Risk Assessment Tools
Video outlining best practices for states to improve their risk assessment and error detection processes, including model design, review sequencing, feature engineering, review process optimization, and quick wins
Georgetown University Better Government Lab
Promising Practices in SNAP PER Reduction: Data-Driven Preauthorization Reviews
By Sarah Esty (Aspen Institute Financial Security Program) and Eric Giannella (Georgetown University Better Government Lab) To assist states working to rapidly reduce SNAP payment error rates to avoid new cost-share requirements, the Safety Net Response Network has been convening monthly meetings of state data practitioners for peer learning around successful error reduction strategies. Through these meetings, as well as research led by data experts at Georgetown and Yale, promising practices are emerging. While we will refine and validate these…
Is This Working? Data-Driven Strategies to Reduce SNAP Payment Error Rates
States can act to reduce their SNAP PERs but must move quickly for their efforts to impact their share of program costs in 2027.