While anti-money laundering (AML) programs may present the biggest opportunity for machine learning to thrive, there are many issues when it comes to fighting money laundering. The number and high caliber of resources required to battle fraud in today’s highly regulated environment are immense, and the cost – of both personnel and technology required to manage the increase in criminal activity, not to mention meet regulatory requirements – continues to grow. In fact, according to the study conducted by PWCGlobal, “Nineteen percent of those surveyed claim that their ability to hire experienced staff is the biggest challenge to AML compliance.”
Moreover, most of the technology deployed today does not have the ability to view all the data that is essential for combating these crimes. As it stands today, 33% of financial services firms face challenges with data quality, the study found.
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When it comes to detecting criminal activity, some of the most notable difficulties revolve around the fact that a good portion of money laundering activity is never detected. One study suggests that “only 50% of money laundering or terrorist financing incidents were detected by system alerts.” This creates a significant financial loss for firms. Another major challenge for companies is that there are simply too many false positives.
At the end of the day, these issues can lead to even more severe consequences, such as large fines and personal liability for company executives. According to the Global Economic Crime Survey, one survey even indicates that “18% of banks have recently experienced enforcement actions by a regulator.”
In our new guide, we explore the basic tenets of AML and know your customer (KYC), as well as how you can leverage the power of machine learning technologies to enhance your firm’s compliance programs. You can download it here.