How state and local agencies can get ahead of fraud starting with the data they already have
Blog post from Elastic
State and local agencies face increasingly sophisticated fraud across programs such as unemployment insurance, Medicaid, SNAP, tax refunds, loans, grants, and procurement, while fragmented data systems often prevent investigators from identifying suspicious patterns before payments are issued. The piece argues that unifying existing claims, identity, payment, enrollment, tax, and other records can reveal cross-program relationships, such as multiple identities tied to one bank account or conflicting applicant information, while reducing manual record reconciliation and improving detection rules, machine learning models, and anomaly detection. It cites estimates of substantial government fraud losses, rising AI-enabled identity and phishing threats, and examples including California’s Employment Development Department, which consolidated data to improve threat-response times. Rather than requiring large technology replacements, agencies can begin by establishing data-sharing agreements, connecting and normalizing a limited number of high-value sources, improving entity resolution, and automating alerts, then expand incrementally to strengthen program integrity, meet oversight expectations, and protect public funds.
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