The federal government loses an estimated $233 billion to $521 billion to fraud every year. The compliance architecture — risk assessments, GAO frameworks, Do Not Pay screening — is working as designed. The problem is that the fastest-growing fraud isn't caught by exclusion lists. Synthetic identities, coordinated benefit rings, and cross-program behavioral patterns emerge from data, not from known bad actors. This policy brief argues that closing the federal fraud gap requires a fundamental infrastructure shift: the ability to store, unify, and analyze transaction data at the speed and scale modern fraud demands. It examines why existing compliance frameworks are necessary but not sufficient, what the data infrastructure requirements look like, and how federal agencies can move from reactive exclusion-based controls to proactive, analytics-driven fraud prevention.
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Federal agencies lose between $233 billion and $521 billion to fraud annually — not because compliance frameworks are missing, but because the fastest-growing fraud patterns are invisible to exclusion-based controls.
Synthetic identities, coordinated benefit rings, and cross-program behavioral patterns only become visible through unified, high-velocity transaction data analysis — a capability most federal data infrastructure cannot currently deliver.
Closing the federal fraud gap requires moving beyond compliance tools to a data infrastructure that can store, unify, and analyze transaction data at the speed and scale the modern fraud problem demands.
Federal agency CIOs, CDOs, fraud prevention program leads, and technology policy officials responsible for data infrastructure modernization and anti-fraud capabilities.
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