Organization:RaptorX AI
Copyright:RaptorX AI
Credit:RaptorX AI
Organization:RaptorX AI
Copyright:RaptorX AI
Credit:RaptorX AI
Spot structured laundering and circular trade value flow — before it disappears into clean accounts.
Trade-Based Money Laundering (TBML) is one of the most sophisticated and underreported laundering typologies.It exploits legitimate trade flows — through fake invoices, circular shipments, and mispriced goods — to move illicit funds across borders while appearing compliant.
Layering via shell entities, manipulated SWIFT references, and forged trade routes breaks traditional AML systems.
RaptorX connects the dots others miss — even when the trail is camouflaged.
Invoice and payment values are manipulated (over- or under-invoicing)
Funds move through unrelated third-party accounts (layering)
Shell or dormant entities are used as intermediaries
High-volume, low-value trades make detection difficult
SWIFT references & documentation appear clean but don’t align with trade logic
Flags discrepancies between declared invoice values and actual SWIFT-settled amounts across trade routes.
Identifies frequent counterparties with no trade footprint or mismatched jurisdictions indicating shell routing.
Catches circular, low-value trade flows that mimic recurring transactions but signal hidden layering intent.
Visualizes trade networks and highlights nodes with excessive, one-directional, or cyclical flows.
Quantifies the gap between trade documents and actual fund flows, using both rules and ML.
Directly ties suspicious trade cases to FIU/FATF-compliant suspicious activity reports (SARs).
Feed into your compliance, risk, or fraud case queues seamlessly.
Works even when documentation is forged or partial
No dependency on labeled laundering data — pattern-first and typology-aware
Combines trade flow data, SWIFT logs, and entity metadata in one system
Detects layering, circularity, and price/value fraud with explainable logic
See how RaptorX detects TBML in days — not quarters.