Healthcare fraud is evolving – and in some cases, it’s no longer visible in the ways payment integrity teams expect.

Generative AI has made it possible to create real-world clinical documentation that looks legitimate at a glance. Clinical notes can be well-written, structured correctly, and aligned to expected coding from healthcare systems. Supporting materials can appear complete and consistent.

But that doesn’t mean they’re real.

This shift is being driven by the rise of deepfakes: AI-generated fraud or manipulated documentation, images, and other materials used to support claims. What once required clinical knowledge can now be produced quickly, at scale, and with a high degree of realism.

The impact of AI deepfakes is already being felt by health payers. In 2024, 92% of insurance companies reported financial loss from AI deepfake-related incidents. And even when trained reviewers know AI-powered deepfakes are present, they can only identify them around 30% of the time without AI assistance.

For SIU and payment integrity teams, artificial intelligence introduces a new challenge: documentation that looks “perfect” but cannot be trusted. 

Understanding how this type of fraud appears in practice is the first step toward detecting it. Across AI generated documentation, three patterns are emerging.

1. Fully AI-Generated Documentation

In this scenario, the entire record is created using generative AI.

Fraudsters can generate:

• Progress notes
• Therapy documentation, including voice cloning
• Imaging reports

These records often include:

• Internally consistent
• Plausible patient narrative
• Proper terminology 
• Realistic structure

Because modern AI models understand medical terminology and documentation structure, these records can pass standard review processes. There are no obvious red flags: no misspellings, no broken logic, no formatting issues. And, no human authored any part of the document.

What makes this risky:
The documentation appears valid, but no provider actually authored it.

2. Blended (Partially AI-Generated) Documentation

This is often the most difficult type of fraud to detect.

A legitimate medical record exists, but AI-generated content has been layered into it.

Examples include:

• Extended diagnoses to support higher reimbursement
• Inflated procedure counts
• Modifying clinical narratives to better justify procedures

Because part of the record is real, the document does not appear fabricated. Instead, it looks complete and credible.

Investigators reviewing the record may see:

• Authentic clinical language
• Consistent structure
• No obvious inconsistencies

What makes this risky:
Real and synthetic content are combined, making manipulation difficult to isolate through manual review.

3. Cloned Documentation at Scale

The third pattern introduces scale.

A single legitimate medical record from a provider is used as the basis for multiple variations. Generative AI-driven tools can quickly automate updating the same documentation with small wording and/or imaging changes so each version appears unique.

These variations can then be used across:

• Multiple patients
• Unrelated cases
• Separate claims

To a reviewer, the records may not appear duplicated. The wording differences are enough to avoid simple detection methods.

What makes this risky:
One real document can be reused to an unlimited number of claims, without triggering traditional detection tools or duplication checks.

Want a quick visual breakdown of these patterns? View the infographic.

Learn More About AI-Generated Medical Fraud

AI-generated medical fraud is already impacting healthcare payers, and it’s evolving quickly.

Download or share the infographic to help your team recognize these patterns.

Related Content:

• On-Demand Webinar: The Deepfake Threat to Healthcare: Why Traditional Fraud Detection Is No Longer Enough: See real examples of how synthetic documentation and deepfakes are appearing in claims.
• See Deepfake Detection in Action: Learn how Codoxo’s helps healthcare payers detect AI-generated documentation and strengthen payment integrity workflows.
• Deepfake White Paper: The Deepfake Threat to Healthcare: Explore the white paper for an in-depth look at how deepfakes are evolving and the tools to stay ahead of fraud risk.
• AI for Payment Integrity (PI) Community on LinkedIn: Join the deepfake conversation with other payment integrity professionals.