Subscribe / Unsubscribe Enewsletters | Login | Register

Pencil Banner

How big data analytics can help track money laundering

Thor Olavsrud | Jan. 21, 2015
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.

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 — the commercial invoices, bills of lading, insurance certificates, inspection certificates, certificates of origin and more — 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 — extracting and analyzing in-house and external data, both structured and — 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.


Previous Page  1  2  3 

Sign up for Computerworld eNewsletters.