AI-edited insurance photos: what to check before a claim moves forward

The problem is already in the claims queue

Aviva’s June 2026 update describes rising use of AI images in claims. Its £233m figure covers suspected claims fraud across Aviva and Direct Line in 2025, not AI-image fraud alone.

Consider a review scenario: a customer submits three photographs of a damaged vehicle. Two are wide shots; one shows a deep dent. The file could be genuine, locally edited or a real photograph from another incident. Those possibilities need different follow-up. A single ‘AI or real’ label does not settle them.

For claims software providers, the useful place to start is the upload step. Preserve what arrived, check the selected media and attach the findings to the existing case. The reviewer should not need to copy files between tools or reconstruct which result belongs to which photograph.

• Aviva’s June 2026 claims fraud update

Ask what the photograph is meant to establish

Separate three questions. Was the file generated or altered? Does it depict the insured object and the reported incident? Does the claim meet the insurer’s conditions? An image detector contributes to the first question. It cannot answer the other two from pixels alone.

An unedited photo can be reused with a false date or caption. A legitimate customer may also submit an enhanced, compressed or cropped image. Treat each observation as a reason to check a specific fact. For example, request another angle of the same panel or compare the location of the damage across the submitted set.

Keep a traceable copy of the evidence

Retain the original submission in the claims system under your approved access and retention rules. Record a file reference, receipt time and hash where your system supports it. Use separate copies for annotations and resizing. This makes it possible to show exactly which file was examined, even when a customer later supplies a replacement.

C2PA Content Credentials can provide signed provenance information. They may be stripped from a file, so missing credentials are not evidence of fraud. Record the actual validation result and any limits.

Do not upload entire claim folders when only a few photographs need analysis. Select the relevant files and exclude unrelated identity documents, correspondence and payment details.

• C2PA: provenance and missing credentials

Put the API result beside the case, not in a separate inbox

With DeepfakePolicy, a backend can submit a photo or video job and retrieve the completed report by polling or webhook. Store the returned report identifier next to your own file reference. Keep an explicit pending or failed state: a timeout is not a clean result.

A useful review screen shows the detector finding, available file information, interpretation and limitations together. Keep conflicting signals visible. A plausible visual description must not erase a strong detector finding, and an elevated score must not become an automatic rejection.

For a first evaluation, use a selected batch and read the reports file by file. For an integration, start with sandbox fixtures to test the request and response flow, then connect company billing before processing real files. Sandbox output is example data; it does not measure detection quality.

• Photo and video API quickstart
• Insurance integration workflow
• API and batch pricing

Measure the cost of the review, not just the score

Build a test set that resembles your actual submissions: genuine phone photos, ordinary edits, recompressed files and known synthetic examples. Keep a separate group out of threshold selection. Otherwise, a promising result may simply reflect the files you tuned the workflow on.

Track how many genuine files are flagged, how many known manipulations are missed, how often a result is inconclusive and how long reviewers spend resolving each flag. Compare the cost per useful escalation with the process you already use. A detector that raises too many weak alerts can make the queue slower.

• Record the file types and transformations included in the evaluation.
• Separate false alerts, missed edits and unavailable results.
• Review disagreement between detector findings and other evidence.
• Keep the final claims decision with the authorised reviewer.

Give the next reviewer an understandable record

The report should say what was submitted, when it was analysed, what the check found and what still needs verification. DeepfakePolicy’s detailed PDF can carry that record beyond the dashboard; the API gives your software structured findings to work with.

For a claims team, that is the commercial value: a repeatable check at the point where unfamiliar media enters the process, with enough explanation to decide the next step. The report is an analysis record, not a determination of liability or a signed expert opinion.