Nonprofit playbook looks to help SNAP leaders manage payment error rates

Starting in 2027, states could be on the hook to pay millions more in Supplemental Nutrition Assistance Program funds if they are unable to lower their payment error rates. As state leaders scramble to implement dire system and operational changes to improve payment accuracy, a civic tech nonprofit has released a playbook to guide their efforts. 

Oct. 1, 2027, marks the date that states will have to pay a larger share of SNAP food benefit costs if they cannot reduce their payment error rates to 6% or below, according to the “One Big, Beautiful Bill” that President Donald Trump signed in July. The national payment error rate in fiscal 2024 was 10.93%, the Agriculture Department reported in June. 

Nearly every state would be subject to higher SNAP costs under the new rule, and an estimated 27 states would have to cough up $100 million annually to cover program costs. California alone, for example, could face an additional $2 billion in expenses with a payment error rate of 10% or above. 

With the HR 1 deadline on the horizon for states already facing competing priorities, the nonprofit U.S. Digital Response released a playbook this month to help state SNAP leaders and staff “shape their systems and tooling to make it easier to be accurate and harder to make mistakes,” report authors wrote. The playbook is based on research and survey results from state leaders that USDR conducted from September 2025 through February 2026. 

The playbook suggests that implementing technical and operational improvements to make frontline caseworkers’ jobs easier is at the root of improving the detection and correction of erroneous SNAP payments. 

Indeed, “frontline staff are under tremendous pressure. We want to help make their jobs more straightforward,” the report states. 

One way state leaders can reduce downstream payment errors is by improving the usability of SNAP eligibility and enrollment systems. Automation, for example, can be leveraged to streamline the process for clients to report changes to their income or other SNAP status and for benefit staff to review them, the report stated. 

SNAP officials should consider opportunities to deploy automatic detection of client-reported changes in, for example, benefit portals or other integrated systems that manage the distribution of public benefits, according to the playbook. Leaders can also implement system features that automatically tag clients’ cases when they are new, altered or unchanged to reduce caseworkers’ burden of manually reviewing all SNAP cases to determine how to proceed with benefit payments.

State officials could also implement enhanced data verification experiences for caseworkers. USDR found that wage and salary data account for 43% of erroneous SNAP payments, and 46% of state agencies’ error dollars come from manual data miscalculations, system complexities for reporting and documenting client data and other challenges. 

SNAP staff could improve user interfaces to make it easier for caseworkers to view client documents and related data, the report states. For example, enabling case management systems to show verification documents, like a client’s pay stub, next to related data inputs, such as a form field to enter a client’s earned income, can help SNAP workers more accurately report data that ultimately determines benefit payments. 

The USDR playbook also recommends more proactive solutions that help caseworkers correct inaccuracies before a case is finalized. Researchers note that an in-system module that automatically flags case elements like missing data fields or address changes  helps streamline caseworkers’ review process. 

Such efforts to modernize and enhance SNAP systems and operations are already underway, according to USDR. Indeed, nearly 50% of the more than 100 state leaders surveyed told USDR researchers that their state has already launched changes to technical systems for improving payment accuracy, and 65% reported that they plan to or are already talking to caseworkers to develop solutions. For example, 65% of state leaders are turning to solutions that flag form fields that could include errors, like inaccurate or missing information. 

Social workers in Riverside County, California, for example, have been leveraging an AI assistant tool developed by Nava Labs that alerts staff to potentially incorrect or missing data as they work with clients to enroll or determine their eligibility for public assistance programs. 

Indeed, several states have tried to leverage legislation to encourage SNAP system changes in response to HR 1, according to the National Conference of State Legislatures. 

California lawmakers last year introduced a bill that would appropriate state funding for automation and other system modernization efforts to help reduce payment error rates under SNAP. In New Hampshire, a bill introduced earlier this year aims to require data sharing agreements among state agencies to further assist with eligibility determinations and verification purposes. Additionally, Maine policymakers are considering a bill that would establish an electronic system that identifies potentially incorrect eligibility determinations. 

Ultimately, the USDR playbook aims to help states navigate such legislative and programmatic changes because “reducing worker burden is a crucial — and often overlooked — aspect of improving SNAP payment accuracy,” researchers wrote.