AI cuts SNAP payment errors and review costs significantly
Reducing SNAP Payment Errors at Lower Cost With AI: A Simulation Study
Computers and SocietyArtificial Intelligence
Summary
States need to reduce errors in SNAP food assistance payments to avoid costly penalties. The authors compare two ways to find errors before payments go out: one uses risk scores to pick cases, and the other uses AI to flag likely mistakes. They find that using AI along with risk scores costs much less than just risk scores alone while still catching errors effectively. This means AI can help states save money and improve how they check SNAP payments.
What this means in practice
- •For state welfare agencies: Reduce SNAP payment errors and review costs by using AI-assisted preauthorization review combining AI flags with risk scores.
- •For government audit teams: Improve targeting of case reviews for public assistance programs by integrating AI discrepancy flags with statistical risk models to cut error rates and expenses.
Tested on simulated data.
Authors
Alex Chohlas-Wood
Abstract
New federal legislation will require states with high payment error rates in the Supplemental Nutrition Assistance Program (SNAP) to pay a share of benefit costs, potentially costing some states over a billion dollars per year. These costs create substantial pressure to improve payment accuracy. One strategy to improve payment accuracy is preauthorization review, in which staff double-check applications and correct mistakes before benefits are issued. But limited staff capacity makes it important to target these reviews effectively. One leading targeting approach uses statistical risk models to prioritize cases likely to contain errors. Another novel approach uses AI to flag specific potential discrepancies for reviewers to check. For states under pressure to reduce error rates, it is unclear which approach-or combination of approaches-would reduce payment error rates most effectively. We compare these strategies through simulations using public SNAP quality control data. Under our main assumptions, risk-based review alone costs about seven times as much as combining AI flags with risk scores. This cost advantage persists across a wide range of plausible assumptions. Our findings suggest that AI-assisted preauthorization review could be a key tool to help states avoid substantial cost-sharing obligations.