GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: CFOO Transactional Data Analysis
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Finance
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: AI assistance in monitoring financials/invoices for proper submittal, duplicate payments, and other abnormalities. Reduced time to catch errors, perform reviews, make data visualizations available.
12_outputs: A recommendation on when a gap is identified, why it was identified, and possible causes
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: FDIC Staffing Model
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Finance
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Monitor retention rate, retirement planning, and knowledge retention and succession. Reduced time to receive answers, allowing folks to focus on more critical mission-driven work.
12_outputs: A prediction on future staffing and recommendations on how to meet varied goal points
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: FDIC Financial Audit Report
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Finance
8_topic_area: Mission-Enabling
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Monitor employee transactions ensure regulations and policies are being followed. Reduce time to receive answers, allowing folks to focus on more critical mission-driven work.
12_outputs: An identification of items that all outside policy and a recommendation on if a trend needs to be addressed
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Risk Identification and Response
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Finance
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Apply a risk model to identified risks and incorporate additional data sets to identify any correlations or possible solutions. Reduce risk to corporation by being able to more quickly and accurately analyze and report out on data.
12_outputs: A recommendation of any correlations among risk data and a report including possible solutions
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: FDIC Narrative Reports Generation
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Finance
8_topic_area: Mission-Enabling
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Analyze quarterly narrative/table data points to generate a narrative report for internal use. Reduced time to receive answers, allowing folks to focus on more critical mission-driven work.
12_outputs: A report or table with a summary of data points for the quarter with visualizations
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: FDIC Investment Portfolio Management 
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Finance
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Analyze investment history and provide an additional predictive analysis for investments. 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.
12_outputs: A prediction of the outcome of possible investment choices and a recommendation of which would do best for the current portfolio
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/11/2024
2_use_case_name: Analytics on Loan Data
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Resolutions and Receiverships
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: This reduces the effort to analyze a portfolio of received failed bank loans and provides data supported strategies for disposition of the portfolio.  This reduces the effort to analyze a portfolio of received failed bank loans which is a time- and labor-intensive process, and provides data supported strategies for dispositioning the portfolio.
12_outputs: Formatted data set with elements extracted from unstructured data files/documents into a readily usable data to support disposition portfolio strategies.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 4/25/2023
2_use_case_name: Analytics on Securities Data
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Resolutions and Receiverships
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: This reduces the effort to analyze a portfolio of received failed bank loans and provides data supported strategies for disposition of the portfolio.  This reduces the effort to analyze a portfolio of received failed bank loans which is a time- and labor-intensive process, and provides data supported strategies for dispositioning the portfolio.
12_outputs: Formatted data set with elements extracted from unstructured data files/documents into a readily usable data to support disposition portfolio strategies.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 4/25/2023
2_use_case_name: Business Data Extraction from Loan Files
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Resolutions and Receiverships
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: Format data from unstructured documents to a structured format in a timely fashion and at scale, which will reduce cost and effort. This reduces the effort to analyze a portfolio of received failed bank loans and provides data supported strategies for dispositioning the portfolio.
12_outputs: Formatted data set with elements extracted from unstructured data files/documents.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/26/2022
2_use_case_name: Data Extraction from Security Prospectus
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Resolutions and Receiverships
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: Format data from unstructured documents to a structured format in a timely fashion and at scale, which will reduce cost and effort. This reduces the effort to analyze a portfolio of received failed bank loans and provides data supported strategies for dispositioning the portfolio.
12_outputs: Formatted data set with elements extracted from unstructured data files/documents.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/26/2022
2_use_case_name: Deposit Insurance Determination
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Resolutions and Receiverships
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: Extract and format data from unstructured documents, which will reduce the cost and effort to release or retain provisional holds placed on accounts for banks exceeding $250M . 
12_outputs: Structured data sets that can be analyzed by Claims Specialists.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/26/2022
2_use_case_name: Initial Deposit File Analysis
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Resolutions and Receiverships
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: Evaluate multiple data files in different formats to extract and normalize the inputs into a rationalized data set with the key elements needed for analysis by other functional areas within FDIC.
12_outputs: List of accounts and assets with key data elements needed for insurance determination and asset valuation.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/26/2022
2_use_case_name: Owned Real Estate (ORE) Property Appraisal
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Resolutions and Receiverships
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: Validate and enrich loan data by extracting data from Loan Underwriting Documents, Title Documents, and Property Appraisal Documents, which will reduce the time to value a loan portfolio and improve the quality of the valuation.
12_outputs: List of property records with current and historical appraised values and historical sale transaction details.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/26/2022
2_use_case_name: Trust Document Entity Extraction
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Resolutions and Receiverships
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: Extract key data elements, such as grantor/settler and beneficiaries, from Trust Agreements. This will reduce the time and cost to transpose information from Trust Agreements.
12_outputs: The extracted data, along with the original Trust Agreement, will be presented to users through an application user interface.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/7/2024
19_date_acq_dev_began: 6/10/2024
22_dev_method: Developed in-house.
24_hisp_support: No
31_data_catalog: Other
31_question_type: FDIC's Enterprise Data Catalog is still being implemented
33_agency_data: We used open source, sample trust agreements available on the web, as well as an internal repository of Trust Agreements retained by the FDIC after they were used in earlier insurance determination activities.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
40_has_ato: Yes
41_system_name: Resolution and Receivership Management Portal
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Various intake processes are available depending on the nature and scope of the request
45_compute_request: Other
45_question_type: Various resource request processes are available depending on the nature and scope of the request
47_timely_resources: Other
47_question_type: Communication timeliness depends on the process relevant to the request. Many processes are automated and status updates are push instantaneous or are available on demand. Other manual processes involve the requestor at each step for transparency.
