Trade-Based Money Laundering
By Mike Betron, Infoglide Director of Marketing
Who’d have thought that iTunes could be used for money laundering? Yet that is exactly what five men in Great Britain were recently jailed for the other day. Using stolen credit card numbers, they bought £750,000 in vouchers, then sold them at cheaper prices over eBay.
Methods of money laundering continue to evolve. When authorities constrain certain types of money laundering, perpetrators migrate to other methods. Since law enforcement has focused its efforts on two methods – (1) the movement of value through the financial system using checks and wire transfers, and (2) the physical movement of banknotes via cash couriers and bulk cash smuggling – a third method called “trade-based” money laundering is growing in popularity.
Trade-based money laundering is defined by the Financial Action Task Force (FATF) as “the process of disguising the proceeds of crime and moving value through the use of trade transactions in an attempt to legitimize their illicit origins.” Kenneth Rijock, Financial Crime Consultant for international anti-money laundering risk intelligence firm World-Check, recently commented that disguising funds as goods is now the way a significant portion most of laundered money is moved illicitly. “If I can move $100 million from New York to Columbia via Venezuela, I’m certainly not going to smuggle it down there when I can move it through trade-based money laundering.”
The newly revised Bank Secrecy Act and Anti-Money Laundering Examination Manual contains an expanded section on trade-based money laundering. These operations are successful because of the difficulty in detecting complex relationships between trading operations, operators, and money movements. Three key barriers make it tough to detect trade-based money laundering:
1. The tremendous volume of trade makes it easy to hide individual transactions;
2. The complexity that is often involved in multiple foreign exchange transactions; and
3. The limited resources available to agencies wanting to detect the fraud.
These barriers are difficult if not impossible for traditional methods to address. The volume of trade means that highly scalable automated methods are needed, but the complexity of sifting through multiple transactions and finding hidden connections is beyond the capabilities of normal methods.
For those familiar with identity resolution (e.g., Identity Resolution Engine [IRE]) technology, its strengths address these barriers directly:
- Volume – IRE can process millions upon millions of transactions daily in the largest and most demading application environments.
- Complexity – Non Obvious Relationship Analysis finds hidden relationships, or neural networks, across multiple disparate and remote data sources, including both internal and external data.
- Resources – Configurable, automated processes optimize the use of available human resources by eliminating “clean” transactions and prioritizing potential “dirty” ones.
