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Which states report the highest SNAP trafficking and fraud rates and why?
Executive summary
Available reporting shows two different ways of ranking states: (a) improper payment/error rates, where Alaska (60.37%), New Jersey (35.7%), South Carolina (22.57%), Delaware (22.8%) and D.C. (20.26%) were cited among the highest error rates for FY2023 [1], and (b) reported SNAP theft/transaction fraud counts and dollar losses, where New York (151,000 claims, ~$80M), California (~86,000 claims, ~$38M) and Maryland (~63,800 claims, ~$24M) top recent lists through March 2025 [2]. These two metrics measure different problems and are produced by different data systems, which complicates simple “which states have the most fraud” answers [3] [4].
1. Different measures, different stories: trafficking vs. improper payments
The USDA and analysts use several distinct measures: the National Payment Error Rate (NPER) or state improper payment/error rates capture all overpayments and underpayments (recipient error, state agency error, sometimes fraud) and are not a pure measure of fraud [3] [4]. Separately, retailer trafficking estimates — produced from retailer studies and investigations — measure benefit redemption converted to cash or illicit transactions and are reported less frequently; trafficking is a subset of fraud and is estimated roughly every three years [3] [4].
2. States with the highest error rates are not necessarily those with the biggest theft totals
Aggregate error-rate lists name states like Alaska (60.37%), New Jersey (35.7%), South Carolina (22.57%), Delaware (22.8%) and D.C. (20.26%) as having the highest FY2023 improper payment rates [1]. Those high percentages reflect a combination of administrative errors, eligibility mistakes, and some fraud — not solely trafficking or criminal theft [3] [4]. By contrast, counts of reported fraudulent EBT transactions or “SNAP benefit thefts” show New York, California and Maryland with the largest numbers and dollar losses in recent USDA data through March 2025 [2]. The two lists therefore point to different problems and data sources [3] [2].
3. Why some states show high error or trafficking numbers — reporting, program size, and detection
Experts and oversight bodies note three main drivers: program size, detection practices, and administrative changes. Large states or those with big caseloads (e.g., New York, California) naturally report larger absolute counts of theft and fraud claims; small states with concentrated administrative problems can show very high percentage error rates [2] [1]. The Congressional Research Service and GAO emphasize that error-rate increases after the pandemic were linked to policy changes, operational strain, and reporting differences rather than only criminal intent [4] [5]. Detecting trafficking also depends on state investigative capacity and USDA tools, so variation may reflect resources and enforcement choices as much as true prevalence [3] [6].
4. Recent spikes and what they reflect
News outlets and USDA releases flagged a sharp rise in reported fraudulent claims in late 2024–early 2025: one headline noted 226,000 fraudulent claims and $102 million lost in Q1 FY2025, with Alabama highlighted in one report, while Newsweek compiled state-level claim totals through March 2025 showing New York highest by count and dollars [7] [2]. Such quarter-to-quarter spikes can reflect increased reporting, active investigations, new detection methods (card cloning, skimming, electronic theft), or true increases; available reporting notes detection and classification differences but does not fully attribute the cause of any single spike [7] [2].
5. Oversight, disagreement and policy implications
GAO has recommended improved measurement and national analytics capacity to better identify improper payments and trafficking; many recommendations remained open as of late 2024 [5]. USDA emphasizes prevention and penalties and has a SNAP Fraud Framework combining data analytics with state actions [6]. Commentators and partisan outlets differ on emphasis: some focus on victimized recipients and administrative error [3] [4], others emphasize criminal trafficking and taxpayer loss [7] [2]. Available sources do not present a single unified accounting that reconciles state error rates, retailer trafficking estimates, and counts of reported thefts into one ranked “fraud list” [3] [4].
6. What reporting does not (yet) tell us and how to read the numbers
Current sources show that “fraud” and “errors” are measured differently, that large absolute counts often come from large caseload states while extreme percentage rates can come from small or administratively strained jurisdictions, and that USDA, GAO and CRS caution about interpretation [3] [5] [4]. Available sources do not provide a single dataset that definitively ranks states by total fraud adjusted for caseload, nor do they fully attribute drivers of specific recent spikes beyond noting improved detection and operational factors [5] [4]. For policy debate, readers should compare both absolute dollar/count measures (for taxpayer impact) and percentage/error measures (for program integrity) and note which measure a headline cites [1] [2].