How Board of Governors of the Federal Reserve System uses AI

The Federal Reserve Board runs a central AI Program that collects every AI use case in Board work and in functions delegated to the Reserve Banks, checks each against the Board's AI policy, screens it for high impact characteristics and routes it to the matching governance path. Use cases sit in a common repository that supports reporting and ongoing tracking and is validated periodically. The 2025 public inventory records, per use case, the stage, purpose, vendor, data used, personal data involvement and high impact designation.

No outcome disclosed.

The Federal Reserve Board's Division of Supervision and Regulation uses models developed in house to check the data that reporting firms submit. In its Regulatory Data Analysis use case, in operation since September 2024, analysts receive predicted values at several percentile levels for each reporter to compare with the values it actually reported. A related use case, Decision Tree for Deposits Data (still in implementation and assessment), calculates set variables and filters them to flag potential outliers in the current reporting period. These are supervisor side checks that mirror the validation a bank can run on its own returns before filing. No outcome figures are published.

No outcome disclosed.

The Federal Reserve Board processes public comments on rulemakings, information collections and other proposals in its Comment Review System. The system uses traditional natural language processing for summaries, matching comments to lists of topics, entity identification and similarity matching, and flags duplicate and near duplicate comment letters. The Board states that all public comments are still reviewed in their entirety and that summaries only assist the review. The inventory lists it as deployed since July 2021; no outcome figures are published.

No outcome disclosed.

The Federal Reserve Board's Division of Consumer and Community Affairs has used an in house natural language processing tool since 2019 to sort large volumes of consumer complaint narratives into topics, so staff can analyse and respond to them. For each narrative it outputs a topic number, a fit score and the top five terms of that topic. The input is complaint data from the CFPB. It is a central bank analysing consumer complaints about financial companies from the CFPB database rather than a firm analysing its own complaints, but the method is the same clustering step a bank's root cause work starts from.

No outcome disclosed.