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How state and local agencies can get ahead of fraud starting with the data they already have
State and local agencies can combat fraud by unifying fragmented data across disparate systems to detect anomalies before payments are issued.
Why it matters
Reducing the hundreds of billions lost annually to fraud ensures public funds remain available for citizens who rely on these services.
The details
- The US GAO estimates $233 billion–$521 billion is lost annually to federal program fraud.
- California's EDD achieved a 99% reduction in threat response time using unified data.
- Fraudulent SNAP transactions rose 55% between Q4 FY2024 and Q1 FY2025.
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Key connections
California Employment Development Department (EDD) uses Elastic
EDD used Elastic to consolidate system and transactional data across more than 3,000 servers.
California Employment Development Department (EDD) is located in United States
California Employment Development Department is a state agency in the United States.
Government Accountability Office (GAO) is located in United States
GAO is the supreme audit institution of the United States federal government.
Centers for Medicare & Medicaid Services is located in United States
Centers for Medicare & Medicaid Services is a federal agency of the United States.
United States Department of Agriculture (USDA) is located in United States
USDA is a federal executive department in the United States.
Elastic Agent Builder is built with Generative AI
Elastic Agent Builder utilizes generative AI for conversational investigation capabilities.
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Elastic Agent Builder is built with Semantic Search
Elastic Agent Builder works alongside semantic search to empower fraud analysts.
Elastic uses Machine Learning
Elastic uses machine learning models and detection rules on unified data for fraud detection.
Elastic is related to OpenTelemetry
Elastic fraud detection architecture leverages industry standardization like OpenTelemetry.
Related events
California EDD Consolidates Data with Elastic to Reduce Threat Response Time
Elastic Outlines AI-Driven Fraud Detection Architecture for Government Agencies
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