Insurance Claims in the Age of AI: The Emerging Challenge of AI-Driven Fraud

Artificial intelligence is transforming nearly every stage of the insurance process. While the technology creates opportunities for greater efficiency, it is also introducing a new generation of fraud risks—particularly as generative AI makes it easier to create, alter, and manipulate the digital evidence insurers rely upon when evaluating claims.

Michael A. Rosenberg, Firm Managing Partner at Roig Lawyers, recently presented on Insurance Claims in the Age of AI, examining the emerging risks AI poses to insurers and the strategies carriers and defense counsel can use to identify and respond to increasingly sophisticated forms of fraud.

Insurance fraud has traditionally required some level of physical staging, coordination, or document manipulation. Generative AI changes that equation. Today, readily available technology can potentially be used to alter photographs of vehicle or property damage, fabricate supporting documentation, generate synthetic video, and even clone a policyholder’s voice.

For insurers, the challenge is no longer simply determining whether the facts surrounding a claim are credible. Increasingly, carriers must also consider whether the evidence itself is authentic.

 

When a Photograph Is No Longer Proof

Digital photographs have become central to modern claims handling. Insurers routinely rely on submitted images to evaluate vehicle damage, property losses, accident scenes, and other elements of a claim.

Generative AI has complicated that reliance.

Simple digital manipulation—or “shallowfake” editing—can add dents, scratches, cracks, shattered glass, and other apparent damage to an otherwise legitimate photograph. More sophisticated AI-powered tools can blend those changes into the existing lighting, shadows, and textures of an image, making alterations increasingly difficult to identify through visual inspection alone. Generative AI can even create an entirely fabricated image of a damaged vehicle or property without an authentic underlying photograph.

One reported fraud scheme demonstrates how straightforward this can be. Fraudsters obtained a social media photograph of a tradesman’s van in good condition, digitally added a cracked bumper, and submitted the altered image with a fabricated repair invoice as part of a false claim. Investigators ultimately identified the fraud by conducting a reverse image search and locating the original, undamaged photograph online.

The example highlights an important shift: fraudulent physical damage does not necessarily have to exist in the real world before it appears in a claim file.

 

The Risk Extends Beyond Photographs

Manipulated images represent only one component of the emerging AI fraud landscape.

AI technology can be used to generate convincing supporting documentation, including repair estimates, invoices, medical billing records, and other materials intended to substantiate a claim. Deepfake technology can similarly create synthetic videos depicting exaggerated injuries, accident scenes, or vehicle damage that did not occur as represented.

Voice cloning presents another potential vulnerability. With a relatively small audio sample, fraudsters may be able to create a synthetic version of a policyholder’s voice and use it to attempt to authenticate an identity, modify account information, change an address, or redirect claim payments.

Together, these technologies create the possibility of increasingly sophisticated, multi-layered schemes in which photographs, documents, audio, and video may all appear to corroborate the same claim.

 

Detecting Manipulation Requires More Than the Human Eye

As AI-generated content becomes more realistic, insurers cannot rely exclusively on visual inspection to determine whether submitted evidence is authentic.

A stronger approach combines multiple verification methods. Depending on the circumstances, this may include preserving the highest-resolution version of an original file, reviewing metadata and timestamps, examining compression and pixel inconsistencies, conducting reverse image searches, comparing available GPS information with the reported loss location, and using AI-detection technology to identify potential synthetic content.

No individual tool, however, provides a definitive answer. Detection technology is evolving alongside generative AI and may produce false positives or fail to identify certain forms of manipulation. A suspicious result should therefore be treated as an investigative indicator rather than conclusive proof of fraud. When authenticity becomes a material issue in litigation, a qualified digital forensic expert may be necessary.

 

Building AI Awareness Into Claims Handling

The most effective response to AI-assisted fraud begins before litigation.

Carriers can strengthen their claims processes by developing multi-layer verification protocols for digital evidence and training adjusters to recognize indicators of possible manipulation. Reverse image searches and authenticity checks can become part of the investigative process, while secure insurer-controlled platforms can provide an alternative method for obtaining photographs when submitted evidence raises concerns.

Documentation is equally important. When potential manipulation is identified, carriers should maintain a clear record of the verification methods used, investigative findings, and subsequent claims-handling decisions—particularly when the matter may proceed to litigation.

Patterns across multiple claims may also warrant broader investigation. Repeated participants, similar manipulated evidence, recurring providers, or other connections can signal activity extending beyond an isolated questionable claim and may require coordination among claims professionals, SIU teams, investigators, and defense counsel.

 

Staying Ahead of an Evolving Threat

AI is unlikely to replace traditional forms of insurance fraud. Instead, it provides another set of tools that can make existing schemes easier to execute, more convincing, and potentially more difficult to detect.

That reality requires a corresponding shift in how digital evidence is evaluated. A photograph, video, document, or voice recording may look authentic and still warrant verification when other aspects of a claim raise concerns.

For insurers, the goal is not to assume that every piece of digital evidence has been manipulated. It is to develop the processes and investigative capabilities necessary to recognize when further scrutiny is appropriate.

As artificial intelligence continues to evolve, collaboration among claims professionals, SIU investigators, forensic experts, and defense counsel will become increasingly important in identifying suspicious activity, preserving evidence, and developing effective responses to emerging fraud schemes.

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Roig Lawyers works with insurers and Special Investigation Units to identify, investigate, and respond to complex and emerging forms of insurance fraud. Our attorneys combine litigation experience, fraud investigation strategies, and data-driven analysis to help clients identify patterns, evaluate suspicious claims, and develop proactive strategies designed to protect against evolving risks.