##cen. gov / sites / default / files / shared / fta - check - fraud - final508. pdf. see u. s. department of the treasury, “ treasury announces enhanced fraud detection processes, including machine learning ai, prevented and recovered over $ 4 billion in fiscal year 2024, ” news release, october 17, 2024, https : / / home. treasury. gov / news / press - releases / jy2650. - 6some consumers, like those with poor or no credit history but with sufficient cash flow to support loan repayment. 7 ai could be used to further expand this access, as financial entities mine more data sets and refine their understanding of creditworthiness. of course, we also know that using ai in this context — in a way that has more direct impact on credit decisions affecting individual customers — also presents more substantial legal compliance challenges than other ai use cases. ai also has promise to improve public sector operations, including in regulatory agencies. as i have often noted, the data relied on to inform the federal open market committee decisionmaking process often is subject to revisions after - the - fact, requiring caution when relying on the data to inform monetary policy. 8 perhaps the broader use of ai could act as a check on data reliability, particularly for uncertain or frequently revised economic data, improving the quality of the data that monetary policymakers rely on for decision - making. additional data as a reliability check or expanded data resources informed by ai could improve the fomc ’ s monetary policymaking by validating and improving the data on which policymakers rely. while these use cases present only a subset of the possibilities for the financial system, they illustrate the breadth of potential benefits and risks of adopting an overly cautious approach that chills innovation in the banking system. over - regulation of ai can itself present risks by see board of governors of the federal reserve system, consumer financial protection bureau, federal deposit insurance corporation, national credit union administration, and office of the comptroller of the currency, “ interagency statement on the use of alternative data in credit underwriting, ” news release, december 12, 2019, https : / / www. federalreserve. gov / supervisionreg / caletters / ca % 201911 % 20letter % 20attachement % 20interagency % 20statement % 20on % 20the % 20use % 20of % 20alternative % 20data % 20in

purpose is a definition required? in the context of the financial system, the definition of ai may help to delineate how the regulatory system addresses it and establishes the parameters for how it can be used by regulated institutions. other specific contexts could also be included, like third - party service providers that support banks or other financial services providers, or use by regulators in support of their mandates. a definition helps regulators and regulated institutions understand the activities that are subject to rules and requirements by defining the scope. while this definitional question is important to establish clarity about the scope of what constitutes ai, it can also distract us from a more important point — what is the appropriate policy framework to address the introduction and ongoing use of ai in the financial system? i have no strong feelings about the ideal or optimal definition of ai, and some version of the many definitions floating around are probably adequate for our purposes. at a minimum though, a definition must establish clear parameters about what types of activities and tools are covered. but before leaving the topic, i want to offer a cautionary note. a broad definition of ai arguably captures a wider range of activity and has a longer “ lifespan ” before it becomes outmoded, and potentially never becomes outdated. but a broad definition also carries the risk of a broad — and undifferentiated — policy response. this vast variability in ai ’ s uses defies a simple, granular definition, but also suggests that we cannot adopt a one - size - fits - all approach as we consider the future role of ai in the financial system. - 4innovation and competitiveness knowing that the technology and use of ai continues to evolve leads to the question of how it should be viewed by regulators, particularly in light of the need for innovation and the effect on competition. innovation ai tools have the potential to substantially enhance the financial industry. in my view, the regulatory system should promote these improvements in a way that is consistent with applicable law and appropriate banking practices. one of the most common current use cases is in reviewing and summarizing unstructured data. this can include enlisting ai to summarize a single report or to aggregate information from different sources on the same or related topics. the ai “ output ” in these cases may not directly produce any real - world action, but it provides information in a more usable way to assist a human. ai use cases like this may present opportunities to improve operational efficiency, without introducing substantial new risk into business processes. in