Modernize Medicaid Fraud Detection with Data Driven Insights

by Jane Doe Smith, Data Analyst Specialist, LexisNexis

Modernize Medicaid Fraud Detection with Data-Driven Insights

How one state uncovered hidden provider risks — and built a replicable model for others.
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Fraud, waste, and abuse continue to impact Medicaid budgets nationwide. As schemes evolve, agencies require modern tools that expose hidden connections and strengthen oversight. This proof of concept demonstrates how advanced data integration and analytics can help identify high-risk providers before they cause damage.

Across the country, fraudulent providers exploit gaps in enrollment and monitoring — re-entering networks through family ties, business partners, or shell organizations. One forward-thinking Medicaid agency tackled this challenge head-on, partnering with LexisNexis® Risk Solutions to implement a data-driven, service-based approach to program integrity.

By merging state enrollment data with vast identity and business intelligence, the agency revealed:

  • Inappropriate connections between active and excluded providers
  • Collusive networks attempting to re-enter via hidden ownership
  • Operational efficiencies that let staff focus on high-value investigations

The model leverages real-time analytics, exclusion list management, and continuous monitoring to deliver actionable fraud insights. Case reports, risk scoring, and advanced linkage analysis provide Medicaid leaders with the intelligence needed to make faster, better-informed decisions.

Fraud prevention today means seeing beyond the surface. Learn how your agency can replicate this success — protecting program dollars and restoring public trust in Medicaid.

 

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