GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Anomaly Detection
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Improve the iterative data quality process
12_outputs: Suggested messages regarding data quality of subsets of firm submitted data based on similar historical messages
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/1/2022
19_date_acq_dev_began: 8/1/2022
20_date_implemented: 12/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Internal- QIS
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.
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Consumer Complaints Explorer
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Consumer and Community Affairs
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Improve the classification of consumer complaints into topics using topic modeling
12_outputs: Gamma value, topic number, and top five terms for the topic number for each narrative
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/1/2018
19_date_acq_dev_began: 6/1/2018
20_date_implemented: 1/1/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: The relevant data catalog is maintained by the CFPB
33_agency_data: External- CFPB Consumer Complaints
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.
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), and took place within during use case’s development. This documentation has not been shared beyond the data science team building the tool.
2_use_case_name: Graphic Design Improvements
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Information Technology
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: Increase working efficiency related to image processing
12_outputs: Images
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Regulatory Data Analysis
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Improved data quality for stakeholders
12_outputs: Values for various predicted percentile levels for a given reporter are provided to an analyst to compare to current reported values
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2023
19_date_acq_dev_began: 6/1/2023
20_date_implemented: 9/9/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Internal- Firm Data
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.
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
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: Decision Tree for Deposits Data
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Improved process efficiency and data quality. 
12_outputs: Predetermined variables are calculated and then filtered to identify potential outliers in the current reporting period data
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 3/1/2023
19_date_acq_dev_began: 3/1/2023
20_date_implemented: 8/29/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Internal- H.6 Money Stock Measures
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.
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
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: Institutional Profile Dashboard
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Education & Workforce
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The output provides leadership high level information about district FR Y-9C reporters to inform discussions with business partners or stakeholders
12_outputs: Interactive dashboards
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Novel Activities Call Report Classification
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: The random forest model would parse out what reported categories most significantly predict inclusion on novel banking activity lists
12_outputs: The model identifies call report line items that are correlated with the banks on internal supervisory lists and classifies banks based on their statistical similarity to banks engaged in novel activities
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 7/1/2024
21_date_retired: 9/30/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Internal- FDIC Deposits
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Financial News Processing
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Assist with financial news processing
12_outputs: Interactive dashboards
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/1/2023
19_date_acq_dev_began: 8/1/2023
20_date_implemented: 9/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: External- News  Feed
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.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Bank Exam Quality Control
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: A traditional machine learning NLP model of bank performance that utilizes information in news articles among various inputs
12_outputs: Model outcomes are used as a component of quality control
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/1/2022
19_date_acq_dev_began: 1/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: External- News  Feed
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.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Document Summarization Statistics
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Provide statistics about bank review letters based on the length of the letters and the frequency of common financial terms
12_outputs: Statistics on letters contained in a PDF
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2022
19_date_acq_dev_began: 1/1/2022
20_date_implemented: 12/22/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Internal- Firm Data
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: Writing Quality Analysis Model
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: A NLP model to help leaders better understand the writing quality and consistency of documents
12_outputs: Provides analysis score of consistency of writing style
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2022
19_date_acq_dev_began: 1/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
2_use_case_name: Comment Review System
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Information Technology
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_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).​ 
12_outputs: To assist the analyst in reviewing each comment, CRS uses traditional machine learning natural language processing (NLP) to provide text summarization, text matching with lists of topics, entity identification, and text similarity matching. In addition, CRS identifies duplicative or near-duplicative comment letters, provides full text search (including metadata properties), and provides the optionality of providing notes or labelling comments based on various metadata attributes. All public comments are reviewed in their entirety, and summaries are used to assist with these reviews.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/1/2021
19_date_acq_dev_began: 2/1/2021
20_date_implemented: 7/1/2021
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: External- Public Comments
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.
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: Comment Review System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
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.
2_use_case_name: Trading Desk Grouping
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Grouping Trading Desk Descriptions
12_outputs: An asset class type for each desk
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2024
19_date_acq_dev_began: 1/1/2024
20_date_implemented: 4/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: External - Banking Data
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.
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Labor Market Analysis
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Research and Statistics
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Estimates factors in the labor market
12_outputs: Time series measuring the quantity of layoffs
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2022
19_date_acq_dev_began: 6/1/2022
20_date_implemented: 4/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: External- SEC Filings
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Entity Name Matching Tool
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Improved process efficiency
12_outputs: Aids analysts in comparing a reported entity name to a database of legal entity names
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2022
19_date_acq_dev_began: 8/1/2022
20_date_implemented: 11/1/2023
21_date_retired: 6/30/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: External- Corporate Structure
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.
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Yes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside the agency.
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: Time Reporting Tool
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: Logging and analyzing time spent on tasks using AI-powered time management tools.
11_purpose_benefits:  Automatically classify individual work activities and produce a classification report
12_outputs: Categorized list of activities by time
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/1/2021
2_use_case_name: Threshold Monitoring
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Enhance monitoring on limits thresholds, faster collaboration
12_outputs: ​Tools serve analysts to enhance monitoring on limits thresholds and actuals
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2022