GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Pre-Examination Planning Monitoring
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Depositor & Consumer Protection
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: Assist examiners in pre-exam planning and identify pre-exam questions that are misaligned or do not inform risk assessments
12_outputs: A report from  Division of Consumer Protection (DCP) Analytics to DCP Compliance and UDAP (Unfair Deceptive Acts and Practices) section related to pre-exam questions on their alignment with risk identification
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/8/2023
19_date_acq_dev_began: 2/8/2023
20_date_implemented: 12/14/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: FDIC's Enterprise Data Catalog is still being implemented
33_agency_data: PEP IR (Pre-Examination Planning Instrument Report) uses pre-exam planning documents such as ARCH (Assessment of Risk of Consumer Harm), Call Report data, to identify pre exam planning questions for further review.
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: R
42_dev_tools_wait: 6-12 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: 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: Einstein Call Center Assistance
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Depositor & Consumer Protection
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: Improve categorization and routing of consumer cases within Epic (Enterprise Public Inquiries and Complaints), to call agents and tag more effectively and efficient
12_outputs: Categorization and routing of calls and cases.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 4/19/2024
2_use_case_name: HMDA (Home Mortgage Disclosure Act) Outlier Screen
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Depositor & Consumer Protection
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: The HMDA (Home Mortgage Disclosure Act) Outlier program pre-screens off-site mortgage data to identify heightened fair lending risks related to pricing, underwriting, and redlining
12_outputs: Reports results of statistical analysis to support examination supervisory actions
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2018
19_date_acq_dev_began: 1/1/2018
20_date_implemented: 1/1/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Other
31_question_type: FDIC's Enterprise Data Catalog is still being implemented
33_agency_data: The HMDA Outlier program uses regulator version HMDA data provided by the CFPB.  The public version of HMDA data strips out or obfuscates PII.
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.
35_demo_features: Race/Ethnicity, Sex/Gender, Age
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: SAS
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: 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 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: FDIC Deposit Insurance Misrepresentation 
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Depositor & Consumer Protection
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: Division of Consumer Protection (DCP) Analytics is using AI to search social media posts to identify potential misrepresentation of FDIC deposit insurance
12_outputs: A spreadsheet report identifying specific social media posts related to a bank and FDIC deposit insurance 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/9/2024
2_use_case_name: Aggregating Complaint Data
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Depositor & Consumer Protection
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: DCP (Depositor & Consumer Protection) Exams has proposed to explore ways to increase efficiencies of analyzing complaint data to easily identify trends/concerns at our most complex banks.
12_outputs: To be determined
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/20/2024
2_use_case_name: Financial Well-Being Project
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Insurance & Research
8_topic_area: Other
8_question_type: Financial Inclusion
10_commercial_ai: None of the above.
11_purpose_benefits: (1) Developed a census of non-profit organizations that provide one or more of 9 types of financial well-being services. Such a census did not exist. (2) This census would advance the FDIC's focus on increasing economic inclusion by enabling DCP Community Affairs staff to more easily connect banks with the non-profits in their communities that provide specific types of financial well-being services.
12_outputs: Classification of the types of financial well-being services provided by a non-profit organization.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 12/31/2023
2_use_case_name: Generative Artificial Intelligence (AI) for Legal Research
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Administration
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: This use case will allow users the ability to find the answers they need for legal research faster and easier. This directly supports our mission by providing users with enhanced research capabilities through generative AI.  
12_outputs: Commercially available legal research tools will provide users with generative AI capabilities to give attorney's information faster.
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Outlook Unstructured Text Extraction 
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Administration
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The Corporate Services Branch (CSB) would like to use a custom AI prompt to test extraction from unstructured text emails that are currently going to a group box. This effort will be used to condition the data so that it can be used for reporting efforts and other needs. This is meant to be a pilot test to determine if this course of action is feasible and as a training opportunity for the Corporate Services Branch (CSB) Citizen Developer. Custom AI prompts are used for this use case.
12_outputs: The Corporate Services Branch (CSB) Pilot will automatically extract unstructured text from group email boxes when new email arrives to write the data into a usable data table for further use in management reporting and Key Performance Measures tracking. The model will also be used to look at historical data sitting in group mail boxes. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/1/2024
2_use_case_name: Public-Facing ChatBot
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Administration
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: The chatbot is an Enterprise Information and records Management Section (EIRMS) effort.  It will support the FDIC mission by mitigating calls to the FDIC Contact Center through the current RASA open-source platform.  This project is waiting for a vendor platform to support their chatbot technology in the government cloud environment.  
12_outputs: The ChatBot will use a Natural Language Processer (NLP) to provide standard responses to consumers inquiring about deposit insurance and what is covered by the FDIC.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2023
2_use_case_name: Purchase Card Activity Monitoring
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Administration
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: The monitoring of prohibited and restricted purchase card information is a manually intensive process, whereas a Machine Learning (M/L) algorithm would have a level of complexity to determine whether purchases fit within those distinct set of parameters.  This AI use cases would require the development of a training and test dataset based on actual activity  in order to determine a true level of accuracy for the Machine Learning (M/L) algorithm.  The data would then feed into a dashboard/report. 
