GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Goals and Workload Assumptions
Agency: Federal Deposit Insurance Corporation - Division of Finance
Problem addressed: Summarize divisional efforts into a narrative taking into consideration staff numbers and competencies. The AI solution is intended to increase employee awareness of efforts and provide focus for what specialty a given employee may be interested in. This helps resolve and prevent internal communication problems.
Purpose and benefits: Reduced time to receive answers based on data. This internal solution improves operational efficiency of the agency.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: CFOO Admin Chatbot
Agency: Federal Deposit Insurance Corporation - Division of Finance
Problem addressed: Compiling data from internal FDIC website and targeted documents to answer questions about CFOO admin. The AI solution is intended to increase user access to already available data in a more digestible form. This helps internal employees self solve problems quickly when engaging with administrative issues.
Purpose and benefits: Makes information more readily available , searchable and readable. This internal solution improves operational efficiency of the agency.
Topic area: Admin Functions
Development stage: Deployed
First used: 2023-01-01
US federal AI use case: CFOO Travel Chatbot
Agency: Federal Deposit Insurance Corporation - Division of Finance
Problem addressed: Compiling data from internal FDIC website and targeted documents to answer questions about FDIC travel. The AI solution is intended to increase user access to already available data in a more digestible form. This helps internal employees self solve problems quickly when engaging with travel issues.
Purpose and benefits: Makes information more readily available, searchable and readable. This internal solution improves operational efficiency of the agency.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2020-09-01
US federal AI use case: CFOO Transactional Data Analysis
Agency: Federal Deposit Insurance Corporation - Division of Finance
Problem addressed: AI assistance in monitoring financials/invoices for proper submittal, duplicate payments, and other abnormalities. The AI solution is intended to be an automated assist for auditing and monitoring efforts. This helps ensure compliance and reduces corporate risk.
Purpose and benefits: Reduced time to catch errors, perform reviews, make data visualizations available. This helps protect the agency from the financial errors listed.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Knowledge Article Generation
Agency: Federal Deposit Insurance Corporation - Chief Information Officer Organization
Problem addressed: Increase in self-service available answers and a reduction in issue resolution times through improved quantity and quality of information available to provide answers to previously solved problems.
Purpose and benefits: Decreased effort to produce and validate knowledge articles, which will result in an increase in self-service available answers and a reduction in issue resolution times through improved quantity and quality of information available to provide answers to previously solved problems.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: FDIC Staffing Model
Agency: Federal Deposit Insurance Corporation - Division of Finance
Problem addressed: Monitor retention rate, retirement planning, and knowledge retention and succession. The AI solution is intended to be an automated assist for auditing and monitoring efforts. This helps reduce corporate risk and works toward solving problems of succession management.
Purpose and benefits: Reduced time to receive answers, allowing folks to focus on more critical mission-driven work.
Topic area: HR
Development stage: Pre-deployment
US federal AI use case: FDIC Financial Audit Report
Agency: Federal Deposit Insurance Corporation - Division of Finance
Problem addressed: Monitor employee transactions ensure regulations and policies are being followed.  The AI solution is intended to be an automated assist for auditing and monitoring efforts. This helps ensure compliance and reduces corporate risk.
Purpose and benefits: Reduced time to receive answers, allowing folks to focus on more critical mission-driven work. This helps the agency meet mandated reporting requirements.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Risk Identification and Response
Agency: Federal Deposit Insurance Corporation - Division of Finance
Problem addressed: Apply a risk model to identified risks and incorporate additional data sets to identify any correlations or possible solutions. The AI solution is intended to reduce time and pain points of processing or reading through large amounts of disparate datasets or narratives.
Purpose and benefits: Reduce risk to corporation by being able to more quickly and accurately analyze and report out on data.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: FDIC Investment Portfolio Management
Agency: Federal Deposit Insurance Corporation - Division of Finance
Problem addressed: Analyze investment history and provide an additional predictive analysis for investments. The AI solution is intended to decrease time to produce results via automation to reduce risk of human error.
