State and local governments that administer the Supplemental Nutrition Assistance Program (SNAP) face increasing pressure to improve payment accuracy, prevent fraud and manage rising administrative costs. As federal policy changes shift more program costs and accountability to states, agencies must find more effective ways to detect errors, prioritize investigations and ensure benefits reach eligible recipients quickly and accurately.
This paper explores how artificial intelligence and machine learning (AI/ML) can help agencies strengthen SNAP program integrity while improving operational efficiency. By analyzing large volumes of transaction and application data, AI tools can identify patterns that signal potential errors or fraudulent activity, automate routine reviews and help staff focus their efforts on high-risk cases.
