The Wolfsberg Group is a global authority on standards for know your customer (KYC), anti-money laundering (AML), and counter-terrorist financing (CTF) in banking. The group recently issued five key principles that financial institutions should use to guide their approach to use of AI and machine learning in their anti-financial crime programs.
Possibly because of the landmark ruling in Holland earlier this year, which upheld the legitimacy of neo bank, bunq’s, use of AI technology in its AML processes, the Wolfsberg Group has issued five new guiding principles for financial institutions to consider when adopting and using this type of technology in their fight against financial crime.
The group talks about what can be achieved when harnessing the power of AI and machine learning, including “efficiency and effectiveness” in compliance processes. But there is also a note of caution that financial institutions should exercise diligence in how it’s used, and care in the technology that is ultimately chosen, because it is the institution that will bear the responsibility for any outcomes – be they positive or negative.
Clearly, it is the amount of data that can be gathered and processed through automation that is both the boon and bane of the financial institution when using AI and machine learning. Algorithms can collect millions of pieces of data – some relevant, many irrelevant to a screening or risk monitoring process.
With such vast amounts of data to be processed, great care needs to be taken to choose the right technology that will filter out what is irrelevant, return what is material and important, and that won’t result in creating further exposure to risk, because there is simply too much data to interpret.
The AI platform chosen for effective and efficient support of an automated approach to anti-financial crime management must be developed around a firm’s risk appetite; it must learn from the decisions and outcomes the financial institution has made; and, crucially, it must not inadvertently become responsible for unconscious bias, resulting in poor decisions and people being excluded from the economy.
Machine learning and AI are helpful for doing the bulk of data collection work, and it can support decision-making, but understanding the nuances of humans, their behavior, and their risk levels can be missed by machines. Bringing people into the process at the right time to review the data and make decisions may be optimal.
There can also be issues created through the number of “false positives” an algorithm can create. “Solutions, like Moody’s Grid and AI Review, flag risk alerts to people for enhanced due diligence, having reduced and removed many of the false positives generated by gathering millions of pieces of information during a screening process.” said Hugo Veazey, Industry Practice Lead at Moody’s Analytics KYC.
Choosing the right provider with a history of risk management, an understanding of compliance globally, and a suite of innovative technology solutions is the right answer. The solution should also be flexible, so it can be adapted when and if further efficiencies can be found in an AML or CTF process.
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