Looming SNAP budget crisis demands SAS Payment Integrity for Food Assistance to reduce error rates

States are poised to be on the hook for billions of dollars in food assistance benefits they didn't plan for

CARY, N.C., Nov. 17, 2025 /PRNewswire/ -- US states are scrambling to meet new federal accuracy requirements for Supplemental Nutrition Assistance Program (SNAP) benefits, with billions of dollars at stake. A new social benefits solution from data and AI company SAS, SAS Payment Integrity for Food Assistance, can not only calculate a more accurate SNAP error rate; it can also identify opportunities to reduce errors caused by human mistakes or deliberate fraud and abuse.

"What was once considered a 'nice-to-have' has rapidly become a 'must-have'," said John Maynard, a principal solutions architect at SAS and former program integrity lead for Ohio Medicaid. Maynard has spent the last three months traveling to dozens of states to discuss the looming budget crisis and how SAS Payment Integrity for Food Assistance can make a difference.

"In some cases, states didn't realize what their budget exposure was when lacking federal help, because it's not something they've had to consider before. We're seeing a rapid groundswell of demand for technology like SAS Payment Integrity for Food Assistance," said Maynard.

The time for states to act is yesterday

With the end of the US government shutdown, states will have to rush to issue benefits and process pending cases, including a backlog of emergency food stamp cases. As speeding up processing efforts often results in more errors, enhanced error reduction efforts will be essential to restoring systems.

The shutdown has exacerbated a looming crisis for states. Starting in 2027, states that don't meet new federal accuracy thresholds for SNAP payments will face steep penalties with some potentially exceeding $1 billion annually. Under new rules in the 2025 US budget bill HR1, if a state's SNAP payment error rate exceeds 6%, it will be required to contribute between 5% and 15% of its total SNAP benefits - a cost previously covered entirely by the federal government.

Currently, 42 states are on track for budget-busting penalties and consequences unless they take urgent action. Potential ramifications include:

• More populous states could face over $1 billion annually in new costs.
• Smaller states with high error rates could owe $100 million to $500 million a year, crippling state budgets.
• This will likely trigger budget cuts elsewhere - potentially education, healthcare, or infrastructure.

Additionally, many state SNAP systems are aging, underfunded and staffed by a shrinking workforce. As states scramble to avoid fiscal disaster, many are still using outdated quality control processes that estimate error rates from small samples but do little to reduce them. By contrast, SAS Payment Integrity for Food Assistance uses AI and machine learning to analyze every active SNAP case in real time. Instead of just estimating an error rate, the solution also directs personnel to the exact cases most likely to contain payment errors - giving states a real opportunity to lower their error rates and avoid costly penalties.

Payment errors are often caused by mistakes made by citizens in their filings or during the data entry process. However, some errors are due to fraud perpetrated by individuals, SNAP vendors or more sophisticated organized collusion between groups of citizens and vendors. SAS Payment Integrity for Food Assistance can uncover those fraudsters, protect the system and help ensure benefits are reaching the people that need them.

Targeted, industry AI solution addresses arduous tasks to free up workers

SAS Payment Integrity for Food Assistance, built on the data and AI platform SAS^® Viya^®, offers intuitive dashboards and visualizations that allow quality staff and operational managers to quickly act against errors. Trend analysis tracks high-risk error cases over time to monitor and improve potential effects on federal quality control error rates. SAS Payment Integrity for Food Assistance enables users to resolve errors faster, enhance payment integrity and drive continuous improvement.

SAS brings decades of public sector experience, including working with public health and assistance agencies to enhance social benefit integrity. Fueled by a $1 billion investment in industry solutions, SAS Payment Integrity for Food Assistance joins a family of SAS AI models and solutions that address a specific labor- and time-intensive process that can slow organizations down. In the case of SNAP programs, social benefits workers can more clearly see where to dedicate their newfound time to reduce errors and fight fraudsters. SAS' packaged models and solutions come intended to tailor and accelerate training on customer data. All can quickly and easily integrate with the existing systems of organizations of all sizes.

Inspired by guidelines from SAS' Data Ethics Practice, customers can rely on SAS' simple documentation to deploy solutions that deliver results that are easy to understand and explain. Users are equipped to take back their time and enjoy boosted productivity and return on investment, all while adhering to the highest standards of trustworthy AI and innovation.

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