​What is Trade-Based Money Laundering?

Trade financing is a significant revenue stream for banks and a crucial factor in the economic growth of developing countries. However, it also presents challenges for anti-money laundering (AML) compliance. The Financial Action Task Force (FATF) has identified trade as a worrisome method for criminal organizations and terrorist financiers to move large sums of money while disguising their origins. Trade-based money laundering (TBML) is a sophisticated method for laundering dirty money, and it is difficult to detect due to the complexity of trade finance and global shipping logistics. As a result, many banks de-risk or limit their activity, which leads to businesses and economies losing access to global trade.

To address the challenges of detecting TBML, the FATF has issued guidelines that include specific risk indicators for TBML. These indicators are grouped into four categories: structural, trade activity, trade document and commodity, and account and transaction activity. However, traditional money-laundering flags used in a rules-based approach are not sufficient for the complexity of global trade, where many factors are involved in the transaction.

Artificial intelligence (AI)-based solutions are experiencing accelerated adoption by financial institutions in search of more sophisticated solutions to manage compliance operations. AI can effectively calculate risks to indicate suspicious activities outside of expected behavior in commerce. It can analyze a multitude of risk factors and pinpoint abnormal cases. AI-based solutions can also detect new and unpredictable typologies in trade, which is not possible with traditional rules-based approaches.

Trade finance is a key area of financial activity where AI can be deployed for AML programs. AI can detect and isolate abnormal cases from routine trade activity within complex sets of data. Some examples of data that can be analyzed include SWIFT trade codes, KYC, country risk factors, unusual shipment origin, industry codes, letter of credit amendments, and counterparty ratios.

In conclusion, trade financing presents significant challenges for AML compliance due to the sophisticated methods used by criminal organizations and terrorist financiers to launder money. The FATF has issued guidelines that include specific risk indicators for TBML, but traditional rules-based approaches are not sufficient for the complexity of global trade. AI-based solutions are gaining popularity and can effectively calculate risks to indicate suspicious activities outside of expected behavior in commerce.