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
2_use_case_name: Trust Document Entity Extraction - LLM
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Resolutions and Receiverships
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: DRR’s objective is to minimize the current processing time for monetary claims related to Trust Accounts following any bank closure. The current process involves claim specialists manually reviewing trust agreements to match and verify customer form inputs. The review process is labor intensive and may require further reviews by legal experts, hence, prolonging the processing time. This use case is to automate this process using a Gen AI powered function that can extract text from Trust Documents to match/validate customer inputs.
12_outputs: The extracted data, along with the original Trust Agreement, will be presented to users through an application user interface.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/7/2024
2_use_case_name: Post Examination Survey Comment Analysis
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Office of the Ombudsman
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: (1) Numerous comments written by different people that are challenging to group in a meaningful way.
(2) Categorize the various comments to enhance analysis and understanding of the asks. 
12_outputs: To be determined, but potentially slotting whole or parts of comments in various categories. Potentially a table with a column of comments and rows of labels. 
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Extracting IT information
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Risk Management Supervision
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: Searching for information using AI.
11_purpose_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.  
12_outputs: Information collected across the IT Profile, FDIC Review of Examinations (ROEs), and multiple other sources (both unstructured/structured data) is a critical business need and is used by FDIC to promptly identify and address IT risks and Cyber risks for the FDIC Insured Deposit Institutions (IDIs).  In addition, this information is used to effectively identify trends, patterns and areas of improvement for the InTREx program; including training and changes to the FDIC IT Examination processes.   
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Transcribing Structured Interviews
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Risk Management Supervision
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Risk Management Supervision (RMS) Atlanta wants to pilot the use of transcription  to record structured interviews. This would allow RMS interview panels to engage with the applicant without needing to focus on taking detailed notes but would allow them to refer back to structured answers as needed. 
Today, panel members must transcribe every word themselves, and must split their attention between the candidate and the transcription. The use of AI is meant to supplement (not replace) human note taking during the interview.
12_outputs: The output will be the  transcription of the audio of the interview.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: New Offsite Models
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Risk Management Supervision
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: This machine learning (ML) model is intended to predict 1- and 2-rated banks most likely to be downgraded to 3-rated or worse. The expected benefit is to improve bank downgrade prediction using a ML technique.
12_outputs: Ranking of banks based on composite rating downgrade probability.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2018
2_use_case_name: Document Indexing
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Risk Management Supervision
8_topic_area: Other
8_question_type: Indexing FDIC Documents
10_commercial_ai: Digitizing text from scanned documents or smart forms for archival purposes using AI.
11_purpose_benefits: The Regional Automated Document  Distribution and Imaging System (RADD) is an electronic document management system that manages all supervisory related correspondence between the FDIC, financial Institutions, and other regulators.  When documents are received from banks, other regulators, and FDIC staff they are manually indexed using a table-driven schema.  The schema includes business line, date, source, document name, and folder.  All indexing, except date, is table driven and every document must be fully indexed.  Currently, RMS has roughly 24 individuals that index RADD documents.  RADD has close to two million documents that are accuracy indexed.  These documents have unique characteristics, such cover sheets, key words, etc.  The hope is we can use AI/ML to replace, or at least supplement, the manual indexing process thus reducing labor costs and improving accuracy.    
12_outputs: The system would output the recommended indexing data for documents received from banks and other agencies
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: eDiscovery Organize & Search
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Legal Division
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Electronically stored information and records have become so voluminous, it is impossible to review and search without automated assistance. 
The Legal Division's eDiscovery vendors use a commercial off the shelf (COTS) software to perform eDiscovery related tasks. 
The software deploys machine learning capabilities to receive, organize, search, and identify electronically stored information in large data sets.
12_outputs: The AI software identifies to the software user electronically stored information (such as documents, emails, text messages, etc. that have been uploaded into the software) likely to be relevant/responsive to user-entered searches. Human users remain responsible for verifying the AI's predictions of what documents are relevant/responsive to searches.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Leverage AI in the Rulemaking Process Use Case 
3_agency: Federal Energy Regulatory Commission
3_abr: FERC
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The AI solution will be designed to streamline the manual review of public feedback, providing insights on sentiment and key themes for FERC’s rulemaking process. Enabling FERC analysts to complete their work more efficiently and accurately saving costs, shorten the regulatory timelines, and ensure timely implementation of policies that benefit the energy industry and the public.
12_outputs: Initial Comment Sentiment Analysis
17_impact_type: Neither
18_date_initiated: 1/16/2024
19_date_acq_dev_began: 1/16/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 89603024F0006
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Comments submitted into the agency via a public facing application will be utilized to train.
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
35_demo_features: Other
35_question_type: No demographic variables will be used.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Microsoft Azure Commerical Environment for prototype.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
2_use_case_name: Improve Safety Inspections Use Case 
3_agency: Federal Energy Regulatory Commission
3_abr: FERC
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: 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. 
12_outputs: Digital Images for Review
17_impact_type: Neither
23_contract_piids: 89603024F0006
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: The use case will build the repository for this use case.
33_agency_data: Images obtained by the AI will build a repository for training.
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
35_demo_features: Other
35_question_type: No demographic variables will be used.
37_custom_code: Yes
40_has_ato: No
43_infra_provisioned: Other
43_question_type: This use case has not been initiated at the time of this reporting. Once initiated, the answer will be yes to the IT infrastructure provisioning.
45_compute_request: Yes
47_timely_resources: Other
47_question_type: This use case has not been initiated at the time of this reporting. Once initiated, the answer will be yes to the IT infrastructure provisioning.
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.