Purpose and benefits: Reduced time to receive answers, allowing folks to focus on more critical mission-driven work, act as an additional investment analysis review for staff to compare with. This helps protect the DIF.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Analytics on Loan Data
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Development stage: Retired
US federal AI use case: Analytics on Securities Data
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Development stage: Retired
US federal AI use case: Business Data Extraction from Loan Files
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Development stage: Retired
US federal AI use case: Data Extraction from Security Prospectus
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Development stage: Retired
US federal AI use case: Initial Deposit File Analysis
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Development stage: Retired
US federal AI use case: Owned Real Estate (ORE) Property Appraisal
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Development stage: Retired
US federal AI use case: Trust Document Entity Extraction
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Problem addressed: Currently, claims specialists spend countless hours manually reviewing trust agreements to extract grantor/settlor and beneficiary information. Implementing OCR and Llama to automate key data extraction from these agreements will significantly make the process faster, more efficient, and more accurate while eliminating the potential for manual errors.
Purpose and benefits: This will reduce the time and cost to transpose information from Trust Agreements.
Topic area: Other
Development stage: Pilot
First used: 2024-09-01
US federal AI use case: Trust Document Entity Extraction - LLM
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Problem addressed: The current trust document review process is labor intensive and may require further reviews by legal experts, hence, prolonging the processing time. By leveraging the use of a Large Language Model to automate the extraction of key information from trust agreements to match and verify customer form input, this will lead to a faster, accurate and efficient workflow for claims specialists.
Purpose and benefits: The current trust document review process is labor intensive and may require further reviews by legal experts, hence, prolonging the processing time.  Using a LLM to automate the extraction of data will reduce manual effort and improve the quality of data extracted from trust documents.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Extracting IT Information
Agency: Federal Deposit Insurance Corporation - Division of Risk Management Supervision
Problem addressed: Searching for information using AI rather than manual reviews of pdf documents or using the RADD search function. AI simplifies the search efforts with a more robust search engine and provide better potential hits of desired information much faster than the current methods.
Purpose and benefits: Algorithm, Language Processing Horizontal Analysis - Risk Examination (AlphaREx) analyzes unstructured narrative content in Information Technology (IT) examination workpapers and Reports of Examination (ROE) to provide meaningful, actionable insights into information. AlphaREx capabilities contribute to the FDIC’s Modernization initiative for the Supervision Modernization Business Drivers and supports the mission of the RMS Information Technology Section.  Additionally, AlphaREx was developed to enable the automation, ingestion, analysis, and visualization of the information technology and operational unstructured and structured data gathered per the supervised financial institution.
Topic area: IT
Development stage: Deployed
First used: 2018-01-01
US federal AI use case: Transcribing Structured Interviews
Agency: Federal Deposit Insurance Corporation - Division of Risk Management Supervision
Development stage: Retired
US federal AI use case: New Offsite Models
Agency: Federal Deposit Insurance Corporation - Division of Risk Management Supervision
Problem addressed: This machine learning (ML) model is intended to identify banks most likely to be downgraded at or before the next examination.
Purpose and benefits: An improved methodology of identifying potential downgrades for use in our quarterly offsite review program.  This provides information on potentially deteriorating financial conditions in the better rated banks across the country.
Topic area: IT
Development stage: Pilot
First used: 2018-01-01
US federal AI use case: Corporate Impact Analysis Object
Agency: Federal Deposit Insurance Corporation - Office of Legislative Affairs
Problem addressed: The Office of Legislative Affairs is the Corporation’s congressional liaison that closely monitors and responds to legislation important to the FDIC. In doing so, the Office of Legislative Affairs is responsible for identifying legislation in a timely manner and working with the Legal Division to complete a legislative analysis summarizing the impact of the legislation relative to the FDIC, insured depository institutions, and consumers (at a minimum). The Corporate Impact Analysis Object use case will utilize previously generated legislative analysis for both recently introduced legislation in the last six years, as well as full public law analyses since 1993 to source draft impact statements of bills.  The quicker turnaround of draft analyses will address the significant reduction in attorneys available to draft this work product.
Purpose and benefits: Currently, the Office of Legislative Affairs (OLA) spends time reviewing legislative analyses generated by the Legal Division.  The AI solution is intended to enhance this process by decreasing both the time necessary to compile an initial review, as well as allowing for more focus to be placed on the fundamental impacts and thereby leading to productivity gains.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: OIG computer forensics investigation tools.
Agency: Federal Deposit Insurance Corporation - Office of Inspector General
Problem addressed: Access and store information from electronic devices in support of criminal investigations.
Purpose and benefits: Assists in law enforcement investigations.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2026-01-03
US federal AI use case: Immersive Learning
Agency: Federal Deposit Insurance Corporation - Corporate University
Problem addressed: This training tool will allow FDIC users to practice what they have been trained on. This tool provides an interactive learning method for participants to engage in low-stakes practice. Users can apply different outcomes to determine best methods.
