By: Yasin Turkcan, SVP, Product & Innovation at Gainwell Technologies
The passage of H.R. 1 has fundamentally shifted the landscape for state agencies administering the Supplemental Nutrition Assistance Program (SNAP). By tightening the link between state error rates and financial consequences, the legislation has turned accuracy in eligibility decisions from a quality standard into a critical financial imperative.
The U.S. Department of Agriculture (USDA) has made it clear: states with error rates above 6% face severe penalties. Given that the average national payment error rate hit 10.9% in FY2024, including $9.3 billion in overpayments, these penalties threaten benefit issuance, administrative funding, and repayment liabilities, demanding a shift in operational behavior.
The Reality: Administrative Performance Under Scrutiny
Most SNAP payment errors don't stem from fraud. They're a result of process strain.
Caseworkers are managing rising caseloads, outdated systems, and staffing shortages while navigating complex eligibility rules. The common causes of errors are largely operational:
- Incorrect income reporting or missed household changes
- Administrative and data entry errors
- Legacy system defects or incomplete data matching
These issues compound under pressure with growing backlogs and inconsistencies. This frustrates workers and, more importantly, impacts families who rely on these benefits. For recipients, eligibility errors often mean prolonged delays, leaving them without critical food assistance for weeks.
Using Technology to Reduce Exposure
Under the new H.R. 1 guidelines, states cannot afford high error rates. But improving accuracy doesn't mean hiring more staff. Technology can act as a lever to reduce financial exposure without cutting access or sacrificing service quality.
H.R. 1 creates stronger incentives to prioritize quality control (QC), accuracy, and process modernization. By working smarter with technology, you can identify where improvements will deliver the most impact.
One state that modernized its processes and integrated technology with human review is already seeing results:
- 10% reduction in caseworker errors through AI and machine learning quality checks
- 5,000 pre-renewals reviewed per day through robotic process automation (RPA) based on state needs
- Improved accuracy and timeliness using document validation and data-matching tools
When AI handles routine verification and data validation, caseworkers are free to focus on nuanced cases and quality reviews rather than manual evaluation and data entry.
Harnessing Predictive AI to Mitigate Risk
As health and human services agencies increasingly turn to analytics and AI, the opportunity to reduce errors through data-driven intervention grows. Speed is essential. To innovate quickly, look for AI solutions that operate as "plug and play" components in your existing workflows. This reduces the need for massive, disruptive system upgrades.
By combining predictive AI models with operational workflows, agencies can identify changes in eligibility or benefit levels in near real-time, reducing overpayments and underpayments. Modern AI tools analyze complex data patterns to flag risks that traditional reviews might miss.
The real impact comes from embedding these insights directly into daily case management. This integration allows frontline workers to act quickly on potential errors, resulting in improved payment accuracy, lower administrative costs, and reduced risk of federal penalties.
Tangible Steps to Reduce SNAP Payment Errors
1. Use predictive models to identify high-risk cases
Use data-driven prediction tools to flag and address potential payment errors efficiently. This enhances accuracy and prevents future issues before they impact error rates.
2. Automate detection of data mismatches and errors
Automated systems can monitor case data for policy inconsistencies. This reduces errors, standardizes evaluations, and enables quick correction of discrepancies, improving decision accuracy.
3. Let regulations guide interventions
Use the FNS 310 Handbook, state policies, and quality control results to implement compliant corrective actions. Evidence-based interventions prevent recurring errors in eligibility and payment processes.
4. Integrate insights into case review workflows
Integrate predictive analytics and automated alerts into SNAP tools. This provides real-time feedback, enhances compliance, and reduces manual oversight.
5. Start with targeted caseloads, then scale
Pilot predictive error detection in high-risk populations first. Once proven, scale statewide to ensure effective, sustainable integration into your SNAP operations.
Building a Foundation for Accuracy and Trust
Reducing SNAP error rates is about more than just compliance; it's about restoring confidence in the safety net. With fewer eligibility mistakes, states protect their funding, reduce costly rework, and deliver benefits more quickly to the families who need them most.
As federal oversight tightens, the message is clear: H.R. 1 has raised the stakes. Identifying opportunities where AI and analytics can drive down error rates is the most effective way to strengthen program integrity and maintain public confidence in essential benefit systems.