Financial Crime is Becoming Harder for Authorities to Trace – AI Technology Can Help

Take a look at any news source and more than likely you’ll find a recent article on financial crime. Gone are the days of ski masks and bank holdups, now financial crime takes place behind the scenes using untraceable accounts, stolen personal information, and money laundering tactics. Criminals have changed organized crime using innovative technology created for the average do-gooder. With cybercriminals utilizing such advanced technology, how can banks keep up?

IBM Technology Reducing False Positives

Financial institutions comb through thousands of flags on watch lists to ensure money laundering activity is dealt with in a timely manner. However, many alerts end up being false positives, something that IBM is out to change. IBM’s Promontory Financial Group has created the Financial Crimes Insight for Watson platform that is able to identify 30 to 50 percent of alerts as false positives, reducing unnecessary work for banking employees. This technology also helped bankers perform customer due diligence in as little as five minutes and 20 seconds.

“A robust metrics/reporting program will help to carve out current revenue performance and risk levels, both at a customer level and at an overall portfolio level. Predictive analytics models such as a revenue propensity model and dynamic customer risk rating model can help determine future revenue streams and changes in behavior, including an uptick in risk,” explained Nikhil Aggarwal, Director of IBM’s Promontory Financial Group.

Dutch authorities have fined ING Group 775 million euros, about $900 million, for their participation in money laundering activities. ING Group failed to report suspicious activity and took no action when customers were subject to possible illicit activity. Implementing the right watch list technology could save other financial institutions from the mess ING has ended up in. According to an IDC study, financial institutions who implement entity resolution solutions are more likely to meet Financial Crime Compliance (FCC).

“What we found that was by reducing the noise up front in the process, pre TMS and prior to an event ever becoming an alert requiring investigation, financial institutions could have a dramatic impact while improving the accuracy of the matches between the party and portfolio,” explained Jim Burnick, Financial Services Managing Director at Pitney Bowes.

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AI is a Crime Fighting Machine

Many financial institutions have implemented artificial intelligence (AI) to better direct employee’s time, but what about anti-money laundering (AML) initiatives? Jim Arndts, director of enterprise financial crimes compliance strategy, transformation and governance at US Bank, explains that AI can be used for watch list verification, providing real-time notifications that have been thoroughly vetted.

“Improving our alert production and alert management processes is the biggest opportunity we face in our industry,” said Arndts. “The industry is familiar with producing a large number of alerts: many are high value, many are lower value. With the prototyping and proof-of-concept work we’re doing, we can have big gains towards eliminating the items that we don’t need, looking at how they’re generated, focusing on the higher‑value alerts and getting to the higher‑value cases.”

According to a recent C.D. Howe Institute report, Canada is a popular destination for criminals partaking in money laundering activities due to the weak financial laws. The report highlighted Canada’s rules for corporations and trusts, which are few compared to other countries, resulting in an influx of owners who can hide the identities and dirty money.

“Organized crime, tax evaders and money launderers don’t stand still. Their dirty money flows on a path of least resistance to the safest harbor,” said author Denis Meunier.

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To learn more about AML technology, click here.