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 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 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 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).
