The digitization of financial services is generating unprecedented volumes of data – so much that regulators basically have more information than they know what to do with. Artificial intelligence offers a way for them to convert this overload from a problem to a solution. Combining prolific data and AI tools is enabling the emergence of new regulatory methodologies that advance the government’s ability to fight financial crimes, protect consumers, expand financial inclusion, and maintain a safe and sound financial system.
In a new paper for the Brookings Institution, AIR — Alliance for Innovative Regulation CEO Jo Ann Barefoot makes a robust case for AI being at the center of regulators’ innovation strategies. Without honing supervision technology (suptech) techniques offered by machine learning, natural language processing and other AI tools, she argues, government agencies could find themselves sitting on top of mountains of data about global financial transactions, compliance efforts and other developments, but unable to analyze it.
“The potential for peril arises from the concern that the regulators’ current technology framework lacks the capacity to synthesize the data,” Barefoot writes. “The irony is that this flood of information is too much for them to handle. Without digital improvements, the data fuel that financial regulators need to supervise the system will merely make them overheat.”
The paper, which expands on research she conducted while a senior fellow at Harvard’s Kennedy School of Government, lays out five use cases for regulators to place AI at the center of a suptech program: anti-money laundering and sanctions screening; fraud prevention; consumer protection and financial inclusion; credit discrimination and predatory lending; and climate change.
Find the full Brookings Institution paper here.
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