Global Money Laundering Network Dismantled in Coordinated International Investigation

In a joint action day supported by Europol and Eurojust, 33 people have been arrested for their alleged connection to a global money laundering network. According to investigators, the network ran a trade-based money laundering (TBML) scheme that utilized companies in China, Turkey, and the United States to conceal the origin of illegal funds linked to drug trafficking in South America.

Led by law enforcement agencies in Italy and France, the investigation led to the seizure of €18.5 million in assets. 

The Black Market Peso Exchange

Commonly used by narcotics traffickers in Mexico and Columbia, the Black Market Peso Exchange has been used as a TBML method since the 1980s. In this case, the money laundering network acted as a “controller” – an integral feature of the scheme that ensures drug sales revenue do not appear to cross any borders. 

According to law enforcement, South American drug cartels provided Italian buyers with narcotics as a form of credit. The proceeds generated from the sale of the drugs in Europe were then collected by brokers, introduced into various companies worldwide, and used for ordering goods – such as mobile phones from China. Once the goods had been purchased, they were shipped to the US and then to Colombia, where they were put on the market. Upon being sold, the initial drug cartels received the funds as payment for the drugs sold to European buyers.

TBML Methods

In addition to the Black Market Peso Exchange, the Financial Action Task Force (FATF) identifies the following TBML methods and typologies often used by criminal networks:

• Over- and under-invoicing of goods and services.
• Falsely describing goods and services.
• Multiple invoicing of goods and services.
• Over- and under-shipment or phantom shipment of goods and services.
• Informal value transfer systems. 

Trade-Based Money Laundering Risk Indicators

In a guidance paper published by the FATF in March 2021, TBML red flag indicators are listed and categorized according to the type of risk they present. The four types of risk firms may need to account for include:

• Structural risk: Indicators of this type of risk include unusually complex or illogical corporate structures and trading entities with addresses that do not reflect the business in which they are engaged.

• Trading risk: This can occur when a trading entity overly complicates the use of financial products, intermingles different products, or uses a single product for an unusual amount of time.  
• Document risk: Trade documents containing vague or general references to the commodities being transported can indicate document risk in TBML. This type of risk also relates to missing, counterfeit, or falsified trade documents.  
• Account and transaction risk: Indicators may include accounts engaging in high volumes of transactions that are inconsistent with their stated business activity and payments for imports made by parties other than the account holder.  

Key Takeaways

Since TBML can involve multiple parties and jurisdictions, some schemes can be very challenging to detect. To mitigate the threat of TBML, compliance staff should complete business-wide risk assessments as required to determine their risk exposure. Firms should then review their anti-money laundering (AML) solutions in light of this assessment, ensuring their tools are effectively calibrated according to their predefined risk appetite. Some solutions to help detect and prevent TBML include:

• AI-powered transaction monitoring that can keep up with the scale and speed of TBML.
• Adopting a risk-based approach to TBML and partnering with a transaction monitoring provider that allows custom rule sets to be built and deployed quickly with the right thresholds set based on risk exposure.
• Uncovering hidden relationships between exporters and importers by implementing customer due diligence (CDD) measures that are built on a combination of technology and expertise.
• Employing a negative news screening solution that can analyze true adverse media context at scale so no relevant information is missed.