This blog post serves as a guide for state agencies to develop flexible and actionable metrics systems for tracking the implementation and impact of new work requirements under H.R. 1.
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).
An advisory playbook to help state and local government leaders improve SNAP payment accuracy and lower administrative burdens following the passage of H.R. 1.
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 analyzes how the One Big Beautiful Bill Act (OBBBA) will dramatically shift SNAP costs onto state governments, projecting massive budget increases and fiscal strain.
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 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.
