This guide outlines key strategies, definitions, and procedures for improving SNAP payment accuracy and reducing quality control (QC) error rates across states.
A case study explaining how a predictive, data-driven machine-learning model was developed to detect unauthorized cash benefit withdrawals more quickly and accurately in California.
An interactive dashboard that enables users to explore and monitor key metrics of the Supplemental Nutrition Assistance Program (SNAP) Quality Control (QC) system.
This workshop summary synthesizes key takeaways from a convening of nearly 40 research and data analytics staff from 15 states focused on SNAP Quality Control (QC) data modeling.
This blog analyzes how the One Big Beautiful Bill Act (OBBBA) will dramatically shift SNAP costs onto state governments, projecting massive budget increases and fiscal strain.
A directive issued by the Commonwealth of Virginia to materially reduce the error rate in Supplemental Nutrition Assistance Program (SNAP) benefit processing among local social services offices.
This blog discusses how the “Big Beautiful Bill” (H.R. 1) contains provisions that undermine SNAP and warns that states will be burdened by its fiscal and administrative impact.
A report summarizing effective state practices, promising initiatives, and federal resources to improve payment accuracy in the Supplemental Nutrition Assistance Program (SNAP).
A blog post outlining key strategies states can use to lower SNAP payment error rates, a priority given new fiscal penalties tied to error rates under recent federal law.
A report examining how risk assessment tools are used to improve payment accuracy in nutrition assistance programs and identifying effective practices for their design and implementation.
This article provides an overview of the Medicaid Payment Error Rate Measurement (PERM) program and examines how the 2025 budget reconciliation law introduces new federal funding reductions for states that exceed specific eligibility error thresholds.
The report reviews the scope and methods of SNAP benefit theft—including card skimming, cloning, phishing, and algorithmic attacks—and examines the effectiveness of state and federal countermeasures.
