Agent/Broker Fraud Analysis (ABCQI)

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Presumed high-impact, determined notSource: b) Presumed high-impact, but determined not high impact

Impact justification

The Agent/Broker Fraud use-case is not considered high-impact as it does not serve as the basis for decisions or actions that affect civil rights, liberties, privacy, critical resources, safety, or strategic assets. The AI models within this use-case use machine learning techniques to detect patterns that are inconsistent or anomalous relative to standard consumer, agent broker, and partner actions. Our findings are shared with the agency divisions in the form of reports and tables and can be used in combination with additional information derived outside of this AI tool to determine if CMS should take any corrective actions. All outcomes are internal facing. CMS makes all determinations on actions.

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Information Technology

Operational functions

Fraud, waste and improper payments Derived by keyword rules; see methodology

Problem the AI is intended to solve

(Marketplace) Agents and brokers support the consumer enrollment and eligibility process. Because of this, they have learned the intricate details of the Federally-facilitated Exchange (FFE) for accessing applications, submitting eligibility determinations, and adding enrollments to their line of business, opening up the possibility of fraud.

Expected benefits

Implement Machine Learning (ML) to identify potential fraud/waste/abuse within Agent/Broker data.

System outputs

Reduce waste, fraud, and abuse

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.

Potential impacts and how they were identified

Not reported