Purpose and benefits: Immersive learning offers an engaging approach to training programs by creating realistic environments using GenAI where learners can practice and refine their skills. A controlled environment wherein learners interact with immersive technology to solve pre-determined complex situations and problems. This approach amplifies employee engagement and retention, performance management, learning and development, administrative tasks, workforce planning, employee onboarding, recruiting, interviewing as well as others.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: FDIC Deposit Insurance Misrepresentation
Agency: Federal Deposit Insurance Corporation - Division of Depositor and Consumer Protection
Problem addressed: AI can help the FDIC identify instances of misrepresentation of FDIC Deposit Insurance.
Purpose and benefits: Natural language processing  can search media reports and postings to identify posts with heightened risk of misrepresentation for additional review.
Topic area: Other
Development stage: Pilot
First used: 2025-01-01
US federal AI use case: Financial Well-being Project
Agency: Federal Deposit Insurance Corporation - Division of Insurance and Research
Development stage: Retired
US federal AI use case: Enterprise FDIC Chat
Agency: Federal Deposit Insurance Corporation - Chief Information Officer Organization
Problem addressed: Human capital resources are used on reading and summarizing content, research, generating draft documents, generating briefings, and searching for agency information.
Purpose and benefits: Improvements in speed to skillset and business productivity for employees by introducing a secure, agency compliant Chat that can provide AI-assisted Q&A, content summarization, and basic semantic search.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Criminal Background Investigation
Agency: Federal Deposit Insurance Corporation - Division of Administration and Office of Inspector General
Problem addressed: By automating the verification process for conducting background screenings, the tool helps FDIC make faster and more informed hiring and investigatory decisions.
Purpose and benefits: Improving the time in completing background screening.  Reduce inaccurate and/or incomplete information on individuals.  Reduce the manual verification process, which can be cumbersome and place a heavy administration burden on HR staff. Protects FDIC insured institutions from high risk individuals.
Topic area: HR
Development stage: Deployed
First used: 2026-01-03
US federal AI use case: Financial Background Investigation
Agency: Federal Deposit Insurance Corporation - Division of Administration
Problem addressed: By automating the verification process for conducting background screenings, the tool helps FDIC make faster and more informed hiring and investigatory decisions.
Purpose and benefits: Improving the time in completing background screening.  Reduce inaccurate and/or incomplete information on individuals.  Reduce the manual verification process, which can be cumbersome and place a heavy administration burden on HR staff.
Topic area: HR
Development stage: Deployed
US federal AI use case: Restitution Order AI Assistance
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Problem addressed: The current process for managing restitution order data lacks efficiency, making it difficult to effectively identify, track, classify, and resolve these orders. This hinders the FDIC's ability to maximize recoveries from failed financial institutions. Implementing a strategic solution to enhance the identification, tracking, classification, and resolution of restitution order data will improve efficiency and ultimately support the FDIC's mission to maximize recoveries from failed financial institutions.
Purpose and benefits: This is a strategic solution to enhance the identification, tracking, classification, and resolution of restitution order workload, ultimately supporting the FDIC’s mission to maximize recoveries from failed financial institutions The outputs will include restitution order dataset, case resolution report, consolidated recovery statement, loss estimation and asset tracking metrics.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Provisional Holds Model
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Problem addressed: This solution will enable the FDIC to proactively manage financial risk by providing the ability to estimate potential overpayments, even without real-time account-level data. The strategic insights gained will allow for the optimization of financial holds, minimizing both overpayment risk and operational inefficiencies.
Purpose and benefits: The model supports the provisional holds process by estimating potential overpayment. Appropriate provisional holds allow depositors access to their funds in a timely manner, while controlling risks to the DIF from overpaying uninsured depositors during a bank failure.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Background Check Web Application
Agency: Federal Deposit Insurance Corporation - Division of Resolutions and Receiverships
Problem addressed: FDIC can only maintain contact details of individuals or entities if they are self reported by them. The CLEAR service provides potentially the most current contact and entity information for Individuals or Companies who may be holding monetary assets or bonds in failed institutions and may need to be reimbursed.
Purpose and benefits: Expected benefits are the most current contact information for individuals/companies including any related subsidiary entities.
Topic area: Other
Development stage: Deployed
First used: 2011-01-01
US federal AI use case: Blockchain and Cryptocurrency Transaction Identification and Visualization
Agency: Federal Deposit Insurance Corporation - Legal Division and Office of Inspector General
Problem addressed: Tool will improve ability to identify and track the flow of transactions executed on the blockchain.
