Financial institutions are spending billions to combat money laundering and other financial crimes, but is it working? Recent headlines with billions in laundered money like Danske Bank, Deutsche Bank, or ING Group suggest that legacy anti-money laundering systems are failing to meet new demands. Using data-driven technology and artificial intelligence, banks can detect and address money laundering activates sooner and more efficiently. Read on to learn about recent money laundering cases and the technology that can prevent them.
Preliminary money laundering charges have been filed against the biggest bank in Denmark for violations of money laundering laws. This is the first step to formal charges after Danske Bank admitted that more than $226 billion had moved through an Estonian branch between 2007 and 2015 linked to money laundering activity.
Danske Bank’s interim chief executive, Jesper Nielsen, said, “It is in our interest that the case is fully investigated,” and that the bank plans to cooperate.
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The European Union finance ministers are proposing money laundering reform that will work “to strengthen the effectiveness of the current framework,” but does not propose legislative changes. This reform was sparked by recent money laundering across the EU including states like, Denmark, Estonia, and Malta.
The proposal recognizes that the legal action surrounding money laundering need to be improved and urges “rapid” change. The plan will be enacted on December 4th at the EU finance ministers monthly meeting. The plan is set to be carried out until 2020 and creates a path to reduce the discretion of national supervisors in applying anti-money laundering regulations.
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How can Money Laundering be Stopped?
By reading the morning headlines, we know that money laundering is a serious issuing plaguing multiple nations across the globe. To reduce these crimes, banks and financial institutions can implement data-driven technology that analyzes relationships and transactions to determine risk and alert employees when financial crime is likely.
“In the last five years global AML spend has risen from $5.9 billion to over $8.2 billion,” said Richard Stocks, Managing Director of Financial Crimes and Compliance Solutions at Pitney Bowes. “Banks and other financial institutions have invested in AML and anti-fraud solutions but have not been able to stem criminal activities, or able to achieve a return on their investments in this technology. A significant part of the problem, here, is that frequently, financial institutions are starting out with poor quality data, which makes the job of identifying an event very difficult.”
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Artificial intelligence can be implemented by banks and financial institutions to not only complete repetitive tasks, but to fight financial crimes. For example, Capital One uses AI in the Capital One Center for Machine Learning to explore technology that can help banks pair AI and machine learning to simulate human intelligence.
Capital One is using these technologies to build anti-money laundering tools that are scalable and effective. Leveraging data has opened up ways to customize user experience, make informed decisions on customers, and bolster security.
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To learn more about anti-money laundering tools, click here.
