AI USE CASE
Trade-Based Money Laundering Detection
Detect over/under invoicing and trade fraud schemes using ML on commercial transaction data.
What it is
Machine learning models analyse trade finance documents and transaction patterns to flag anomalies consistent with trade-based money laundering (TBML), such as over/under invoicing, multiple invoicing, and falsified commodity descriptions. The system continuously learns from confirmed cases, improving detection accuracy over time while generating audit-ready alert rationales for compliance teams.
Data you need
Structured trade finance transaction records including invoice data, shipping documents, commodity descriptions, counterparty identifiers, and historical SAR/confirmed fraud labels for model training.
Required systems
- erp
- data warehouse
Why it works
- Integrate real-time commodity price benchmarks (e.g. UN Comtrade, World Bank) to anchor invoice valuation anomaly detection.
- Establish a dedicated compliance-ML feedback loop so investigators close the loop on every alert, continuously improving precision.
- Engage the regulator early to agree on model explainability standards and documentation requirements.
- Pilot on a single commodity corridor or geography before scaling to full trade finance portfolio.
How this goes wrong
- Insufficient labelled historical TBML cases starves the model of signal, producing high false-positive rates that overwhelm compliance teams.
- Trade data is siloed across legacy systems in incompatible formats, making feature engineering prohibitively expensive.
- Model flags commodity price anomalies without access to live market pricing feeds, generating noise rather than actionable alerts.
- Regulatory expectations around explainability are not met, leading to findings being rejected during AML audits.
When NOT to do this
Don't build a custom TBML model if your bank processes fewer than 5,000 trade finance transactions per year, the labelled case volume is too thin for reliable ML and a rules-based system will outperform it.
Example vendors
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