To protect program integrity, agencies should consider modern tools that go beyond static provider checks and support continuous, intelligence-driven oversight.

Provider fraud is one of the most complex and costly challenges facing Centers for Medicare and Medicaid Services (CMS) programs today. Traditional provider screening models were not designed to detect coordinated fraud schemes, hidden ownership structures, or rapidly evolving provider risk.

Many agencies continue to rely on siloed systems that store incomplete provider identities, lack cross-program entity resolution, and provide little insight into emerging risk after enrollment. These gaps allow fraudulent providers to operate undetected—often for months or years—before enforcement action occurs.

Modern Medicare and Medicaid program integrity strategies are shifting toward advanced analytics, Artificial Intelligence (AI), and identity intelligence to close these gaps. By unifying fragmented provider data,  uncovering hidden relationships, and continuously monitoring provider behavior, agencies can identify risk earlier and respond faster.

This white paper examines how a next-generation provider fraud detection framework enables agencies to:

• Strengthen provider enrollment and post-enrollment screening
• Detect hidden ownership, affiliations, and fraud networks
• Unify provider identities across disparate data sources
• Monitor provider risk continuously—not just at enrollment
• Reduce improper payments while protecting patient access