HHS · no agency ID · record R0763

iVeri-Fi (Test)

Department of Health and Human Services · HHS/CMS/CCIIO

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: High-impactSource: a) High-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

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

Problem the AI is intended to solve

In October 2024, Serco will begin utilizing iVeri-Fi, (a decision service platform) to perform automated processing of remote identity proofing (RIDP) verification tasks. These tools are already in our Eligibility Workers Support System (EWSS) stack and have an existing Authority to Operate (ATO). We are not introducing any new technologies - we are just changing how and where the work is done through automation (before Task Inconsistency Processing System (TIPS) not integrated with TIPS).

Expected benefits

A decision service (Sapiens) will make the adjudication decision using Remote Identity Proofing (RIDP) business rules. This service integrates with Event-Based Processing (EBP) microservices, Sapiens Decision, and Rosette Name Indexer (RNI) for matching identity data. Sapiens Decision uses AI to ensure consumer and RIDP data match and will incorporate more machine learning in the future.

System outputs

Significant reductions in operational costs, increased efficiency in task processing, improved quality and consistency in decision-making, and enhanced user experience for eligibility support workers by reducing their manual workload. The system also aims to facilitate easier updates and modifications, supporting ongoing improvements and expansions of automation.

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: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
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