Criminal and terrorist organizations are increasingly relying on international trade to hide the flow of illicit funds across borders. Big data analytics may be the key to tracking these financial flows.
or the past decade, governments around the world have established international anti-money laundering (AML) and counter-terrorist financing efforts in an effort to shut down the cross-border flow of funds to criminal and terrorist organizations. Their success has encouraged criminals to move their cash smuggling away from the financial system to the byzantine world of global trade. According to PwC US, big data analytics are becoming essential to tracking these activities.
It’s easy to understand why criminal and terrorist organizations would turn to the global merchandise export trade to hide the movement of their funds. It’s a classic needle in a haystack â an $18.3 trillion business formed of a “web of complexity that involves finance, shipping and insurance interests operating across multiple legal systems, multiple customs procedures, and multiple languages, using a set of traditional practices and procedures that in some instances have changed little for centuries,” PwC says.
Watching the Money Flow
There’s no real way to quantify how much money criminals are invisibly exchanging using this system. PwC notes that the Global Financial Integrity (GFI) research and advocacy organization estimates that 80 percent of illicit financial flows from developing countries are accomplished through trade-based money laundering (TBML), from more than $200 billion in 2002 to more than $600 billion in 2011. GFI believes more than $101 billion was illicitly smuggled into China in 2012 via over-invoicing, which is only one of the common TBML techniques.
“At its core, trade finance is an old-fashioned business,” the report says. “As other industries have adopted more technology- and data-driven infrastructures, trade finance has remained extremely document-intensive and paper-based, moored on a framework of instruments, systems, and practices that have proven their effectiveness and earned global trust over the generations.”
But they are also opaque, PwC says, making it extremely difficult for AML efforts to see what’s going on.
“For example, trade finance’s legacy procedures affect the relationship management aspect of AML, which includes know-your-customer (KYC) procedures and examination of customer documentation prior to transaction approval,” the report says. “In this paper-intensive environment, AML remains a largely manual procedure and thus prone to human error. It remains reliant upon established “red flag” checklists provided by regulators, in which transactions are manually reviewed by analysts, escalated should any concerns be raised, and then subjected to further manual review if wrongdoing is suspected.”
The Need to Share Data
This state of affairs is exacerbated by a number of factors, especially the lack of data sharing between customs, tax and legal authorities and a tendency to rely on AML procedures designed to target cash smuggling and financial system misuse. Instead, PwC says, authorities need to develop targeted TBML responses that focus on data sharing and text and data analytics.
So what exactly does TBML look like? Common TBML techniques include the following:
Under-invoicing. The exporter invoices trade goods at a price below the fair market price. This allows the exporter to effectively transfer value to the importer, as the payment for the trade goods will be lower than the value the importer receives when reselling the goods on the open market.
Over-invoicing. This technique is much the same as the first, except in reverse. The exporter invoices trade goods at a price above the fair market value, allowing the importer to transfer value to the exporter.
Multiple invoicing. With this technique, a money launderer or terrorist financier issues multiple invoices for the same international trade transaction, justifying multiple payments for the same shipment. “Payments can originate from different financial institutions, adding to the complexity of detection, and legitimate explanations can be offered if the scheme is uncovered (e.g., amendment of payment terms, payment of late fees, etc.),” the report explains.
Over- and under-shipment. In some cases, the parties simply overstate or understate the quantities of goods shipped relative to the payments sent or received. PwC calls out an extreme example of this, known as “phantom shipping,” in which no goods are exchanged at all, but shipping and customs documents are processed as normal.
False description of trade goods. With this technique, money launderers misrepresent the quality or type of trade goods. For instance, they might replace an expensive item listed on the invoice and customs documents with an inexpensive item.
Informal money transfer systems (IMTS). These networks have, in many cases, been co-opted by criminals and terrorists. PwC points to Colombia’s Black Market Peso Exchange (BMPE) as a prime example. Established by Colombian businesses trying to get around Colombia’s restrictive currency exchange policies, the BMPE allows users to sell dollars to a broker, who then trades them for Pesos to a legitimate Colombian business that needs hard U.S. currency to purchase goods for shipment to South America. It’s not just Colombian drug traffickers repatriating their profits either; PwC notes that similar systems exist around the world, including the hawalahundi system on the Indian sub-continent and others in Venezuela, Argentina, Brazil and Paraguay.
What Can Big Data Do?
So how can big data analytics help organizations find these illicit transactions in an $18.3 trillion haystack? Well, for one, the sea of documents generated by this activity â the commercial invoices, bills of lading, insurance certificates, inspection certificates, certificates of origin and more â that make it so difficult to see what’s truly happening may also be the point of vulnerability.
“A global, one-stop solution to TBML is highly unlikely,” PwC says. “The most effective solution would involve the imposition of bank-like compliance requirements on all organizations that trade internationally. But while this would create transparency across transactions, it would also create a massive layer of red tape that would adversely impact the preponderance of traders and related parties who are engaged in legitimate activity. The largely unquantifiable nature of the TBML problem makes it difficult to justify such an intrusive, expensive and vastly complicated solution. Short of global regulation, we have global analytics.”
In other words, automating anti-TBML monitoring â extracting and analyzing in-house and external data, both structured and unstructured â is of critical importance.
PwC believes such a program must properly align across key business areas and incorporate automated processes using a variety of advanced techniques, including:
Text analytics. The capability to extract data from text files in an automated fashion can unlock a massive amount of data that can be used for transaction monitoring.
Web analytics and Web-crawling. These tools can systematically scan the web to review shipment and custom details and compare them against corresponding documentation.
Unit price analysis. This statistic-driven approach uses publicly available data and algorithms to detect if unit prices exceed or fall far below global and regional established thresholds.
Unit weight analysis. This technique involves searching for instances where money launderers are attempting to transfer value by overstating or understating the quantity of goods shipped relative to payments.
Network (relationship) analysis of trade partners and ports. Enterprise analytics software tools can identify hidden relationships in data between trade partners and ports, and between other participants in the trade lifecycle. They can also identify potential shell companies or outlier activity.
International trade and country profiling analysis. An analysis of publicly available data may establish profiles of the types of goods that specific countries import and export, flagging outliers that might indicate TBML activity.
Orginally posted via “How Big Data Analytics Can Help Track Money Laundering”
Source: How Big Data Analytics Can Help Track Money Laundering by anum