Trade-based money laundering (TBML) remains one of the hardest threats to detect across the global financial system. Criminal networks manipulate cross-border trade to move illicit funds, using the scale and complexity of international commerce as cover. Some estimates suggest TBML accounts for 87% of global illicit financial flows, while more than 51% of compliance leaders rank it as a top threat to their institutions.

RegTech firm ZIGRAM is tackling the challenge through an ecosystem of solutions designed to connect entity screening, transaction monitoring and due diligence. The need for a more joined-up approach is clear, as financial institutions often review trade documents individually, checking invoices for completeness or matching letters of credit against their terms. While this can catch obvious errors, it often misses the wider anomalies that define laundering schemes.

Organisational silos compound the problem. Trade operations handle documents, payments teams process transfers and compliance teams screen names, leaving no single team with a complete view of the transaction.

A data-driven approach can help close these gaps. Structured data is extracted from invoices, packing lists, bills of lading and payment messages, then standardised and compared across systems. In a hypothetical scenario resembling publicly discussed TBML cases, an electronics exporter invoices shipments at 35–40% above market value, packing list weights differ from bills of lading, the buyer is a newly incorporated company with no visible operations, and payments arrive from unrelated third parties.

Each discrepancy might have a legitimate explanation in isolation. Together, they create a stronger indication of potential money laundering.

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