Summary
Artificial Intelligence (AI) is known for enabling health care and other companies to operate more efficiently—and now the technology is giving the government enhanced tools to quickly identify, investigate, and prosecute health care fraud cases.
The 2026 National Health Care Fraud Takedown by the Department of Justice (DOJ) prominently featured the use of AI and advanced data analytics. Federal officials continue to emphasize that these tools are now central to how health care fraud is detected, investigated, and prosecuted.
The Upshot
- DOJ on June 23 announced charges against 455 defendants—including 90 doctors and other licensed medical professionals—in connection with schemes involving more than $6.5 billion in alleged fraud.Federal officials repeatedly highlighted how AI and data analytics were used to detect fraudulent billing, stop improper payments, and support prosecutions.
- The government is now institutionalizing these new capabilities. The DOJ’s Fraud Division has secured cloud-computing space inside the Centers for Medicare & Medicaid Services’ (CMS) data environment to run AI and analytics algorithms, and it has entered into data-sharing agreements with the Department of Homeland Security and the Federal Trade Commission “aimed at breaking down data silos.”
- AI is moving into real-time claim review and payment prevention, including through state programs such as Indiana’s 90-day pilot with CMS and Oracle to use AI to flag suspicious Medicaid billing before payment and assemble investigation-ready case packets.
The Bottom Line
Health care fraud remains a top white-collar enforcement priority, and AI has accelerated the speed and scale at which the government can act on it. In the first case brought through the Fraud Division’s new Financial Intelligence Review Team, prosecutors opened an investigation within five days of a financial-intelligence review and made an arrest in less than seven months. Providers, digital health companies, and technology vendors should be prepared for increasingly sophisticated analytics applied to their billing data.
The Government’s Expanding AI and Data Analytics Infrastructure
For years, the Department of Justice (DOJ) has strengthened the infrastructure that makes its current enforcement push possible. A critical facet of the DOJ’s new infrastructure is the Health Care Fraud Unit’s Data Fusion Center, announced as part of last year’s June 30, 2025, National Health Care Fraud Takedown, and staffed by the Unit’s Data Analytics Team, together with HHS-OIG, the FBI, and other agencies. DOJ says the Data Fusion Center used advanced analytics in many of the cases charged in 2026.
As part of this year’s takedown, the Fraud Division and the Centers for Medicare & Medicaid Services (CMS) disclosed a new agreement giving the Division cloud-computing space within the CMS Integrated Data Repository “in which to deploy advanced data analytics algorithms and artificial intelligence tools.”
Separate data-sharing agreements with the Department of Homeland Security and the Federal Trade Commission are “aimed at breaking down data silos,” and CMS is standardizing data fields reported across Medicaid, managed-care, and other plans to make cross-program analytics faster and more reliable.
The government is explicit in stating its goal is to use AI and advanced data analytics for both prevention and prosecution. HHS Secretary Robert F. Kennedy, Jr., explained that the government is “deploying advanced artificial intelligence and data analytics to identify fraudulent billing patterns in real time, stop improper payments before they occur, and strengthen oversight across federal health programs.”
State-level efforts deploying AI are also emerging. Indiana’s Family and Social Services Administration recently launched a 90-day pilot with CMS and Oracle to test whether AI can flag suspicious Medicaid billing before claims are paid and assemble investigation-ready case packages. And, through its AI-powered WISeR model, CMS has begun using AI to screen requests for services flagged as vulnerable to fraud or misuse, including skin substitutes and nerve stimulator implants across six states.
From Data to Indictment
This year’s takedown shows how analytics can drive cases from start to finish. DOJ announced the first prosecution arising from its Fusion Center’s Financial Intelligence Review Team in a $67 million scheme to bill Illinois Medicaid for behavioral health services that were not provided, with claims allegedly submitted for 500 or more hours of counseling and therapy services per day. Prosecutors “opened the investigation within five days of the financial intelligence review,” and the defendant was arrested less than seven months later at an airport while allegedly attempting to leave the country.
Data analytics also helped expose one of the year’s largest health care fraud categories. The Fraud Unit’s Data Analytics Team detected a spike in Medicare claims for wound allografts. According to the DOJ’s figures, payments climbed from roughly $200 million in 2019 to $14.4 billion in 2025 before collapsing to about $100 million in 2026. This was driven in part by CMS’s decision to realign the payment rate to $127 per square centimeter effective January 1, 2026. The allograft cases included an alleged $906 million scheme in the Southern District of Texas built on allegedly fraudulent billing. As CMS Administrator Dr. Mehmet Oz put it, prosecuting fraudsters “is necessary – but stopping them before a single dollar leaves the building is smarter.”
Implications for Providers
Real-time analytics, interagency data-sharing, and AI-assisted case-building may mean anomalous billing is detected faster, and the path from anomaly to charging decision is shorter than it used to be. In-house counsel should consider the following:
- Identify High-Risk Billing Areas: Conduct targeted reviews of billing categories most susceptible to data-driven detection (e.g., high-cost procedures), where statistical outliers are more likely to draw scrutiny. Billing patterns that deviate from expected norms can trigger investigation.
- Know Your Data Before the Government Does: Ensure that the company is using AI to promptly identify billing trends, volume spikes, and coding anomalies across facilities. Internal detection and investigation of anomalies—before they are flagged externally—create an opportunity to remediate and, where appropriate, to consider self-disclosure.
- Pressure Test AI-Driven Findings: Early reporting on WISeR describes denials that doctors attribute to AI “hallucinations” that invented clinical facts. Providers should preserve the underlying medical records, claims data, and communications needed to test whether an AI-flagged anomaly reflects an actual billing issue or a hallucination.
Ballard Spahr’s White Collar Defense and Investigations Group is currently advising health care clients on navigating changing DOJ priorities, as well as on responding to governmental requests, investigations, and civil enforcement proceedings. Please contact us if you have questions regarding these matters or need assistance responding to active inquiries, investigations, or proceedings.
Subscribe to Ballard Spahr Mailing Lists
Copyright © 2026 by Ballard Spahr LLP.
www.ballardspahr.com
(No claim to original U.S. government material.)
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, including electronic, mechanical, photocopying, recording, or otherwise, without prior written permission of the author and publisher.
This alert is a periodic publication of Ballard Spahr LLP and is intended to notify recipients of new developments in the law. It should not be construed as legal advice or legal opinion on any specific facts or circumstances. The contents are intended for general informational purposes only, and you are urged to consult your own attorney concerning your situation and specific legal questions you have.