When AI Fakes the Evidence: How VIEWAPP Keeps Insurance Inspections Fraud-Proof

Insurance fraud is a global process that changes faster than insurers' verification procedures can keep up with. According to the Coalition Against Insurance Fraud, losses from insurance fraud in the United States alone exceed $300 billion a year across all lines of business — from property and auto to health and life insurance. The National Insurance Crime Bureau (NICB) has for years documented that signs of fraud are present in roughly one in ten settled property and auto claims.

What is changing is not the scale of the problem but its nature. Fabricating evidence of a loss used to take time, skill, and accomplices — staging an accident or forging a photograph was expensive and risky. Today, all it takes is a smartphone and one of dozens of readily available generative AI tools.

Photos and Video Are No Longer Proof

According to Verisk's 2026 research, 98% of surveyed insurers agree that AI-based editing tools are fueling the rise in digital fraud, and 99% have already encountered altered or generated materials submitted as part of a claim. The same study found that 36% of surveyed consumers admitted they would be willing to attach a digitally altered photo to a claim — many do not perceive this as a serious violation, unlike staging an accident outright.

A scheme examined by the UK market back in 2023–2024 became a telling example: a claimant took a photo of their own car from social media and used a generative editor to add a realistic-looking bumper dent, then submitted the image along with a fabricated repair invoice. The insurer grew suspicious and found the original, undamaged photo publicly available online — that was the only thing that exposed the fraud. Investigations don't always end so successfully: in early 2026, the law firm Debevoise & Plimpton noted that insurers routinely make claim decisions based on images — photos of damage to property or vehicles — while today's image-generation models make such photos indistinguishable from genuine ones under visual review.

Why Standard Verification No Longer Works

The core problem is that AI-generated images and documents pass traditional control mechanisms. According to Hesper AI, such forgeries successfully clear classic OCR- and rule-based checks in more than 90% of cases: the text on a document is internally consistent, and the metadata is clean, because modern generative tools produce correct EXIF data without any manual effort. A human reviewer often misses the forgery on a quick pass as well — especially as the industry simultaneously moves toward "touchless" claims settlement with minimal human involvement.

At the same time, investigating every suspicious case is simply not physically possible: per the same data, up to 75% of claims flagged as suspicious never receive a full investigation — largely because a single SIU (Special Investigation Unit) analyst handles more than 200 active cases on average, and a thorough investigation of one case takes one to two weeks.

How VIEWAPP Is Built to Address This

VIEWAPP was designed from the outset as a remote inspection tool, not as an app for uploading arbitrary photos — and that is the fundamental difference from schemes where an insurer simply asks a customer to send a picture over messenger or a web form.

An inspection in VIEWAPP follows a predefined scenario: the app guides the user step by step through the required angles and objects to photograph, rather than leaving it up to them to decide what and how to shoot. Every photo and video is captured directly within the app during the scenario and is accompanied by metadata — geolocation, timestamp, device, and shooting sequence — which is protected against later tampering through checksums that verify file integrity. Dedicated detectors check whether a shot is a re-photograph of another screen or image, screening out attempts to slip a ready-made picture from the gallery or the internet into the scenario.

From there, automated analysis takes over: neural networks assess image quality and authenticity, check the inspector's GPS track and route for anomalies, and computer vision algorithms detect damage to the object. These models are continuously retrained on new labeled data and feedback, allowing the system to adapt to new deception schemes faster than individual insurers can update their own procedures. Once the inspection is complete, a final report is generated with a digital signature that locks in the result and rules out retroactive editing.

For complex objects — commercial real estate, specialized equipment, industrial sites — VIEWAPP allows a single scenario to be split across several inspectors working simultaneously: one checks the production line, another the warehouse, a third the building perimeter. For an insurer, this means not only speed but an added layer of reliability: the result is built from several independent data sources rather than a single set of photos submitted by one person with no control over how they were taken.

What This Changes for Insurers

No technology makes fraud impossible in principle — the goal is to raise its cost and reduce its effectiveness to the point where large-scale schemes stop paying off. The difference between "send us photos of the damage" and a structured, scenario-based inspection with data integrity controls is that in the second case, a fraudster simply has no point of entry for a ready-made forgery: the scenario leaves no room for uploading an outside image, and the metadata and digital signature form a chain of evidence suitable for later verification — not a single artifact that has to be taken on faith.

For markets where claims settlement is increasingly moving to a remote, automated format — from retail auto and property insurance to corporate policies covering assets spread across regions — controlling the data collection stage stops being an optional add-on and becomes a precondition for treating the digitization of inspections as safe at all.

Data sources: Coalition Against Insurance Fraud, National Insurance Crime Bureau (NICB), Verisk 2026 State of Insurance Fraud Report, Hesper AI analysis (2026), Debevoise & Plimpton (January 2026), reporting on the Arup case (Hong Kong) as covered by cybersecurity industry publications.