Federal banking agencies are mandated to conduct a comprehensive study on the use of advanced fraud detection technology by insured depository institutions and credit unions. This study, to be performed in consultation with various federal entities like the Treasury and FTC, will evaluate the current use and effectiveness of technologies such as artificial intelligence , machine learning, and predictive analytics. It will also examine the extent to which financial institutions of varying asset sizes deploy these tools, focusing on measurable outcomes related to fraud reduction and consumer protection. A significant component of the study involves assessing community financial institution access to these advanced tools, identifying specific challenges they face in adoption, such as cost and regulatory uncertainty. It will explore options like shared services, utility models, or consortium-based platforms to facilitate access for smaller institutions. The study will also delve into the governance frameworks for AI and machine learning models, considering their interaction with consumer protection laws. Furthermore, the study will investigate the effectiveness of existing information sharing frameworks and the potential for expanded public-private partnerships to enhance detection capabilities. It will address payments system risks, particularly those associated with electronic funds transfers, and analyze regulatory and supervisory considerations that might create barriers to innovation. The agencies must then issue a public report within 18 months, including legislative, regulatory, or supervisory recommendations to support technology adoption, potentially through pilot programs or guidance for responsible AI use. Following the study, the Federal banking agencies may establish a voluntary pilot program within one year to further facilitate community financial institutions' access to advanced fraud detection tools. This program could feature pooled procurement models, technical support for model validation, standardized vendor risk management templates, and regulatory clarity regarding model governance expectations.