Food assistance programs like SNAP are lifelines for millions of households. Yet, ensuring their accuracy is an ongoing challenge for state agencies managing them.

Even small errors in eligibility decisions can quickly add up, costing states billions, straining resources and undermining trust in the program. The question isn’t whether these errors exist, it’s how states can uncover their root causes and fix them before they spread.

SNAP error symptoms to root cause

When I began my career in public service processing SNAP applications at the county level, I knew the key SNAP error types well: income, shelter, deductions and household size.

Later, leading quality control (QC) and quality assurance (QA) teams, I saw firsthand how states struggled to go beyond identifying these “symptoms” and get to the underlying “disease.”

The QA team used statistical sampling and case reviews in the current year ahead of the federal QC review to forecast the federal SNAP error rate. The hope was to detect any trends or systemic issues and work to resolve them to minimize the impact on the state’s QC error rate. Data queries were created to try to find more erroneous cases.

The problem is that traditional QC processes take place months after benefits have been issued. QA teams try to forecast problems by sampling current cases, but sampling is inherently limited.

It is the equivalent of taking temperature readings on various parts of the human body and using them together to determine one's body temperature. It offers only a snapshot, not the full picture. Worse, it can’t pinpoint which cases are likely to be wrong or why.

That’s where advanced analytics changes the game.

Better diagnostics through technology

Health care offers a useful analogy. For decades, physicians relied on surface-level exams to spot illness.

When diagnosing patients, health care professionals know the same symptoms may be present for many illnesses. However, the cause may be a recurring or new disease. Like many states, our state QA staff lacked better diagnostic tools to help diagnose the program at any given time. We needed a way to find cases with eligibility errors, fix them and then better understand the root cause of those errors.

Following this analogy between SNAP payment errors and human disease, I like to think about medical advances like X-rays and Magnetic Resonance Imaging (MRI) technology. The ability to look at the whole body at once or at specific parts, to see what the human eye could not, has changed how disease is diagnosed and treated.

Identifying hotspots and then performing additional testing can pinpoint the problem. Advanced analytics can do the same for SNAP today.

Solutions like SAS Payment Integrity for Food Assistance provide regular, typically monthly, “full-body scans” designed to examine all active SNAP cases for potential errors. It would take thousands of QA staff to review every SNAP case every month. With this for SNAP quality assurance, every case is risk-scored to identify those at the highest risk of error for intervention. The result is an automated continuous monitoring system for SNAP QA.

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Advanced analytics for targeted interventions

Think of SNAP case errors like tumors. With limited staff capacity, agencies can’t treat everything at once – they need precision. Analytics-driven QA gives states the ability to:

• Identify cases most likely to result in overpayments.
• Prioritize reviews where intervention will have the biggest impact.
• Layer additional data for even greater precision, like adding contrast dyes to an MRI.
• Maximize positive outcomes by focusing effort on cases that matter most.

Fixing these broad systemic issues is helping prevent future errors and lower payment error rates.

Having a larger number of cases in error for root cause analysis helps a state spot systemic issues related to case workers’ understanding of policy, training needs, common eligibility system data entry errors, or eligibility system processing errors over the long term.

Working smarter, not harder

One of my managers when I was a young SNAP eligibility worker always said, “Work smarter, not harder.”  He knew that more efficient work processes were needed to avoid errors and manage the volume of SNAP cases each worker certified. That wisdom rings true for SNAP quality assurance today.

Instead of throwing more staff at the problem or waiting months for backward-looking reviews, states now can see payment accuracy issues in near real time. Advanced analytics makes it possible to spot high-risk cases quickly, intervene where it matters most and prevent errors from compounding across the system.

Want to learn more? Tune in to the live webinar Feeding Accuracy: Innovate SNAP Quality Assurance Using Data and AI on Oct. 21

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