Big Data

Op-ed: Combating Blood Money With Big Data

“Illicit proceeds from crime are blood money, and blood money should have no place in the U.S. financial system.” That was the conclusion of a report released in April by the Senate Caucus on International Narcotics Control. The best defense against those illicit financial networks? Big data.

In 2009, an estimated $1.6 trillion -- 2.7 percent of global gross domestic product -- was laundered worldwide, including hundreds of billions of dollars in the United States. The Senate Drug Caucus makes clear that much more must be done to combat illicit financial transactions.

Federal officials have acknowledged that U.S. anti-money laundering efforts, measured in convictions and forfeitures, are abysmal. David Cohen, the Treasury Department’s undersecretary for terrorism and financial intelligence, recently announced that the Obama administration will conduct a wide-scale review of regulations under the 1970 Bank Secrecy Act.

The report notes gaps in the government’s anti-money laundering framework and calls for stronger Justice Department action, stricter disclosure rules for the formation of shell companies, cross-border reporting requirements for prepaid bank cards, and tougher enforcement of the 2007 National Money Laundering Strategy.

But the most striking recommendations tap data and analytics as the critical countermeasures to money laundering and terrorist financing. For starters, the report calls for better data collection.

Whether it’s processing the 18 million pieces of financial intelligence filed with Treasury each year, monitoring suspicious transactions, compiling suspicious telephone and license plate numbers, mining law enforcement and intelligence reports, or monitoring social media -- data must be collected. Data also must be formatted, analyzed and visualized.

“Far too little is known about the financial structures and procedures of drug trafficking organizations, particularly those from Mexico,” the report says, adding that efforts to understand drug trafficking finances are “severely lacking” on both sides of the border.

With the right reporting requirements, advanced analytics can help solve that problem. Rather than expecting analysts to know precisely what to look for, built-in alert systems can proactively identify, prioritize and present information to analysts based on pattern identification and quantification of risk.

The Drug Caucus says law enforcement and regulatory agencies should prioritize major investigations that target money laundering facilitators -- meaning data should be ranked and scored. By using predictive analytics, law enforcement officials can maximize scarce resources and staff to identify critical investigations.

Agencies also need to do a better job of examining the “volume of international trade and the prevalence of trade-based money laundering schemes,” the report says. This requires state-of-the-art software that can spot anomalies indicative of trade fraud or trade-based money laundering. The anomalies could open a back door to underground financial systems such as hawala and the black market peso exchange, both of which have been singled out for scrutiny by the Drug Caucus. Trade-based money laundering and hawala are frequently linked to terrorism both overseas and in the United States. 

“In a time of fiscal constraint, improving our anti-money laundering laws will serve the dual purpose of combating transnational organized crime while also bringing much needed revenue back to the United States Treasury,” the report says.

In short, cracking down on the torrent of blood money makes good sense and is good policy. Because of the exponential growth in data, combined with recent advances in analytics, law enforcement agencies have an opportunity to hit criminal organizations where it matters most -- in the pocketbook. 

John A. Cassara, a former intelligence officer and Treasury Department special agent, is author of several books on money laundering and terror finance and is an industry adviser to SAS Federal LLC.

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