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.
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.
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.
The article analyzes the impacts of Arkansas's Medicaid work requirements, finding that while coverage losses were reversed after the policy was halted, it did not improve employment and led to negative consequences such as increased medical debt and delayed care.
This guide outlines key strategies, definitions, and procedures for improving SNAP payment accuracy and reducing quality control (QC) error rates across states.
This technical brief uses predictive analytics to identify the primary drivers of SNAP payment error rates (PER) following the implementation of the One Big Beautiful Bill (OBBB).
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.
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.
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).
An interactive dashboard that enables users to explore and monitor key metrics of the Supplemental Nutrition Assistance Program (SNAP) Quality Control (QC) system.
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.