Purpose and benefits: Making blockchain-based transactions more transparent and creating visualizations to facilitate understanding transaction flow, which supports identification of criminal activity by following the money through mixers, swaps and smart contracts.
Topic area: Other
Development stage: Deployed
First used: 2026-01-17
US federal AI use case: Location and Evaluation of Individuals and Assets for Collectability of Civil Claim
Agency: Federal Deposit Insurance Corporation - Legal Division
Problem addressed: Obtaining information regarding the location of individuals as is needed to pursue civil claims is a cumbersome process requiring many manual hours to search various resources available physically and electronically.
Purpose and benefits: Access to a centralized online investigative database that aggregates public records expedites an evaluation of the personal assets of an individual and an assessment as the likely collectability of damages under a potential civil claim or of an aged, unpaid civil settlement or  federal restitution orders.
Topic area: Other
Development stage: Deployed
First used: 2011-01-01
US federal AI use case: Digital Asset Liquidity Risk Analysis
Agency: Federal Deposit Insurance Corporation - Division of Risk Management Supervision
Problem addressed: Chainalysis traces digital asset funds and provides identities/wallets/flows of digital assets among wallets on public blockchains.
Purpose and benefits: We use Chainalysis for liquidity flow analysis (flow of digital asset funds) to inform policymaking efforts related to stablecoins and other digital assets, operational risk analysis, and analysis of concentration of digital asset activities and entities.
Topic area: IT
Development stage: Deployed
First used: 2024-01-01
US federal AI use case: Social Listening
Agency: Federal Deposit Insurance Corporation - Office of Communications, Division of Complex Institution Supervision & Resolution, Legal Division, Division of Risk Management Supervision
Problem addressed: There are many social media sites that provide information useful to the FDIC's fulfillment of its mission; searching each independently is time consuming and inefficient. Summary of results for social and news items on the FDIC, liquidity risks, banking industry, and other relevant topics helps users to support mission.
Purpose and benefits: Access to a centralized online tool that searches numerous social media sites expedites analysis of publicly available information. Utilized for social listening about policy issues, potential liquidity risks, banks,  and more to help support mission.
Topic area: Other
Development stage: Deployed
First used: 2023-01-01
US federal AI use case: Background Check Tool for Fraud Investigations
Agency: Federal Deposit Insurance Corporation - Division of Risk Management Supervision
Problem addressed: CLEAR is used to assist with fraud investigations.  It searches and retrieves publicly available data on individuals and businesses.
Purpose and benefits: Rapidly obtaining information on individuals and businesses from publicly available data sources.
Topic area: IT
Development stage: Deployed
First used: 2025-01-01
US federal AI use case: Data Extraction from Contract PDF Files
Agency: Federal Deposit Insurance Corporation - Division of Administration
Problem addressed: Reduce the time to extract specific information from electronic forms, saving time and reducing errors from this manual process.
Purpose and benefits: The AI model will extract specific information (dates) from an electronic form. This will turn a manual process into an automated one saving worker time and increasing accuracy.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Summarization & Policy Analysis for Regulatory Comments (originally Leverage AI in the Rulemaking Process Use Case)
Agency: Federal Energy Regulatory Commission - FERC
Problem addressed: Addresses duration, efficiencies, and accuracies in initial comment analysis.
Purpose and benefits: Reduces the time required for comment analysis as part of mission processes.
Topic area: Energy and Environment
Development stage: Deployed
Impact assessment: SPARC does not make decisions or take actions that directly affect individual rights, benefits, or services. It supports internal analysis of public comments and does not operate autonomously or outside human oversight. Its outputs are used by analysts to inform summaries and sentiment trends, not to drive determinations or enforcement.
First used: 2025-10-15
US federal AI use case: Improve Safety Inspections Use Case
Agency: Federal Energy Regulatory Commission - FERC
Problem addressed: The AI solution is intended to enhance the efficiency and accuracy of dam and LNG pipeline structure inspections by FERCs Dam inspectors. It will analyze historical and real-time images, videos, and notes to identify potential safety defects and trends. This approach reduces the manual workload of safety inspectors and improve inspection timelines.
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: This use case is in pre-deployment and initial analysis determines that it will not make decisions or take actions that directly affect individual rights, benefits, or services. Reassessment will be conducted upon development.
