Federal AI Intelligence · U.S. Government AI Observatory
Agency AI profile
National Credit Union Administration (NCUA)
Key findings
- NCUA reported 6 use cases (0.2% of all records): 6 deployed, 0 pilots, 0 in development or acquisition, 0 retired, 0 with no stage.
- 5 are designated high-impact; 0 of those answered at least one minimum-practice question.
- Mean disclosure completeness is 85% of 12 core fields, ranking 12 of 41 agencies.
- Sourcing is reported for 50% of records.
Portfolio
Technology
| Classification | Records | Share |
|---|---|---|
| Classical / predictive ML | 6 | 100% |
Topic areas
| Topic | Records | Share |
|---|---|---|
| Procurement and Financial Management | 6 | 100% |
Governance disclosures (high-impact records)
| Practice | In place | In progress | N/A | No answer | Scope |
|---|---|---|---|---|---|
| Pre-deployment testing | 0 | 0 | 0 | 5 | 5 |
| AI impact assessment | 0 | 0 | 0 | 5 | 5 |
| Independent review | 0 | 0 | 0 | 5 | 5 |
| Ongoing monitoring | 0 | 0 | 0 | 5 | 5 |
| Operator training | 0 | 0 | 0 | 5 | 5 |
| Fail-safe | 0 | 0 | 0 | 5 | 5 |
| Appeal process | 0 | 0 | 0 | 5 | 5 |
| User and public consultation | 0 | 0 | 0 | 5 | 5 |
Field completeness
| Field | NCUA | All agencies |
|---|---|---|
| Stage | 100% | 91% |
| High-impact designation | 100% | 88% |
| Topic area | 100% | 83% |
| AI classification | 100% | 82% |
| Problem | 100% | 84% |
| Benefits | 100% | 82% |
| Outputs | 100% | 79% |
| Sourcing | 50% | 44% |
| Start date | 100% | 39% |
| ATO | 33% | 43% |
| PII | 100% | 42% |
| Custom code | 33% | 47% |
Method
All figures are computed directly from the normalised inventory records. Category labels are normalised from the source answer options; original values are kept on each record. Governance answers are classified as in place, in progress, not in place, not applicable, precluded, waived, other or no answer, and blanks are never read as "no". See the Methodology page for full rules.
Limitations
- Figures are what agencies reported in the 2025 inventory. They are not verified, and registration of use cases is not complete for every agency.
- A blank answer is missing evidence, not evidence that a practice or safeguard is absent.
- Counts are use-case records. A larger portfolio does not indicate better performance or greater AI maturity, and agencies differ in how they split systems into use cases.
- The inventory has no budget, cost, accuracy or outcome data. None is shown or inferred here.
- This is a disclosure analysis. It is not an audit, a compliance determination or an official government assessment.
Source records
All 6 records.
| Record | Agency ID | Agency | Use case | Stage |
|---|---|---|---|---|
| R2434 | NCUA_E&I-01 | NCUA | Machine Learning Data Validation | Deployed |
| R2435 | NCUA_ONES-02 | NCUA | Supervisory Stress Testing | Deployed |
| R2436 | NCUA_ONES-03 | NCUA | Risk Indicator Model | Deployed |
| R2437 | NCUA-12-2025 | NCUA | Moody’s Stress Test Modeling | Deployed |
| R2438 | NCUA-13-2025 | NCUA | ADCo MBS/CMO Modeling | Deployed |
| R2439 | NCUA-14-2025 | NCUA | CoStar CRE Modeling | Deployed |
Prepared with Federal AI Intelligence by Irakli Petriashvili. Source: https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory