AI USE CASE
Trade-Based Money Laundering Detection
Detect over/under invoicing and trade fraud schemes using ML on commercial transaction data.
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Run the diagnostic →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. Banks deploying TBML detection typically reduce false-negative rates by 30-50% compared to rule-based systems and cut manual investigation time by 20-35%. 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.
Vendors to consider
Sources
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