US federal AI use case: Enhance Market Surveillance and Fraud Detection Use Case
Agency: Federal Energy Regulatory Commission - FERC
Problem addressed: The AI solution aims to assist staff in analyzing large and complex market datasets for market integrity and compliance purposes. It will process and interpret vast amounts of trading data at high speed, identifying anomalies, trends, and potential regulatory violations. This capability will help maintain fair and transparent markets, enabling staff to focus on strategic decision-making. The AI’s efficiency and precision will save time, improve compliance, and uphold public trust in market operations.
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: This use case is in pre-deployment and initial analysis determines that it will not make decisions or take actions that directly affect individual rights, benefits, or services. Reassessment will be conducted upon development.
US federal AI use case: Support Interconnection Request Responses Use Case
Agency: Federal Energy Regulatory Commission - FERC
Problem addressed: The AI solution will be designed to address the growing backlog of project interconnection requests and reduce the average three-year wait time. By accelerating the processing of proposals, the AI will help FERC with cost savings and shorting the timelines of requests.
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: This use case is in pre-deployment and initial analysis determines that it will not make decisions or take actions that directly affect individual rights, benefits, or services. Reassessment will be conducted upon development.
US federal AI use case: Gas Blanket Certificates (Permitting)
Agency: Federal Energy Regulatory Commission - FERC
Problem addressed: It is designed to streamline the classification, tracking, and summarization of filings and issuances related to blanket certificates, reducing manual workload and improving consistency across document types.
Purpose and benefits: Improves operational efficiency by automating repetitive tasks, enhances transparency in regulatory processes, and accelerates access to structured data for both internal analysts and stakeholders from the analyst's responses.
Topic area: Energy and Environment
Development stage: Pilot
Impact assessment: Blanket Certificate use case automate administrative tracking and reporting functions without making determinations that affect individual rights, benefits, or services. The system operates under human oversight and does not independently initiate regulatory actions or enforcement. Its outputs are used to support internal reporting and compliance reviews, not to drive decisions with legal or civil implications.
First used: 2025-11-24
US federal AI use case: AI Enabled Assistant Legal Research
Agency: Federal Energy Regulatory Commission - FERC
Problem addressed: Addresses streamlining legal workflows by automating research, document analysis, and drafting. It addresses inefficiencies in legal operations and reduces the time required for complex legal tasks.
Purpose and benefits: Enhances productivity, improves accuracy in legal analysis, and supports faster decision-making. It enables legal professionals to focus on high-value strategic work, improves client service, and reduces operational costs.
Topic area: Other
Development stage: Pre-deployment
First used: 2025-07-30
US federal AI use case: LexisNexis
Agency: Federal Housing Finance Agency - OGC
Problem addressed: Generative AI-assisted legal research tool
Purpose and benefits: Efficiency
Topic area: Admin Functions
Development stage: Deployed
First used: 2023-10-01
US federal AI use case: Security and network monitoring using Cisco Identify Services engine
Agency: Federal Housing Finance Agency - OCIO
Problem addressed: Improves security visibility, segmentation, and threat response by analyzing network traffic and user behavior in real-time, enabling automatic detection, isolation, and resolution of potential security risks.
Purpose and benefits: Risk Reduction
Topic area: Cybersecurity
Development stage: Deployed
First used: 2024-07-01
US federal AI use case: Virtual Desktop using Citrix
Agency: Federal Housing Finance Agency - OCIO
Problem addressed: Citrix uses AI to optimize virtual desktops, manage workloads, and enhance security with behavioral analysis and threat detection.
Purpose and benefits: Cost Savings, Efficiency, and Risk Reduction
Topic area: IT
Development stage: Deployed
First used: 2024-12-01
US federal AI use case: Analytics, Indexing, and Anomaly Detection for Data Management on SQL Server
Agency: Federal Housing Finance Agency - OCIO
Problem addressed: Predictive analytics, automated indexing, and anomaly detection in data management and queries.
Purpose and benefits: Efficiency and Risk Reduction
Topic area: IT
Development stage: Deployed
First used: 2021-11-01
US federal AI use case: Workflow automation and predictive analytics using ServiceNow
Agency: Federal Housing Finance Agency - OCIO
Problem addressed: Help to automate workflows, predictive analytics, and providing intelligent service management solutions.
