H.R. 1 Changed the Rules. SNAP Administrators Need a Different Approach

The H.R. 1 Act fundamentally changed how SNAP accountability works.

Before H.R. 1, when a state’s payment error rate crossed federal thresholds, the U.S. Department of Agriculture (USDA) Food and Nutrition Service (FNS) assessed a liability—but the model was largely corrective-action driven. States could be required to reinvest up to 50% of that liability in improvement activities, while designating the remaining 50% “at-risk” for future repayment. If the state avoided being financially responsible again in the second consecutive year, it kept the at-risk amount; if not, USDA collected it.  

H.R. 1 replaces that paradigm with an ongoing cost-sharing model: beginning in FY 2028, states with payment error rates above 6% must pay a standing share of SNAP benefit costs—generally 5% to 15%, tied directly to annual performance. 

What SNAP systems were built to do and what they were not 

SNAP eligibility systems were built with a clear mission: process applications and renewals efficiently, apply policy rules consistently, issue and recoup benefits, and move large volumes of cases through strained operational environments. 

They are transaction engines that largely perform as intended – supporting access to benefits even as caseloads fluctuate and staffing remains tight. 

What they were never designed to do is independently validate their own decisions, predict where errors are most likely to occur, or explain why outcomes were correct across complex and changing data inputs. They were not built to surface systemic breakdowns across workers, workflows, and data sources. 

H.R. 1 now expects that level of accountability. 

The structural flaw hiding in plain sight 

Most states rely on the same SNAP system to make eligibility decisions and generate data used to evaluate those decisions. 

This creates unavoidable blind spots. 

Systems cannot independently detect their own logic failures. They cannot easily reconcile inconsistent or incomplete data across income, expense, and household information. And they struggle to identify recurring error patterns such as procedural mistakes, data mismatches, or policy interpretation drift before those issues scale. 

In effect, systems are being asked to grade their own work. 

Under H.R. 1, this model creates risk rather than assurance. When the same system both processes eligibility and judges accuracy, states lack the validation needed to confidently defend outcomes—particularly as error rates above 6% now trigger stronger fiscal consequences. 

Why SNAP errors persist—even without fraud 

Most SNAP payment errors are not driven by fraud. They are driven by operational strain. 

States consistently report errors tied to administrative mistakes, data entry issues, system defects, missed household reporting changes, inconsistent policy interpretation, and mismatched data across sources. These are not unique cases. They represent the majority of overpayments and underpayments identified during quality control reviews. 

Once benefits are issued, those errors are locked into official metrics. Correcting them later does not remove them from the payment error rate. 

This explains why national error rates remain elevated even as states invest in training, oversight, and corrective action. 

Why downstream controls are no longer enough 

Quality control (QC) remains essential, but it was never intended to function as a real-time accuracy engine. 

QC reviews are retrospective. They identify errors after eligibility determinations are finalized and benefits are delivered. Under H.R. 1, that timing matters more than ever. 

Beginning in FY2028, states with payment error rates above the federal threshold will be required to assume between 5% and 15% of SNAP benefit costs, depending on how far above the threshold their error rate falls. Those penalties are tied to outcomes—not intent, effort, or post-hoc correction. 

Incremental fixes help at the margins. They do not change when errors are detected. More training does not surface systemic error patterns earlier. Additional reviews increase workload without preventing errors from entering official metrics. 

How states need to respond under H.R. 1 

States already take this layered approach in other areas of program oversight. In Payment Integrity for claims, no single system or vendor is expected to catch every error. States routinely stack multiple approaches—pre-payment controls, post-payment review, analytics, and independent validation—to reduce error rates and financial risk. SNAP accuracy under H.R. 1 requires the same mindset.  

H.R. 1 requires states to rethink where and when accuracy is addressed. 

• Accuracy must move upstream. 
  States need visibility into risk before cases reach quality control. That means identifying inconsistent data, procedural drift, and high-risk cases within workflows—not months later. 

• Processing must be separated from proof. 
  Eligibility systems are effective at processing cases. Proving accuracy requires independent validation. This separation strengthens audit defensibility and reduces reliance on self-reported systems. 

• States must focus on patterns, not just cases. 
  Individual errors matter—but systemic errors matter more. Without insight into recurring root causes, states are left reacting case by case without reducing error rates. 

• Accuracy improvements must not slow access. 
  States are processing enormous volumes of data—often ingesting hundreds of thousands of records monthly from over 1,700 data sources. Any response that adds manual burden risks slowing eligibility decisions and increasing strain on staff. 

• Progress must be measurable. 
  Under H.R. 1, improvement must be clearly demonstrated. States need evidence that error risk is being identified earlier and addressed more effectively—not just assurances that corrective actions are underway. 

• Using multiple solutions for error detection is not only acceptable—it’s best practice. 
  No single solution can identify every type of discrepancy in SNAP eligibility or payment accuracy. Different tools apply distinct algorithms, data sources, and validation logic, which means they often uncover different issues. By stacking complementary technologies, states create a layered defense against errors, improving detection rates and reducing fiscal exposure under H.R. 1. This aligns with modern risk management principles: redundancy and diversity leads to stronger overall program integrity. 

Why the gap is widening 

At the same time expectations are increasing, operational pressure is intensifying. 

States face overburdened staff, long training cycles, rising caseloads, and growing procedural complexity. Caseworkers are expected to move faster—often while managing incomplete or conflicting information. 

This is not a workforce failure. It is a system design challenge. Expecting manual processes to compensate for structural limitations becomes less realistic as volume and accountability increase. 

What H.R. 1 made visible 

H.R. 1 did not make SNAP systems obsolete. It made their limitations visible. 

States that continue to treat accuracy as a downstream audit problem will struggle to defend outcomes after the fact. States that treat accuracy as an upstream design requirement—focused on early detection, independent validation, and systemic insight—will be better positioned financially, operationally, and publicly. 

The rules have changed. The systems have not. Recognizing that gap and responding is now a critical part of SNAP success and being able to serve beneficiaries well.  

Yasin leads Gainwell’s Product & Innovation group, helping Gainwell clients design and devise strategies to deliver their policy goals. He oversees innovation activities across Gainwell to integrate AI, automation, and other innovations into our products & services. Yasin is an expert in business model transformations, with a passion for generating value in healthcare with technology and innovation. He brings a combination of strategy consulting and hands-on industry transformation experience from his past roles.