounts or jurisdictions. The system includes modules for correspondent banking, high-risk typologies, and customer risk rating. Financial organizations can share suspicious behavioral signals privately, generate synthetic data for testing, and analyze suspicious activity reports to meet regulatory compliance while keeping private financial records secure.

What Consilient does

Train machine learning models on decentralized financial recordsIdentify hidden transaction patterns linked to money launderingGenerate synthetic AML transaction datasets for model validationReview and prioritize suspicious activity alertsAssess cross-border correspondent banking risk levelsScore customer accounts based on dynamic AML risk factorsTrack illicit fund transfers tied to high-risk jurisdictionsFederated machine learning across multiple financial institutions without transferring customer dataSynthetic AML data generation to train models without exposing actual customer recordsAutomated Suspicious Activity Report (SAR) analysis and triageCorrespondent banking risk monitoring across distributed networksIdentification of high-risk jurisdictions and complex criminal typologiesContinuous customer due diligence and dynamic KYC/AML risk ratingFalse positive alert reduction by sharing suspicious behavioral patterns