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 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.
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.
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.
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 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.
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 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 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 project portfolio page details a human-centered service design partnership with the Michigan Unemployment Insurance Agency (UIA) to revitalize and streamline the state's unemployment benefits system following crisis-level strain.
