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.
Core trade documents should align. Invoices should match contracts on unit price, Incoterms, currency and parties, while quantities and weights should reconcile across invoices, packing lists and bills of lading. Declared values and Harmonised System (HS) codes should also be consistent with customs declarations.
Common TBML typologies include over-invoicing, under-invoicing and multiple invoicing. The Black Market Peso Exchange combines trade misinvoicing with currency exchange to help repatriate criminal proceeds. Price variances of 15–25% against trade benchmarks, referenced in FATF–Egmont indicators, can provide useful screening thresholds, although legitimate explanations such as distressed sales and transfer pricing must also be considered.
Other warning signs include vague product descriptions, such as “spare parts”; scrap copper priced like refined cathodes; uneconomic routing through free trade zones; reused container IDs; shell-company intermediaries; third-party or split payments; and circular fund flows.
The key is to combine indicators rather than assess them in isolation. Multiple discrepancies that converge can turn a routine alert into a priority investigation. Investigators should first rule out clerical and conversion errors, then check relevant benchmarks and registries before assessing whether a credible commercial explanation exists.
Regulatory frameworks and industry guidance reinforce the need for effective controls. The Financial Action Task Force’s 40 Recommendations, the US Bank Secrecy Act, the EU’s Sixth Anti-Money Laundering Directive and the Wolfsberg Group’s Trade Finance Principles all inform expectations for detecting and preventing financial crime. Guidance from HMRC and FinCEN also supports risk-based monitoring and investigation.
ZIGRAM’s ecosystem includes PreScreening.io, Entity Hero, Transact Comply and Due Diliger, which are positioned to help connect information across counterparties, transactions and due diligence processes. By linking information across trade documents, counterparties and payments, financial institutions can build a more complete picture of suspicious activity and focus investigations on the strongest risk signals. With the World Bank estimating that $1.5tn is paid in bribes annually, the stakes for effective financial crime controls remain significant.
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