VA · VA-24-3291 · record R2232

Payment Redirect Fraud (PRF) Model

Department of Veterans Affairs · VBA: Veterans Benefits Administration

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: High-impactSource: a) High-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: Not reported

Mission

Topic area: Procurement and Financial Management
Operational functions: Fraud, waste and improper payments Derived by keyword rules; see methodology

Problem the AI is intended to solve

The goal of the Payment Redirect Fraud (PRF) model is to identify which Direct Deposit (DD) changes are likely to be fraudulent and refer them on to investigators.

Expected benefits

Preventing Veterans from having their benefits stolen by fraudsters builds their trust in the VA. The accurate delivery of benefits without interruption creates operational efficiencies and demonstrates fiscal stewardship and prudent use of tax-payer dollars.

System outputs

The output is a risk indicator on how likely each daily direct deposit change may be fraudulent.

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: scikit-learn.org ↗

Sourcing

How it was built: Contract and in-houseSource: c) Developed with both contracting and in-house resources
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: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
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