Purpose and benefits: Efficiency
Topic area: IT
Development stage: Deployed
First used: 2023-03-01
US federal AI use case: Security and Compliance in file sharing and collaboration using Kiteworks
Agency: Federal Housing Finance Agency - OCIO
Problem addressed: Enhances security and compliance in file sharing and collaboration by detecting anomalies and potential threats in real-time."
Purpose and benefits: Risk Reduction
Topic area: Cybersecurity
Development stage: Deployed
First used: 2024-10-01
US federal AI use case: Network monitoring, anomaly detection through Whats Up Gold and Flowmon integration
Agency: Federal Housing Finance Agency - OCIO
Problem addressed: Uses AI for network visibility, anomaly detection, and security through Flowmon integration
Purpose and benefits: Risk Reduction
Topic area: IT
Development stage: Deployed
First used: 2022-10-01
US federal AI use case: Mathematical formula transcription using Commonlook Online
Agency: Federal Housing Finance Agency - OCIO
Problem addressed: Commonlook Online is used to translate complex mathematical formula into plain language for publicly released documents.
Purpose and benefits: Efficiency
Topic area: Admin Functions
Development stage: Deployed
First used: 2024-10-01
US federal AI use case: Neural Networks for FHFA Modeling Analytics Platform (FMAP)
Agency: Federal Housing Finance Agency - DHMG
Problem addressed: Help researchers optimize the framework and specifications of the production of the Single-Family FHFA Modeling Analytics Platform (FMAP).
Purpose and benefits: Efficiency
Topic area: IT
Development stage: Deployed
First used: 2024-01-05
US federal AI use case: Phishing Email Identification
Agency: Federal Housing Finance Agency - OCOO
Problem addressed: Knowbe4 PhishER is a tool to report and manage suspicious emails. This tool use ML and AI to analyze suspicious emails to identify if the email is spam, phishing, or malicious. The primary benefit of this tool is to automate responses without requiring human intervention to analyze each email that end users identify as suspicious.
Purpose and benefits: Risk Reduction
Topic area: Cybersecurity
Development stage: Deployed
First used: 2021-04-01
US federal AI use case: WestLaw
Agency: Federal Housing Finance Agency - OGC
Problem addressed: Generative AI-assisted legal research tool
Purpose and benefits: Efficiency
Topic area: Admin Functions
Development stage: Deployed
First used: 2018-07-01
US federal AI use case: Public Comments Summarization and Topic Classifications Pilot Project
Agency: Federal Housing Finance Agency - DBR
Development stage: Retired
US federal AI use case: Virtual Acquisition Office (VAO) Ally
Agency: Federal Housing Finance Agency - OCFO
Problem addressed: VAO Ally is part of the overall VAO subscription. VAO Ally reduces the amount of time necessary to research acquisition-related policy/guidance questions and scenarios by parsing through multiple resources and providing responses/answers based on a user’s question searched.
Purpose and benefits: Efficiency
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2024-07-01
US federal AI use case: Analytics,, Search, Queries and LLM integration for data management on Oracle
Agency: Federal Housing Finance Agency - OCIO
Development stage: Retired
US federal AI use case: Python modules to support AI/ML
Agency: Federal Housing Finance Agency - OCIO
Development stage: Retired
US federal AI use case: R modules to support AI/ML
Agency: Federal Housing Finance Agency - OCIO
Development stage: Retired
US federal AI use case: Economic Trend Modeling
Agency: Federal Reserve Board - Division of Monetary Affairs
Problem addressed: Enhance economic forecasting accuracy beyond traditional statistical models to better predict economic events
Purpose and benefits: Additional data beyond traditional statistical models
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2019-09-23
US federal AI use case: PDF Optical Character Recognition (Text)
Agency: Federal Reserve Board - Division of Information Technology
Problem addressed: Automate extraction of structured column data from unstructured PDF reports to reduce manual processing time and errors
Purpose and benefits: The intended purpose is to extract and to decide the correct column name extracted from a report in PDF format
Topic area: IT
Development stage: Deployed
First used: 2024-09-26
US federal AI use case: PDF Optical Character Recognition (Images)
Agency: Federal Reserve Board - Division of Information Technology
Problem addressed: PDF OCR (Images) – Extract textual information from images embedded within PDF documents for comprehensive document analysis
Purpose and benefits: The intended purpose is to extract text from the image embedded in PDF file
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Stock Market Analysis
Agency: Federal Reserve Board - Division of Research and Statistics
Problem addressed: Quantify relationships between news media narratives and stock market volatility to improve market event understanding
Purpose and benefits: To understand media narratives associated with stock market events
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Commercial Real Estate Index
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Synthesize commercial real estate data points into a comprehensive market index for improved market monitoring
Purpose and benefits: Identify principal component using various data points to create a market index for commercial real estate
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2023-10-01
US federal AI use case: Variable Optimization
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Determine optimal lag structures to enhance accuracy for forecasting call report metrics
Purpose and benefits: Iterate through alternative lag structures to identify the optimal one for forecasting call report metrics
Topic area: Other – Economic & Financial
Development stage: Pilot
First used: 2023-09-28
US federal AI use case: Credit Fragment Analysis
Agency: Federal Reserve Board - Division of Research and Statistics
Problem addressed: Identify meaningful patterns in fragmented data to support more comprehensive research analysis
Purpose and benefits: Research on credit fragments
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Proposals and Public Comments
Agency: Federal Reserve Board - Office of the Secretary
Problem addressed: Efficiently process large volumes of public regulatory comments while ensuring proper handling of sensitive information
Purpose and benefits: The Board of Governors of the Federal Reserve System (Board) is developing the Proposals and Public Comments (PPC) system to electronically process and manage comments from the public on regulatory rulemakings, information collections, and other proposals (collectively, proposals) and to post those comments to the Board’s public website. The first release of PPC is expected in mid-November 2024. The Board’s processing of comments may use artificial intelligence (AI) to provide more efficient processing of public comments (e.g., PII redaction recommendations, spam detection).
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2024-11-16
US federal AI use case: Manufacturer Sentiment Analysis
Agency: Federal Reserve Board - Division of Research and Statistics
Problem addressed: Convert qualitative survey responses into quantitative insights on industrial production forecasts
Purpose and benefits: To quantify survey responses related to forecasts of industrial production
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2022-07-01
US federal AI use case: Supply Chain Estimations
Agency: Federal Reserve Board - Division of Research and Statistics
Problem addressed: Identify and measure supply chain constraints that could impact broader economic performance
Purpose and benefits: To estimate supply chain bottlenecks
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2022-12-01
US federal AI use case: Body Worn Cameras Data Management System
Agency: Federal Reserve Board - Office of the Inspector General
Problem addressed: Body Worn Cameras Data Management – Ensure proper handling of information in body camera footage while making content searchable
Purpose and benefits: Audio recordings may be transcribed to text and/or redacted. Transcripts are
then labeled “unverified” until an OIG designated member reviews, edits and approves the final transcript.

Automatically detects and redacts screens (computer screens, digital
signs), faces and license plates captured. Prior to redacting any evidence, a special agent must first approve the redactions before sharing any evidence.
Topic area: Law Enforcement
Development stage: Pilot
First used: 2024-07-01
US federal AI use case: Market Fund Portfolio
Agency: Federal Reserve Board - Division of Research and Statistics
Problem addressed: Accurately identify security issuers in money market fund portfolios
Purpose and benefits: To assist with issuer classification of money market funds
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2021-05-01
US federal AI use case: Sentiment Analysis of Earnings Transcripts
Agency: Federal Reserve Board - Division of Monetary Affairs
Problem addressed: Measure and classify sentiment in bank earnings calls to identify emerging trends
Purpose and benefits: Additional insight into sentiments related to topics in bank earnings calls
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2024-10-01
US federal AI use case: Anomaly Detection
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Identifying irregular patterns in firm-submitted information based on historical submission patterns
Purpose and benefits: Improve the iterative data quality process
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2022-12-01
US federal AI use case: Consumer Complaints Explorer
Agency: Federal Reserve Board - Division of Consumer and Community Affairs
Problem addressed: Categorize large volumes of consumer complaints into topics to facilitate appropriate analysis and response
Purpose and benefits: Improve the classification of consumer complaints into topics using topic modeling
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2019-01-01
US federal AI use case: Regulatory Data Analysis
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Identify potential reporting anomalies by comparing current submitted values against expected ranges based on historical patterns
Purpose and benefits: Improved data quality for stakeholders
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2024-09-09
US federal AI use case: Decision Tree for Deposits Data
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Detect outliers in current reporting data to enhance overall data quality and reliability
Purpose and benefits: Improved process efficiency and data quality.
