But the key concept is making computer systems understand more of what the user wants.ĭigital assistants that can manage large amounts of structured and unstructured information, and reconcile ambiguous and even self-contradictory data, which are good examples of cognitive computing, are constantly improving.
Let's briefly explore five innovative digital and analytical technologies that can optimize AML compliance.Ĭognitive computing has multiple definitions. The days of having scores of humans manually googling people and institutions and relying on internet pages filled with unstructured data are past.īanks must use advanced digital solutions to comprehensively scour worldwide public and non-public sources to ensure data quality quickly and effectively draw out, sift through, and analyze massive amounts of structured and unstructured information to reach meaningful conclusions and make results available in a user-friendly manner.
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Satisfying beneficial ownership and other AML requirements cannot be done using outdated and inefficient manual processes. This must be done globally, and firms must keep pace with the enormous number of changes continuously occurring. The new rule presents the challenge of looking through multiple, complex layers of legal ownership to understand key hierarchies, associations, and relationships and the risks they pose.
To meet these ever-growing demands and comply with industry standards, today's AML programs increasingly must rely on sophisticated automated analytical and digital processes and tools.Ī prime example: The new FinCEN rule governing beneficial ownership. Plus we now have a formal fifth AML pillar that focuses on ongoing customer-based risk. An AML program satisfactory to supervisors involves implementation and risk-based management of a number of moving parts, beginning with addressing the traditional four pillars of AML compliance-tailored internal controls, independent testing, ongoing training, and designation of a dedicated AML officer with sufficient resources.
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