This wide-ranging article identifies how regulators have slowly opened up to accept the use of machine learning models as a method of detecting AML activity, yet they remain concerned regarding the models’ lack of transparency. It reviews public comments made by key regulators regarding technology and the need to maintain balance between detection and inhibiting commerce and protecting privacy.
Here is one small part of the article that is well worth reading if you are interested in AML processing:
“At a November, 2018, “Fintech and the New Financial Landscape” conference in Philadelphia Pennsylvania conference Dr. Lael Brainard presented her view about the potential for AI and machine learning. In short, while Dr Brainard is bullish on the transformative capabilities of AI and Machine Learning, she is cautious about explainability and the audit-ability of black box AI models. She states the need for “guard-rails” to contain AI risk, while observing safety and soundness and consumer financial protection.
In her address entitled “What Are We Learning about Artificial Intelligence in Financial Services?”, she told delegates she is optimistic about the potential for AI and machine learning in particular, but guarded on how new machine learning models can be audited.
Dr. Brainard’s well informed speech begins, “Modern machine learning applies and refines, or “trains,” a series of algorithms on a large data set by optimizing iteratively as it learns in order to identify patterns and make predictions for new data. Machine learning essentially imposes much less structure on how data is interpreted compared to conventional approaches in which programmers impose ex ante rule sets to make decisions.”
She accurately states the value of machine learning when applied to banking AML and loan processing; here are quotes from her remarks:
1.”Firms view AI approaches as potentially having superior ability for pattern recognition, such as identifying relationships among variables that are not intuitive or not revealed by more traditional modeling.
2. Firms see potential cost efficiencies where AI approaches may be able to arrive at outcomes more cheaply with no reduction in performance.
3. AI approaches might have greater accuracy in processing because of their greater automation compared to approaches that have more human input and higher “operator error.”
4. Firms may see better predictive power with AI compared to more traditional approaches–for instance, in improving investment performance or expanding credit access.
5. AI approaches are better than conventional approaches at accommodating very large and less-structured data sets and processing those data more efficiently and effectively.”
A Word of Caution
Dr. Brainard continues, ‘The question is how should we approach regulation and supervision? It is incumbent on regulators to review the potential consequences of AI, including the possible risks, and take a balanced view about its use by supervised firms.Regulation and supervision need to be thoughtfully designed so that they ensure risks are appropriately mitigated but do not stand in the way of responsible innovations that might expand access and convenience for consumers and small businesses or bring greater efficiency, risk detection, and accuracy.’ ”
Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group