Wagner Program Services LLC | Phoenix, AZ | SDVOSB (Service-Disabled Veteran-Owned Small Business)
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Federal agencies are deploying artificial intelligence at an unprecedented rate to detect fraud, waste, and abuse in government payment systems. The Centers for Medicare and Medicaid Services (CMS), the Department of Veterans Affairs (VA), the Department of the Treasury, and Offices of Inspector General (OIG) across the federal government now rely on AI-powered tools to flag suspicious claims, identify improper payments, and prioritize investigations.
But AI alone is not enough.
The Government Accountability Office (GAO) has repeatedly found that agencies deploying AI for fraud detection lack qualified staff to oversee these systems. Federal law — including Office of Management and Budget (OMB) Memorandum M-25-21 and the Payment Integrity Information Act (PIIA) — requires human oversight of high-impact AI systems. Every flagged claim needs human eyes. Every override needs an audit trail. Every AI decision that affects a payment, a provider, or a beneficiary must be reviewed by a trained analyst.
The GUARD Protocol™ is Wagner Program Services' proprietary five-phase framework for delivering that human oversight. Developed by a team of certified Anti-Money Laundering (AML) investigators with deep experience in financial crimes detection, the GUARD Protocol™ translates proven private-sector investigation methodology into a structured, repeatable, audit-ready process for federal AI governance.
The federal government reported over $162 billion in improper payments in the most recent fiscal year, with cumulative losses exceeding $2.8 trillion over the past two decades. Agencies like CMS, the VA, and the Treasury Department have turned to AI-powered systems to stem these losses — tools that can analyze millions of claims, flag anomalies, and prioritize investigations far faster than any human team.
But deploying AI creates a new problem: Who watches the machine?
GAO report GAO-25-108412 found that federal agencies are deploying AI for fraud detection but lack the qualified workforce to provide meaningful oversight. AI systems produce false positives that waste investigator time and false negatives that let fraud slip through. Without trained analysts reviewing AI outputs, agencies face: