Forensic Verification in Claims Investigations

What used to be a reliable source of truth is now frequently fabricated. With AI-driven image editing, synthetic media, and deepfake tools readily available, visual evidence can no longer be taken at face value. In claims investigations, where photos of damage, injury, and medical equipment directly influence approvals and settlements, this shift has significant implications.

A recent social media trend underscores just how realistic and accessible these AI tools have become. In the “homeless man prank,” people used AI tools to create realistic images of a stranger inside their home and sent them to family or friends, implying there was an intruder. The reactions were recorded and posted online, generating thousands of videos and millions of views.

In Colorado, one recipient called 911, leading police to respond to a non-existent emergency and divert critical resources. In some states, knowingly triggering emergency response in this way is a criminal offense. Although framed as a joke, the trend underscores a larger reality: AI-generated images are now convincing enough to drive real-world decisions and actions.

If an AI intruder can prompt a 911 response, it is not difficult to imagine similar imagery being used to support staged property damage, inflate injury severity, misrepresent accident conditions, and submit falsified medical documentation.

Today, it is essential to verify visual evidence in claims investigations.

Images Can’t be Trusted in Claims Investigations

Digital image manipulation is now fast, inexpensive, and highly convincing. Free AI-editing and image-generation tools allow anyone, regardless of technical skill, to produce visuals that look authentic. As a result, 66% of U.S. adults say they at least sometimes encounter misleading images or videos (Pew Research Center).

Research shows people misjudge AI-generated images at high rates. According to a study focusing on human faces, participants detected deepfakes with about 62% accuracy, barely better than guessing, and confidence did not improve accuracy (Cornell).

Additionally, the FBI has warned that criminal actors are already using AI to create realistic false videos, audio, and documents to support fraud schemes. When manipulated imagery enters the claims process unchecked, the result can include:

• Inflated or fraudulent payouts
• Incorrect liability determinations
• Wasted investigative resources
• Regulatory and reputational exposure

What is Forensic Verification?

Forensic verification is the process of determining whether visual evidence is authentic, unaltered, and accurately represents the event or condition it claims to show. It combines digital forensics, contextual investigation, and documentation practices to separate genuine evidence from manipulated or misleading imagery.

1. Metadata & EXIF Analysis

Original image files contain EXIF data that can show:

• Device make/model
• Date and time of capture
• GPS coordinates (if enabled)
• Whether editing software was used

If a claimant submits a “same-day accident photo,” but the metadata shows the image was captured months earlier, or at a different location, that discrepancy indicates potential misrepresentation.

2. Compression, Artifact, and Error-Level Analysis

Forensic tools examine pixel patterns and compression layers to detect:

• Splicing or cloning of objects
• Inconsistent shadows or lighting
• Added or removed elements
• Retouching or masking attempts

These manipulation signatures are often invisible to the naked eye, which is why human review alone is insufficient.

3. Provenance and Reverse Image Search

Reverse image search and provenance checks can identify whether:

• The image originated from a stock photo site
• It previously circulated on social media
• It was used in another claim

The Department of Homeland Security notes that the cost of fabricating realistic synthetic media has fallen dramatically, making provenance verification essential.

4. Chain of Custody Practices

Images used in claim evaluation must be treated as evidence, which means documenting:

• Who captured the image
• How it was transmitted (text, email, etc.)
• Whether the original file is preserved

Screenshots often remove metadata, so lack of metadata is a signal.

5. AI/Deepfake Detection Tools

AI detection tools can help identify whether an image has been altered or generated synthetically, but they cannot confirm authenticity on their own. Detection models are improving, yet image-generation tools continue to evolve rapidly, and results can vary based on the model, resolution, and file type.

Detection tools support decision-making, but they cannot replace a layered verification workflow.

Application to Claims Investigations

With remote and digital claims processes now standard, adjusters increasingly rely on photos submitted by claimants, contractors, medical providers, and repair vendors. In today’s environment, where images can be easily edited or even fully AI-generated, a photo is no longer just supporting documentation; it functions as evidence. This means it must be verified accordingly.

Using forensic verification helps protect both the claim outcome and the integrity of the claims investigations process. Below are practical steps adjusters can take to validate visual evidence effectively:

1. Request the Original File: Always request the original digital image. Do not accept a screenshot, PDF, or compressed version.
2. Verify Metadata: Confirm that the timestamp, device information, and location data align with the claimant’s reported details.
3. Run a Reverse Image Search: Check whether the image appears in stock photo libraries, social media posts, or other claims or public forums.
4. Confirm Context and Consistency: Did weather conditions match the date and location? Was the policy active at the time? Did the property or vehicle condition align with previous inspections or records?

Claims teams should escalate to forensic review when:

• Metadata is missing, stripped, or inconsistent
• Shadows, reflections, or surface textures look unnatural
• The image closely resembles stock or shared commercial photos
• The claimant cannot provide the original source file

Failing to verify imagery can lead to inflated payouts, increased loss severity, regulatory or bad-faith scrutiny, and reputational and audit risks.

As visual evidence becomes easier to falsify, forensic verification is becoming a standard expectation in modern claims investigations.

Challenges in Claims Investigations

Implementing forensic verification does come with operational and practical challenges:

• Manipulation tools evolve faster than detection tools
  Synthetic imagery is improving rapidly, often outpacing commercially available review software.
• Verification requires process and training
  Adjusters need clear workflows and guidance on when to escalate to specialized forensic review.
• Not every claim warrants the same level of scrutiny
  Deeper verification is necessary depending on circumstances (e.g., high-value claims, disputed liability, missing metadata).
• Privacy and legal compliance must be maintained
  Requests for original files, device logs, or supplemental images must be handled in line with regulatory, evidentiary, and policyholder privacy requirements.

While verification requires structure and consistency, the cost of not verifying is far greater, including increased fraud exposure, inflated payouts, regulatory risk, and erosion of trust in the claims process. In a digital-first environment, forensic verification is an essential safeguard for claims investigations.

Looking to strengthen image verification within your claims investigations? We’re here to help.

Check out our sources:

Auxier, Brooke. Americans and Digital Knowledge. Pew Research Center, 9 Oct. 2019, https://www.pewresearch.org/internet/2019/10/09/americans-and-digital-knowledge/.

Federal Bureau of Investigation. Malicious Actors Use Artificial Intelligence and Machine Learning to Develop Sophisticated Scams. Public Service Announcement, IC3, 26 June 2023, https://www.ic3.gov/Media/Y2023/PSA230626.

Groh, Matthew, et al. “Human Detection of Deepfake Faces.” Proceedings of the National Academy of Sciences, vol. 119, no. 48, 2022, https://doi.org/10.1073/pnas.2211163119.

Nightingale, Samuel J., and Hany Farid. “AI-Synthesized Faces Are Indistinguishable from Real Faces and More Trustworthy.” PNAS, vol. 119, no. 8, 2022, https://doi.org/10.1073/pnas.2120481119.

U.S. Department of Homeland Security. Combating Synthetic Media and Deepfakes. DHS Science & Technology Directorate, 2023, https://www.dhs.gov/science-and-technology.