What Claims Adjusters Need to Know About Deepfakes in 2026

This article is written for you — the claims adjuster — not your IT department.

You’re the front line. Every photo, video, and document you review is a potential fraud vector. And the tools available to fraudsters have changed fundamentally in the past two years. Generative AI now lets anyone produce convincing fake images, alter videos, clone voices, and forge documents with free software and zero technical skill.

You don’t need to become a deepfake expert. You need to know what to watch for, when to be suspicious, and what to do about it.

What’s Changed — And Why It Matters to You

Until recently, fabricating convincing evidence required effort: physical staging, professional photo editing, document forgery skills. These barriers meant that most fraud attempts left detectable traces — inconsistent quality, obvious editing marks, or simple implausibility.

Generative AI has removed those barriers. Here’s what that means in practice:

A photo of vehicle damage can be generated from a text prompt in under 30 seconds. No damaged vehicle required.
A genuine photo of minor damage can be altered to show catastrophic damage — with the manipulation invisible to the naked eye.
A voice can be cloned from a 10-second audio sample — a voicemail greeting is enough — and used to authorize claims by phone.
Medical records, police reports, and repair estimates can be generated by AI that produces grammatically correct, institutionally formatted documents.

The Sumsub 2024 Identity Fraud Report documented that global identity fraud rates more than doubled between 2021 and 2024, rising from 1.10% to 2.50%, with AI-generated deepfakes identified as the primary driver. This is not a future threat — it’s today’s reality.

Red Flags in Submitted Photos

You review photos every day. Here’s what should make you pause.

Too Perfect

Real claims photos are messy. They’re taken by stressed policyholders on smartphones, often in poor lighting, at awkward angles, with fingers partially over the lens. They’re imperfect because the situation was imperfect.

Be suspicious of photos that are:

Unusually well-composed — perfectly centred, well-lit, professionally framed damage shots
Uniformly sharp — no motion blur, no focus issues, no compression artifacts
Aesthetically consistent — lighting and color tone identical across all submitted photos, as if generated by the same model rather than taken at different times

Inconsistent Details

Look for elements that don’t match:

Shadows going different directions in the same photo
Reflections that don’t match the environment — a car’s paint showing a sunny sky when the rest of the photo shows overcast conditions
Text that doesn’t quite work — license plates, street signs, or watermarks with slightly garbled letters (a common AI generation artifact)
Damage patterns that defy physics — crumple damage inconsistent with the claimed impact direction, or water damage that ignores gravity

Background Anomalies

AI-generated images often get the main subject right but struggle with backgrounds:

Warped or melted details in the periphery — fences, trees, or buildings that look slightly distorted
Repeating patterns — identical leaves, identical bricks, or identical cracks that tile rather than vary naturally
Missing expected elements — a residential street with no power lines, a parking lot with no markings

Metadata Gaps

This requires a quick technical check, but it’s worth doing:

Right-click the image file and check its properties. Genuine smartphone photos contain EXIF data: camera model, timestamp, GPS location. AI-generated images typically lack this data entirely, or contain generic placeholders.
Does the camera model exist? If EXIF data names a camera, verify it’s a real consumer device.
Does the timestamp match the incident date? Even a one-day discrepancy warrants a follow-up question.

Red Flags in Submitted Video

Video deepfakes are harder to produce than image deepfakes, but they’re improving rapidly.

Frame-by-Frame Inconsistencies

If you have the ability to step through video frame by frame (most media players support this with arrow keys when paused):

Watch for flickering or sudden changes in lighting, color, or object position
Look for objects that briefly appear or disappear between frames
Check whether damage is consistent throughout — if a dent appears slightly different in shape or position as the camera moves, the video may have been manipulated

Audio Mismatches

Does the ambient audio match the visual scene? A video claiming to show a rear-end collision at a busy intersection should have traffic noise, not silence.
Is the audio quality suspiciously different from the video quality? Spliced audio often has different background noise characteristics.

Unnatural Motion

If people appear in the video:

Lip sync issues — speech that doesn’t precisely match mouth movements
Rigid facial expressions — faces that seem slightly frozen or transition between expressions too smoothly
Hair and clothing — AI often struggles with realistic hair movement and fabric physics

Red Flags in Documents

AI-generated documents are perhaps the most underestimated deepfake threat in insurance.

Formatting Tells

Slightly wrong logos — institutional logos that are close but not quite right (wrong proportions, incorrect colors, missing fine details)
Inconsistent fonts — mixing fonts within a document in ways a genuine institutional template wouldn’t
Generic reference numbers — numbers that look plausible but don’t follow the issuing institution’s known format

Content Issues

Overly perfect language — genuine police reports and medical records contain shorthand, abbreviations, and occasionally errors. AI-generated text tends to be more polished and formal than real institutional writing.
Factual consistency — do details in the document (dates, names, addresses, incident descriptions) match what the claimant has stated? Cross-reference every detail.
Contact verification — call the institution listed on the document (using a number you find independently, not the one printed on the document) and verify it was issued.