12_outputs: The Machine Learning (M/L) would provide a data extract on prohibited and restricted purchase card activity across the FDIC.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 2/1/2023
2_use_case_name: AI-driven Workflow Automations to Improve Business Operations
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Administration
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Working with vendor software on some AI capabilities for use in the future.  There are various AI capabilities that may come available in a vendor's software (Generative AI to support human interactions, Intelligence chatbot, etc.) Human Resources (HR) is specifically looking at AI driven workflow automation to improve business operations.
12_outputs: The use case output will include decision trees to support real-time insights into business operations and help to identify trends and improve performance.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 12/1/2024
2_use_case_name: Automatically-Scored Writing Assessment (AWA)
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Administration
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The Automatically-Scored Writing Assessment (AWA) is intended to significantly increase speed and reduce cost of essay scoring for the purpose of employment decisions.
12_outputs: A single overall score for the essay on a scale from 1 to 5, as well as three subscale scores ranging from 1 to 5 across the following dimensions: grammar and mechanics, analysis and reasoning, as well as organization and structure.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 1/17/2018
19_date_acq_dev_began: 1/17/2018
20_date_implemented: 1/17/2018
22_dev_method: Developed with contracting resources.
23_contract_piids: FDIC does not use PIIDs
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Other
31_question_type: FDIC's Enterprise Data Catalog is still being implemented
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.
35_demo_features: Other
35_question_type: We have requested and received detailed information from the vendor supporting the validity and reliability of the AI tool. The FDIC AI Coordination Committee is currently developing an enterprise strategy for AI risk management and governance to ensure appropriate monitoring, evaluation, and mitigation of risks from the use of AI, including this use case.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: PDRI Performance-Fit
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: 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: 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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Structured Interview (SI) tool
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Administration
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The FDIC seeks to improve the personnel selection process by automating the scheduling, creation, administration, scoring, and maintenance of structured interview materials using an online platform. 
12_outputs: The SI (Interview) tool will securely house a large number of internally developed, competency-based structured interview questions.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2024
2_use_case_name: FDIC Predictive Budget 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: Take in budget data for reporting and prediction; Analyze and explain differences in budget and actuals data. Reduces time and work to produce the report; increases response time to react and review data; allows deeper dives.
12_outputs: Output is an explanation on gaps in data that are identified and why it was identified.
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: CFOO Assessment Chatbot
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: Compiling data from external FDIC website and targeted documents to answer public questions about Bank Assessments that are currently available on FDIC gov. Makes information more readily available to the public, searchable and readable.
12_outputs: An text-based answer to a question the user asks the bot.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/15/2022
19_date_acq_dev_began: 4/4/2022
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: CFOO (Chief Financial Officer Organization) Assessment Chatbot use the FDIC Assessment data set for training. The data contains assessment related data elements. The assessment data set is internally/externally available for all the FDIC employees, contractors and the public through FDIC.gov external site.
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. 
40_has_ato: Yes
41_system_name: External Chatbot Rasa (ECR) - Assessment Chatbot
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: 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: Goals and Workload Assumptions
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: Summarize divisional efforts into a narrative taking into consideration staff numbers and competencies. Reduced time to receive answers based on data.
12_outputs: A visualization of effectiveness of divisional projects and a recommendation on how to fill the gap on missing elements or competencies
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: CFOO Admin Chatbot
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: Compiling data from internal FDIC website and targeted documents to answer questions about CFOO admin. Makes information more readily available to the public, searchable and readable.
12_outputs: An text-based answer to a question the user asks the bot.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2022
19_date_acq_dev_began: 1/10/2023
20_date_implemented: 6/13/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: FDIC's Enterprise Data Catalog is still being implemented
33_agency_data: CFOO (Chief Financial Officer Organization) Admin Chatbot use the FDIC CFOO Administrative data set for training. The data contains administrative data elements. The administrative data set is internally available for all the FDIC employees and contractors.
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: Internal Chatbot Rasa (ICR) - Admin Chatbot
42_dev_tools_wait: 6-12 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: 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: CFOO Travel Chatbot
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: Compiling data from internal FDIC website and targeted documents to answer questions about FDIC travel. Makes information more readily available, searchable and readable.
12_outputs: An text-based answer to a question the user asks the bot.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/10/2020
19_date_acq_dev_began: 9/1/2020
20_date_implemented: 12/21/2021
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: FDIC's Enterprise Data Catalog is still being implemented
33_agency_data: CFOO (Chief Financial Officer Organization) Travel Chatbot use the FDIC Travel Policy data set for training. The data contains travel policy data elements. The travel policy data set is internally available for all the FDIC employees and contractors.
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: Internal Chatbot Rasa (ICR) - Travel Chatbot
42_dev_tools_wait: 6-12 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: 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.