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Analysis of Robotic Process Automation in SNAP: Three Case Studies

Resource type
Research, Analysis & Background
Research type
Benefit Content/Cost
Cost Study
Resource Materials
PDF Icon Summary (251.24 KB)
PDF Icon Final Report (2.99 MB)
PDF Icon Appendices (1.38 MB)
PDF Icon CT Infographic (281.83 KB)
PDF Icon GA Infographic (305.89 KB)
PDF Icon NM Infographic (334.01 KB)

​This study examines the use of robotic process automation (RPA) technologies by three state agencies—Georgia, New Mexico, and Connecticut—to administer the Supplemental Nutrition Assistance Program (SNAP). RPA is software that integrates with other programs to automatically complete repetitive processes that normally are performed by humans.

To issue benefits to new and recertifying households, SNAP state agencies must verify certain household information to determine eligibility, which requires staff to complete various administrative tasks. Many tasks involve routine data entry subject to error, so some states use RPA technology to automate these repetitive tasks to improve customer service, increase productivity, and reduce errors. This study estimates RPA impacts on time needed to certify SNAP applications, costs of RPA, and state staff perception of RPA.

Key Findings
  1. As of January 2022, nine states were using RPA in SNAP administration.
  2. SNAP recertifications processed using RPA had lower payment error rate in Georgia.
  3. For Georgia’s RPA, benefits exceeded costs within 1 year.
  4. Results suggest New Mexico’s RPA, which helps process address changes, helps save time for participants.
  5. The study did not find a significant correlation between RPA use and reduced time to case approval or denial in Connecticut and Georgia.
  6. A lack of productivity data made it impossible to assess precise time savings for workers.
Page updated: November 08, 2023