⚠️ Disclaimer: This analysis identifies statistical anomalies. Anomalies are not proof of fraud. Unusual patterns may have legitimate explanations.

Medicare Fraud Analysis

Data-driven tools to detect billing anomalies, impossible volumes, and statistical red flags in Medicare claims data.

$14.6B
Alleged fraud — 2025 DOJ takedown
$1.2B
Criminal recoveries — FY2025 MFCU
Home/Fraud Analysis
🤖AI Overview

Our analysis identified 10 statistical anomalies across 10 specialties. While anomalies are not proof of fraud, the DOJ 2025 healthcare fraud takedown — involving $14.6B in alleged fraud and 324 defendants — shows that billing irregularities are a serious and ongoing problem.

Analysis Tools

Common Fraud Types

Upcoding

Billing for a more expensive service than what was actually provided. E.g., billing a complex office visit (99215) when a simple one (99213) occurred.

Phantom Billing

Charging for services or procedures that were never actually performed.

Kickbacks

Receiving payment for patient referrals. Illegal under the Anti-Kickback Statute.

Unbundling

Billing separately for procedures that should be billed together at a lower combined rate.

Related from TheDataProject.ai

⚖️ See healthcare crime data on OpenCrime

📖 The $100 Billion Problem: The Economics of Medicare Fraud

A comprehensive look at the scope, economics, and detection of Medicare fraud — and what the data reveals.