Digital evidence in insurance: how to protect the claims process from AI manipulation

Digital evidence is the backbone of every insurance claims decision. Each claim generates a flow of photos, videos, documents, and communications that pass through multiple parties: from the policyholder to the adjuster, from the loss assessor to the repair network. Damage assessments, investigations, and final payout decisions all rest on this data.

The problem is that this digital content no longer carries any inherent guarantee of authenticity. The Verisk report published in March 2026 paints a picture that insurers can no longer afford to dismiss: one in three consumers would consider digitally altering claim images to strengthen their case. Among Generation Z, that figure climbs to 55%. Meanwhile, 76% of insurers acknowledge that manipulated media submissions have grown more sophisticated. The global cost of insurance fraud stands at roughly $308 billion per year.

The answer is not to chase every fake with detection tools. It is to certify the authenticity of evidence at the moment of capture, using a forensic methodology that guarantees integrity, timestamp, and chain of custody.

AI insurance fraud: the numbers behind a growing problem

The Verisk March 2026 report: what consumers and insurers are saying

The Verisk data leaves little room for ambiguity. 98% of insurers agree that AI-powered editing tools are driving an increase in digital document fraud. 99% report having already encountered manipulated or AI-altered documentation in their claims processes.

On the consumer side, the threshold of what people consider acceptable has shifted. 52% see adjusting brightness and contrast of a claim photo as acceptable. 41% consider flipping or repairing a blurry image to be fine. 39% accept cropping out background elements. But the numbers that matter most are these: 15% consider it acceptable to exaggerate visible damage, and 13% think it is reasonable to create a photo of damage that never happened.

$308 billion: the global cost of insurance fraud

Insurance fraud is not a new problem. It is a problem that has changed scale. 2026 projections put the global cost at $308 billion, driven by the accessibility of AI editing and generation tools that lower the technical barriers to manipulation.

In the first four months of 2025, deepfake-related incidents rose 19% compared to all of 2024. 46% of anti-fraud specialists have already encountered scams based on artificially generated identities and images, with an average loss per incident exceeding $280,000.

Why detection is not enough: the limits of post-hoc detection

The arms race between generation and detection

The dominant approach in insurance remains reactive: detect manipulated content after it has been submitted. 65% of carriers use third-party AI detection tools, 50% have developed in-house solutions, and 44% still rely on manual review.

The underlying problem is asymmetric. Every improvement in detection tools gets quickly neutralized by parallel improvements in generation techniques. The person creating a fake needs to get it right once; the person detecting it needs to be right every time. Verisk's findings confirm this imbalance: only 32% of insurers feel confident they can identify a deepfake, and just 43% trust their ability to assess media authenticity at scale.

The scale problem: millions of claims, limited resources

An insurance carrier handles hundreds of thousands of claims per year. Sometimes millions. Each claim produces dozens of digital files. Manual detection is impractical at this volume, and automated detection inevitably generates false positives and false negatives that slow down the process and increase operational costs.

48% of insurers expect greater adoption of technology solutions to fight fraud, but 36% also anticipate increased operational strain on claims teams, 35% expect longer settlement times, and another 35% foresee higher premiums for consumers. Detection alone does not scale without side effects.

The paradigm shift: certifying evidence at the source

From reactive detection to proactive certification

The alternative paradigm flips the approach. Instead of searching for fakes after they have been produced, you certify the authentic content at the moment it is captured. Every photo taken at the scene of the loss, every video of the damage, every supporting document receives an authenticity guarantee at the exact moment of acquisition.

The difference is measurable. Detection produces a probability: "this image has a 73% chance of having been modified." Source certification produces a certainty: "this image was captured at this location, at this time, on this device, and has not been altered since." When claims decisions are worth hundreds of thousands of euros, the gap between probability and certainty is the gap between a vulnerable process and a reliable one.

Technical requirements: integrity, timestamp, chain of custody

Source certification rests on three technical requirements. Data integrity, verifiable through a digital seal, ensures the content has not been altered after capture. A qualified timestamp, issued by a recognized certification authority, provides legal proof of when the content was acquired. Chain of custody ensures full traceability from the moment of capture to deposit, including contextual metadata such as geolocation and author identity.

When these requirements are met together, the resulting digital evidence is admissible and enforceable in legal proceedings. It complies with the eIDAS Regulation on the validity of electronic documents and the ISO/IEC 27037 standard for digital evidence management.

How forensic certification of insurance evidence works

Certified capture at the scene of the loss

The claims certification process starts where it matters most: at the point of first evidence collection. The policyholder, the loss adjuster, or the network operator captures photos, videos, and documents through a certification application. At the moment of capture, the system automatically runs integrity checks and locks contextual metadata. Date, time, GPS position, and author identity become immutable.

Compare this with the traditional flow. A photo taken on a smartphone and sent via email or WhatsApp can lose metadata during transfer. There is no guarantee that the image the claims handler receives is identical to the one taken on-site. In the certified flow, the data is protected from the start.

Digital seal and qualified timestamp

Each captured piece of content receives a digital seal from the certification provider and a qualified timestamp issued by a qualified trust service provider under the eIDAS Regulation. The seal guarantees the content has not been modified; the timestamp legally proves the exact moment of capture.

The result is a claims file where the authenticity of each piece of evidence does not depend on trusting whoever collected it, but on technical and legal guarantees that anyone can verify at any time.

Regulatory compliance: eIDAS, ISO 27037, GDPR

The forensic certification of insurance evidence fits within a clear regulatory framework. The eIDAS Regulation establishes the legal value of electronic seals and qualified timestamps across the European Union. ISO/IEC 27037 defines guidelines for identifying, collecting, acquiring, and preserving digital evidence. GDPR governs the processing of personal data contained in claims file evidence.

TrueScreen in insurance: certifying digital evidence at the source

How TrueScreen protects the claims process

TrueScreen fits into the claims management process as a AI agent data certification layer. Every party involved in a claim, from the policyholder to the adjuster, from the settlement team to the repair network, can capture certified content through the mobile app, the web platform, or via API/SDK integration with the carrier's own systems.

The workflow is straightforward. The policyholder opens the claim and certifies the first evidence: photos of the damage, documents, statements. Certified inspections add further evidence during on-site assessments. The anti-fraud team works with a file composed of data whose provenance and integrity are guaranteed, reducing false positives. Settlement is based on a complete, auditable file ready for potential litigation.

The digital provenance of every piece of content in the file is traceable and verifiable, from the first report to the closure of the case.

Operational benefits: time, costs, disputes

Native evidence certification produces concrete results. Requests for supplementary documentation decrease when every piece of evidence already comes with verifiable metadata, and claims handling times shorten accordingly. Fraud risk drops because a file certified at the source leaves little room for manipulation. Challenges to evidence validity become rare when every piece of content is backed by a digital seal and qualified timestamp, with direct effects on legal costs.

For risk managers and claims leaders, the strategic benefit is demonstrable compliance with anti-fraud and documentation obligations set by regulatory frameworks, without adding operational complexity to the existing process.