Resources and Toolkits

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

Paper exploring how uncapped benefit calculations in payment error predictive models can help create space for more “buffer” and create inclusion or exclusion criteria for better targeted reviews and greater predictive power.

Workshop examining how predictive analytics and risk scoring can improve SNAP implementation and evaluation, using Conneticut as a case study.

An interactive cross-state analysis tool demonstrating how removing the $58 SNAP QC tolerance threshold could increase payment error rates and related state costs.

An interactive dashboard that enables users to explore and monitor key metrics of the Supplemental Nutrition Assistance Program (SNAP) Quality Control (QC) system.

An interactive dashboard that enables users to explore and monitor key metrics of the Supplemental Nutrition Assistance Program (SNAP) Quality Control (QC) system.

Resource Hub including data visualization tools, modeling tips, and lessons gathered from States to support the work of QC data modeling nationwide.

Workshop to foster collaboration and share modeling practices among state agencies in response to HR1’s new SNAP PER cost share requirements. Gathering encompassed convened a group of nearly 40 research and data analytics staff from across 15 states involved in SNAP QC data modeling.

Guide explaining automatic benefit notice generation process while walking through common underlying system errors (e.g. missing or incorrect information) providing insight on how back-end design decisions cause these issues. Provides strategies to improve notice process and output such as targeted questions on system logic, workarounds for caseworkers, and structural reform through data flow, boilerplate language,…