By Jacob Bielanski
Federal Reserve Board of Governors member Lael Brainard praised the potential for artificial intelligence (AI) in banking, but warned bankers to be vigilant as they use machine learning to augment, or replace, traditional financial processes. "Firms should not assume that AI approaches are less susceptible to problems because they are purported to be able to ‘learn’ or less prone to human error," Brainard said during a November 13 speech at an event titled "Fintech and the New Financial Landscape," hosted by the Federal Reserve Bank of Philadelphia.
Brainard expressed particular concern over "opacity and explainability" within the AI realm, noting that most AI algorithms are proprietary, and therefore shielded from close scrutiny often required to understand risk. With regards to opacity, Brainard suggested that existing Fed guidance was helpful, noting that the inability for a FRBank supervisor to scrutinize AI algorithms would lead to a recommendation to banks to treat the algorithm with "greater caution or with compensating controls."
"Recognizing there are likely to be circumstances when using an AI tool is beneficial, even though it may be unexplainable or opaque, the AI tool should be subject to appropriate controls, as with any other tool or process, including how the AI tool is used in practice and not just how it is built," she said.
Opaque algorithms not only would be difficult to scrutinize up front, but could leave financial institutions liable with regards to banking regulations if a mistake is made, according to Brainard. Many institutions, she said, would likely turn to external, non-bank vendors to utilize AI innovation. She noted that the use of AI models in credit decisions, for example, might result in unfair lending biases as a result of factors the even the algorithms developers might not easily explain, which could put lenders at odds with the Equal Opportunity Lending and Fair Credit Reporting acts. "Algorithms and models reflect the goals and perspectives of those who develop them as well as the data that trains them," she said. "As a result, AI tools can reflect or ‘learn’ the biases of the society in which they were created."
Decision-making. Despite this, Brainard acknowledged the potential from using AI to speed up decision-making and expand credit access to those who lack a measurable credit history by utilizing "factors beyond the usual metrics." She said she was "pleased" to see that a topic of the event where she was speaking included discussions on how to develop "explainable" AI, targeted at expanding access to consumer credit. "There is substantial interest in the potential for those new models to allow more consumers on the margins of the current credit system to improve their credit standing, at potentially lower cost," Brainard said.
Potential for change. Beyond consumer credit, Brainard listed three additional areas, identified by the Financial Stability Board, where AI offered potential for change in the banking industry. These included improvements to "back office" operations; trading and investment strategies; and compliance and risk mitigation.
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