In the news release, Nevada selects SAS to help reduce SNAP error rates with AI, issued 24-Jun-2026 by SAS over PR Newswire, we are advised by the company that changes have been made. The complete, corrected release follows, with additional details at the end:

Nevada selects SAS to help reduce SNAP error rates with AI

State sees early success with SAS Payment Integrity for Food Assistance

CARY, N.C., June 24, 2026 /PRNewswire/ -- The Nevada Division of Social Services (DSS) has chosen data and AI leader SAS to help the state reduce error rates in its Supplemental Nutrition Assistance Program (SNAP) program. New federal accuracy requirements mean states with rates above six percent will be on the hook for billions of dollars they didn't plan for. Nevada DSS will use a social benefits solution, SAS Payment Integrity for Food Assistance, which 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.

While Nevada DSS has historically had SNAP payment error rates well below the national average, the new six percent threshold put the state at risk of a $52 million budgetary penalty. SAS will analyze the department's SNAP benefit determinations and provide monthly risk scoring for which determinations are most likely to be in error. Nevada DSS can triage cases most urgently in need of review based on the likelihood of error and the dollar amount at risk of error.

"We have been working diligently to implement plans to reduce that error rate and come in under six percent," said Kelly Cantrelle, Deputy Administrator over Program and Field Operations at the Nevada DSS. "We are launching technology initiatives that can help reduce error rates utilizing data analytics, along with continuous income evaluation to let us know when income goes over the SNAP threshold. We hope that these two initiatives could potentially lower the SNAP payment error rate by up to two percent, which would bring us under that six percent error rate."

With just the initial results from the first two months of deploying the SAS solution, NV DSS case workers investigating cases have already identified $130,000 in monthly overpayments.

"It was a 'prove it' moment that was quite encouraging," said John Maynard, a principal solutions architect at SAS and former program integrity lead for Ohio Medicaid. "Nevada has done such a great job keeping their error rate down, that once we have more data in our solution, I believe the state will reach its goal. By assisting Nevada in lowering SNAP payment error rates, SAS will help ensure that families eligible for food assistance will get the full amount of the benefits they qualify for, while also reducing the likelihood of families owing the government money due to overpayment."

Following the initial implementation, the SAS solution will be automated and integrated into NV DSS's monthly workflow. This will allow leadership to receive, prioritize and act on automated risk scores while simultaneously identifying suspicious patterns indicative of fraudulent activity.

As the SAS SNAP AI model pipeline ingests more monthly data, the model being trained will become even more precise at distinguishing between unintentional administrative errors and intentional fraud. This continuous model learning and training cycle will enable NV DSS to continuously enhance error and fraud detection capabilities, helping solidify its SNAP payment error rate well below the federal threshold.

The window is closing for states to reduce SNAP error rates

Beginning in 2027, states that fail to meet the new federal SNAP payment accuracy standards will face significant penalties, with some potentially topping $1 billion per year. Under the 2025 US budget bill HR1, any state with a SNAP payment error rate above 6% must pay between 5% and 15% of its total SNAP benefits.

At the same time, many state SNAP systems are outdated, underfunded and supported by a shrinking workforce. As states rush to prevent a fiscal crisis, many still rely on legacy quality control methods that estimate error rates from small samples but do little to actually reduce them. In contrast, SAS Payment Integrity for Food Assistance uses AI and machine learning to analyze every active SNAP case. Rather than simply estimating an error rate, the solution also pinpoints the cases most likely to contain payment errors, giving states a practical way to lower error rates and avoid costly penalties.

Payment errors often stem from mistakes made by citizens in their filings or during data entry. But some errors are the result of fraud carried out by individuals, SNAP vendors or more sophisticated collusion between groups of citizens and vendors. SAS Payment Integrity for Food Assistance can help expose that fraud, safeguard the system and support the delivery of benefits to the people who truly need them.

Purpose-built AI helps staff focus on the work that matters most

Built on the data and AI platform SAS^® Viya^®, SAS Payment Integrity for Food Assistance provides intuitive dashboards and visualizations that help quality staff and operational managers respond to errors quickly. Trend analysis follows high-risk cases over time to track and improve potential effects on federal quality control error rates. The solution helps users address errors more efficiently, strengthen payment integrity and support ongoing improvement.

SAS brings decades of public sector experience, including work with public health and assistance agencies to improve social benefit integrity. Backed by a $1 billion investment in industry solutions, SAS Payment Integrity for Food Assistance is part of a broader portfolio of SAS AI models, model pipelines and solutions designed to help organizations improve operational inefficiencies that can slow them down. For SNAP programs, that means social benefits workers can better target their time to reduce errors and detect fraud. SAS' packaged models, model pipelines and solutions accelerate training on customer data and can integrate quickly and easily with existing systems in organizations of all sizes.

"State government agencies are facing unique, fast-moving and ever-evolving challenges including operational hurdles, data integrity and reporting requirements," said Udo Sglavo, Vice President, Applied AI and Modeling R&D, SAS. "Staying ahead of the curve requires an innovative approach to problem solving. As data and AI challenges rapidly evolve, SAS continues to invest in innovations like our SNAP solution to meet our customers where they are in their analytical journeys and give them the exact tools they need to tackle their most complex objectives."

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Editorial Contact:
Trent Smith
Trent.Smith@sas.com
919-531-4726
sas.com/news

Correction: An update has been made to the quote from Kelly Cantrelle.

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