Agent/Broker Fraud Analysis (ABCQI)
Description
(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.
Detailed example
Reduce waste, fraud, and abuse
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Implement Machine Learning (ML) to identify potential fraud/waste/abuse within Agent/Broker data.
Audit / financial statement impact
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.
Controls / human review
ATO: Not reported; PIA: Not published