Advanced Analytics Can Be Essential to Identifying Trade-Based Money Laundering Schemes
NEW YORK, January 8, 2015 – The trade finance system is being targeted by money launderers and terrorist financiers as they increasingly turn to global trade for moving illicit funds into the formal economy. According to a PwC US whitepaper, “Goods gone bad: Addressing money-laundering risk in the trade finance system,” the rise of trade-based money laundering (TBML) presents serious and costly risks associated with a growing number of fraudulent transactions.
Money laundering and terrorist funding activities continue to gain strength and prominence. As much as 80 percent of illicit financial flows from developing countries are now channeled through TBML methods, according to Global Financial Integrity, a research and advocacy organization. The growing number of TBML transactions has gained the attention of regulators, who are increasingly citing these illicit activities in warnings they issue to global financial institutions.
“Today’s trade-based money laundering activity goes beyond traditional laundering of criminal funds to include terrorist financing and intentional efforts to circumvent international sanctions,” said Vikas Agarwal, a Managing Director in PwC’s Advanced Risk & Compliance Analytics practice. “To evade detection, traffickers are becoming more sophisticated in their methods, and financial institutions should remain two steps ahead by deploying advanced analytical and statistical techniques. It’s an issue that has become a top concern in Board rooms and C-suites across the globe.”
According to PwC, the challenge lies in identifying the questionable transactions within the haystack of the massive global trade business. Among the TBML techniques used by launderers, under- and over-invoicing is quite common, as small discrepancies in stated values make the odds of detection, using current systems, close to zero.
PwC’s whitepaper points to the mining of Big Data as a critical component of an effective anti-TBML program, which involves extracting and analyzing data that is both structured and unstructured, and that resides both in house and externally. The program should also properly align across key business areas and incorporate automated processes using a variety of advanced techniques, including:
Text analytics. Text analytics tools can identify numbers, words and important data points; they can also identify context such as specific form fields (e.g., customer names, types of products).
Web analytics and web-crawling. These methods can search websites to review shipment and customs details and compare them against their corresponding documentation.
Unit-price analysis. A statistics-driven approach that uses publicly available data and algorithms and can 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 life cycle; they can also identify potential shell companies or determine 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.
To download a full copy of the report, please visit: http://www.pwc.com/us/en/cfodirect/issues/risk-management/trade-finance-money-launderings.jhtml
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