DOI · DOI-0010 · record R2735

Anomaly Detection for Financial Transactions Related to DOI Programs

Department of the Interior · OIG

Overview

Development stage: PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2025-04-01 (day precision)Source: 4/1/2025
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Other
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Use traditional machine analytics with difficult-to-manually-analyze financial data for DOI programs to create analytical methods that are scalable and repeatable. Additionally, the analytics must be aligned with quality standards required for OIG audits and investigations.

Expected benefits

It allows OIG to identify anomalies or trends that would be undetectable via manual analysis or would require endless IT and staff resources to manually process and analyze information.

System outputs

Outputs will identify anomalous transactions and will be provided to auditors or law enforcement analysts and agents to further review and/or action.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: l) Other
Training and evaluation data: Financial Transaction Data related to DOI Programs
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported