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).
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.
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.
A resource outlining state approaches to reducing payment error rates (PER) in SNAP and implementing new federal work requirements and other changes under H.R. 1.
American Public Human Services Association (APHSA)
An interactive dashboard that enables users to explore and monitor key metrics of the Supplemental Nutrition Assistance Program (SNAP) Quality Control (QC) system.
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.
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.
An interactive dashboard that allows users to explore Supplemental Nutrition Assistance Program (SNAP) Quality Control data to better understand payment errors, eligibility issues, and administrative performance across states.
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.