Topic area: Other – Economic & Financial
Development stage: Pilot
First used: 2024-08-29
US federal AI use case: Novel Activities Call Report Classification
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Identify emerging or non-traditional banking activities
Purpose and benefits: The random forest model would parse out what reported categories most significantly predict inclusion on novel banking activity lists
Topic area: Other – Economic & Financial
Development stage: Pilot
First used: 2024-07-01
US federal AI use case: Financial News Processing
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Transform large volumes of unstructured financial news into structured, actionable insights
Purpose and benefits: Assist with financial news processing
Topic area: Other – Economic & Financial
Development stage: Pilot
First used: 2024-09-01
US federal AI use case: Bank Exam Quality Control – Model 1
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Enhance quality control by incorporating external news data into performance assessment models
Purpose and benefits: A traditional machine learning NLP model of bank performance that utilizes information in news articles among various inputs
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Document Summarization Statistics
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Extract meaningful metrics and term frequencies from lengthy documents to identify trends and common issues
Purpose and benefits: Provide statistics about bank review letters based on the length of the letters and the frequency of common financial terms
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2024-12-22
US federal AI use case: Writing Quality Analysis Model
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Ensure consistent, high-quality writing across organizational documents through automated style and quality assessment
Purpose and benefits: A NLP model to help leaders better understand the writing quality and consistency of documents
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Comment Review System
Agency: Federal Reserve Board - Division of Information Technology
Problem addressed: Manage, analyze, and categorize large volumes of public comments
Purpose and benefits: The Comment Review System (CRS) is a system used by the Board of Governors of the Federal Reserve System (“Board”) to electronically process and manage comments from the public on regulatory rulemakings, information collections, and other proposals (collectively, “proposals”). The Board’s processing of comments may use artificial intelligence (AI) to provide more efficient processing of public comments (e.g., text matching, entity identification, and text similarity matching).​
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2021-07-01
US federal AI use case: Trading Desk Grouping
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Categorize wide-ranging trading desk operations by asset class
Purpose and benefits: Grouping Trading Desk Descriptions
Topic area: Other – Economic & Financial
Development stage: Pilot
First used: 2024-04-01
US federal AI use case: Threshold Monitoring
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Timely identification of threshold limits
Purpose and benefits: Enhance monitoring on limits thresholds, faster collaboration
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Supply and Demand Tool
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Optimize resource allocation based on defined rules to better balance supply and demand constraints
Purpose and benefits: The tool is used to monitor and project resource and supply demand​ based on defined rule sets
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Outlier Detection
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Efficiently process large volumes of firm data to identify anomalies and extract meaningful insights
Purpose and benefits: Traditional AI to help identify, synthesize, and deliver information provided by firms.
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Earnings Call Topic Model
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Systematically categorize earnings call content to enable more efficient and comprehensive analysis
Purpose and benefits: Model used to classify topics from earnings calls
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Bank Performance Monitoring
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Enhance capabilities through econometric modeling based on historical performance data
Purpose and benefits: Model used to support off site risk analysis
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Bank Exam Quality Control – Model 2
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Incorporating external news data into modeling
Purpose and benefits: A traditional machine learning NLP model of bank ratings that utilizes information in news articles among various inputs
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Risk Rating Model – Community Banks
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Develop more accurate classification framework for community banks to appropriately tailor strategies
Purpose and benefits: To improve upon the classification framework used to tier community banks according to risk, and to tailor examination intensity
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2023-10-30
US federal AI use case: Risk Rating Model
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Improve risk models optimize resource allocation
Purpose and benefits: Identify a set of factors that may be helpful in predicting  the likelihood that a bank will experience an adverse outcome in the future, and to tier banks into High-, Moderate- and Low-risk tiers for review
Topic area: Other – Economic & Financial
Development stage: Pilot
First used: 2019-10-02
US federal AI use case: Financial System Data Analysis
Agency: Federal Reserve Board - Division of Financial Stability
Problem addressed: Model counterparty exposures across multiple institutions.
Purpose and benefits: Enhance the ability to monitor trends in the financial system.
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Bank Examiner Search Engine
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Saving examiners time by providing requested information needed during bank examinations
Purpose and benefits: Provide efficiencies for bank examiners during examinations by helping to provide information faster and at greater scale
Topic area: Other – Economic & Financial
Development stage: Deployed
First used: 2025-06-03
US federal AI use case: Oasis Semantic Search
Agency: Federal Reserve Board - Division of Supervision and Regulation
Problem addressed: Manually searching for documents is a significant task for each business use case
Purpose and benefits: Reduce search time for each business use case
Topic area: Other – Economic & Financial
Development stage: Pre-deployment