When to Escalate

You don’t need to prove a deepfake. You need to know when something warrants a closer look. Here’s a practical decision framework.

Escalate Immediately If:

Metadata is missing or inconsistent — no EXIF data, timestamps that don’t match, GPS that places the photo in a different city
Reverse image search returns matches — the same or similar image appears elsewhere online or in your claims database
Multiple red flags appear together — any single red flag might be explainable; three or more together are a pattern
The claim value is high relative to the evidence quality — a total-loss claim supported by only two or three photos deserves scrutiny
Your instinct says something is off — experienced adjusters develop a sense for claims that don’t feel right. Trust that instinct and document the specifics that triggered it.

How to Escalate

Preserve the original files. Do not open, edit, or re-save submitted media. Digital forensic analysis requires the original files exactly as received.
Document your observations. Note specific red flags — “shadow direction inconsistent in photo 3,” “EXIF data shows creation date two days before claimed incident,” etc.
Flag in your claims system. Use whatever internal mechanism your organization provides to flag claims for SIU review.
Do not alert the claimant. If a claim is genuinely fraudulent, tipping off the claimant allows them to withdraw the claim, destroy evidence, or adjust their approach.

How AI Detection Tools Help You

AI-powered deepfake detection isn’t a replacement for adjuster expertise — it’s a force multiplier.

What Detection Tools Do

Pixel-level analysis — examining images at a level of detail no human can achieve, identifying statistical signatures left by AI generation models
Frequency domain analysis — detecting patterns in the mathematical structure of images that distinguish real photographs from generated or manipulated ones
Provenance checking — verifying the creation history of a media file to identify editing, re-encoding, or injection
Batch processing — analyzing every photo on every claim, not just the ones that look suspicious, catching manipulation that might not trigger visual red flags

What You Get

Good detection tools provide actionable output, not just a score:

Confidence level — how likely is it that the media has been manipulated?
Manipulation heatmaps — visual overlays showing exactly which regions of an image were identified as potentially altered
Forensic reports — detailed documentation suitable for SIU investigation and legal proceedings

At deetech, we designed our output specifically for claims workflows — results you can act on, not academic papers you need a PhD to interpret.

How It Fits Your Workflow

The best detection tools work in the background:

A claim is submitted with photos/videos/documents
Media is automatically analyzed before it reaches your desk
Clean media passes through normally — no delay, no extra work for you
Flagged media arrives with an alert and a forensic summary
You review the alert, add your own observations, and decide whether to escalate

This means you spend your time on claims that need human judgment, not manually screening every photo for manipulation.

Practical Steps You Can Take Today

You don’t need to wait for your organization to deploy enterprise detection tools. Here’s what you can do right now:

Check metadata on every suspicious claim. Right-click → Properties (Windows) or Get Info (Mac) on image files. Look for camera model, creation date, and GPS data.
Use reverse image search. Google Images, TinEye, or Yandex Images can reveal recycled photos in seconds.
Zoom in. View submitted photos at 200-400% magnification. Manipulation artifacts invisible at normal zoom often become apparent at higher magnification.
Request additional evidence. If you’re suspicious, ask for photos from different angles, photos with a specific object placed next to the damage (a pen, a business card), or a brief video walkthrough. Each additional request increases the cost and complexity for a fraudster.
Cross-reference everything. Check dates against weather records. Verify documents with issuing institutions. Look up repair shops and medical providers. Run the claimant’s history in your database.
Document your suspicions. Even if a claim ultimately pays out, recording your observations builds a data set that helps identify patterns over time.

The Adjuster’s Advantage

Here’s the thing about deepfake fraud: it’s optimized to fool automated systems and casual review. It’s not optimized to fool an experienced claims adjuster who asks the right questions.

A deepfake image can look perfect. But it can’t answer follow-up questions. It can’t provide a different angle on demand. It can’t explain why the metadata doesn’t match. It can’t produce a consistent story under probing.

Your expertise — the intuition built from thousands of claims, the ability to spot a story that doesn’t add up, the professional scepticism that comes with experience — remains your most valuable tool. Technology augments that expertise. It doesn’t replace it.

The adjusters who combine their experience with an understanding of the new threat landscape — and the tools to address it — will be the ones who catch the fraud that others miss.

deetech builds deepfake detection for insurance claims professionals. Our platform analyses photos, videos, and documents automatically and delivers clear, actionable forensic reports integrated into your claims workflow. Request a demo.

Sources cited in this article:

Sumsub 2024 Identity Fraud Report