Banking / Finance

Navigating the Big Data Nuances of Financial Crimes

Catch me if you can. That seems to be the mantra of the modern day cyber thief, fraudster or money launderer. Cloaked behind anonymizers, spoofing and botnets, criminals employ extensive means to go undetected. While advances in technology can provide organizations with the proactive tools needed to best tackle these crimes, the problem is still pervasive.

According to estimates from the International Monetary Fund and the World Bank, between $2-3 trillion is laundered around the world each year. To illustrate the challenge of combating laundering, a 2013 report from the Council of Foreign Relations found that government regulations and law enforcement agencies are only able to detect and stop $170 million in money laundering activities annually.

To detect and combat criminal activity, financial organizations are working to implement a framework of strong, effective defenses while also minimizing disruption to their businesses. With today’s market volatility and increased pressure from regulators for financial institutions to detect and prevent financial crime, the requirements to comply with regulations, while viewing the problems from all angles and across multiple departments, are imperative.

When I managed a financial crimes Intelligence and Analytics team, I saw this challenge firsthand. Any move toward an integrated approach to fight fraud, money laundering and cyber threats hinges on the availability of data. Whether extracted from various, disparate databases or accessible as the result of investment in Big Data initiatives, developing an integrated approach only moves forward when organizational silos are removed and data is viewed as an asset from which to derive intelligence.

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