As generative AI becomes more deeply embedded in healthcare workflows, organizations are increasingly recognizing documentation authenticity as another area of risk that impacts payment accuracy. We’ve explored the broader implications of AI-generated medical fraud, the challenges of detecting synthetic content, and why traditional controls weren’t designed for it.

The more immediate question now is operational: How do special investigation units and payment integrity teams validate documentation authenticity without creating another manual review process or slowing investigations?

Why SIU Leaders Are Paying Attention

Fraud schemes are becoming more sophisticated, as artificial intelligence accelerates both their scale and complexity. 

• Healthcare fraud causes tens of billions of dollars in losses annually, according to the FBI.
• In 2025, the DOJ’s National Health Care Fraud Takedown involved 324 defendants and more than $14.6 billion in alleged fraud.
• 92% of insurance companies reported financial losses from deepfake-related payment errors in 2024.
• Studies found only 68% of people identified a deepfake when unaware of the possibility—and just 34% did so when warned one might be present.

Traditional Payment Integrity Workflows Weren’t Built to Validate Documentation Authenticity

Can the documentation itself be trusted?

Traditional payment integrity, audit, medical record reviews, and special investigation unit workflows were designed to determine whether documentation supports a claim—not whether the documentation itself has been manipulated, synthetically generated, or duplicated.

This  means documentation authenticity may represent a validation gap within current health plan workflows.

A Deepfake in Action:

Consider a prior authorization request for a lumbar spinal fusion procedure. The submission includes supporting clinical documentation and diagnostic imaging. Medical necessity appears supported, and nothing appears unusual.

The request moves through utilization management and payment integrity workflows exactly as expected. Reviewers evaluate the clinical narratives for medical necessity, payment integrity teams validate coding and policy alignment, and no concerns about fraudulent claims are raised because the documentation appears complete and internally consistent.

But what if the workflow cannot determine whether the supporting imaging itself is authentic? The documentation wasn’t overlooked—the workflow simply wasn’t designed to validate its authenticity.

The procedure is approved and the claim is ultimately paid. The issue may not surface until months later during a fraud, waste, and abuse investigation, retrospective audit, provider review, or law enforcement inquiry—after the financial exposure has already occurred. At that point, organizations face not only the potential improper payment, but also the cost of investigation, clinical re-review, provider outreach, appeals, recovery efforts, and administrative burden.

That’s the visibility gap many organizations are now evaluating. The question is no longer whether documentation supports the claim, but whether organizations have the ability to determine that the documentation itself can be trusted before payment decisions are made.

The Real Adoption Challenge Is Operational Burden

Experienced SIU leaders have seen this pattern before: New advanced analytics technologies promise greater visibility into emerging fraud schemes, but visibility alone does not guarantee operational success. If a solution surfaces more potential risk than teams have the capacity to investigate, organizations can end up with larger backlogs, more alerts, and more operational complexity without necessarily reducing fraud.

That reality shapes how leaders evaluate documentation authenticity and deepfake detection:

• Will this create an additional queue? 
• Will investigators need to review more records? 
• Will we need additional staff? 
• Will this disrupt the workflows our teams already rely on?

For many payers the extent to which AI is being used to generate documentation remains unclear. This uncertainty raises important questions about the level of validation needed to ensure authenticity and reliability of the documents being submitted. Prevention almost always outweighs recovery, which can involve investigations, appeals, provider abrasion, and administrative burden.

The answer isn’t creating another review process, it’s incorporating a mechanism to ensure documentation authenticity  into existing payment integrity workflows without increasing operational burden.

What SIU Leaders Should Evaluate

Before introducing deepfake detection into an SIU or payment integrity workflow, leaders should ask several practical questions:

Are current controls designed to detect this type of risk?

Traditional payment integrity and fraud detection models were built to identify billing anomalies, utilization patterns, and claims-based risk—not validate documentation authenticity.

What exposure may exist today?

If manipulated documentation enters claims workflows undetected, organizations may face missed FWA, improper payments, increased investigation costs, delayed detection, and unrecoverable downstream spend.

Where should this operationally sit?

Most organizations would align this capability within:

• SIU
• Payment Integrity
• Audit
• Compliance / Governance

The right model depends on operational maturity and current workflow ownership.

Will this create additional operational burden?

The right approach should integrate into existing SIU and payment integrity workflows—helping teams prioritize higher-risk documentation through automation and explainable risk scoring rather than creating new manual review processes or requiring additional headcount.

Operationally Scalable Deepfake Detection for Healthcare Payers

Codoxo Deepfake Detection was designed to help payer organizations validate documentation authenticity within the workflows they already use to review claims, investigate fraud, and assess payment integrity risk.

Operationally scalable deepfake detection:

• Fits within existing SIU and payment integrity workflows
• Helps teams validate suspicious documentation within active reviews
• Prioritizes higher-confidence suspicious documentation
• Provides explainable findings that support investigator judgment
• Supports payment and investigative decision-making without increasing operational burden

Using healthcare-trained AI and machine learning, Codoxo Deepfake Detection analyzes supporting documentation for indicators of manipulation, cloning, duplication, and other authenticity concerns. Explainable findings, contextual validation, and configurable risk scoring provide investigators with actionable evidence, not just another alert.

Documentation authenticity is becoming an increasingly important part of modern payment integrity, but the path forward should be familiar: identify risk earlier, equip investigators with better detection, and evaluate emerging risks within the workflows teams already trust. 

To learn more about Codoxo’s ability to identify AI-generated, manipulated or cloned healthcare documentation, visit our Deepfake Detection Solutions page.