Insurance Fraud: The Industrialization of Fake Claims by AI

Insurance fraud: the industrialization of fake claims by AI

What really happens when a policyholder sends photos

A policyholder sends three photos of their damaged vehicle via the company's mobile app. Dented bumper, paint chips, consistent shooting angle. The file enters the automated process. The payout is approved within minutes.

Problem: the damage was added digitally. The vehicle never had a single scratch.

This scenario is not a working hypothesis. It is a documented reality, made possible by tools accessible to anyone with a smartphone.

The real scale of the problem

What the numbers reveal

On average, nearly 10% of claims reported in property and casualty insurance contain fraudulent elements. [Shift Technology — Insurance Perspectives: The Fraudulent Image Analysis Edition (2024)]

In France, €902 million in insurance fraud was identified in 2024. [Agence de Lutte contre la Fraude à l'Assurance (ALFA) — Rapport annuel fraude à l'assurance (2024)]

Of these amounts, €656 million concern the P&C line — Fire, Accidents, Miscellaneous Risks — mainly linked to material claims. [ALFA — Données sectorielles fraude à l'assurance (2024)]

These figures represent only detected fraud. Actual losses are structurally higher.

Why insurance is a prime target

Three factors combine to enable visual fraud:

Decision automation speeds up flows without strengthening controls. Many insurers have adopted the Straight-Through Processing (STP)Automated process that handles a case end-to-end without human intervention, from declaration to payment. to reduce delays and operating costs. Result: a manipulated image can pass through every filter without any human eye examining it.

Falsification tools are now accessible to the general public. Smartphone apps make it possible to add realistic damage to a photo, modify a scene, or insert non-existent visual elements. Some integrate deepfakeVisual or audio content generated or modified by artificial intelligence to imitate a reality that does not exist.. Visual fraud has become industrializable with no particular technical skill.

Organized fraud networks reuse the same images. A photo of real damage can circulate in several files simultaneously, submitted by different individuals to different insurers. Some analysis databases already list more than 150 million photos examined to identify these reuses. [Verisk — Digital Media Forensics (2024)]

Why current controls are no longer sufficient

Insurance companies still rely mainly on two mechanisms: human review and traditional document-processing tools.

These two approaches share the same fundamental limit.

A claims analyst who reviews hundreds of files per day cannot detect manipulations invisible to the naked eye. Visual fatigue is not a human failing — it is an incompressible physiological constraint.

Tools such as OCRTechnology that automatically extracts text from an image or a scanned document. or document management systems read the content of an image. They do not verify its authenticity. These are two radically different operations.

Legal and compliance framework: what matters most

The legal consequences of a forged document always depend on the facts, the sector involved, the applicable qualification, and the competent jurisdiction. In practice, the main issue for an organization is to be able to demonstrate a proportionate, traceable, and well-documented verification process, with human review whenever a decision may have a significant effect.

The controls described here should therefore be understood as risk-management, compliance, and evidence-preservation measures. Any final blocking decision, report, contractual sanction, or legal action should still be validated by the relevant legal or compliance teams.

Frequently asked questions

Does the system work with images from smartphones?

Yes. The engine analyzes images from smartphones, digital cameras, or messaging apps, in all common formats. Compressed or resized images remain analyzable. The goal is to identify the technical traces left by manipulation operations.

What is the difference with a traditional OCR or document tool?

An OCRTechnology that converts an image or a document into text usable by an information system. tool or a document management system extracts the content of an image. It does not verify its authenticity. DeepForgery analyzes the technical structure of the file to detect modifications made after the shot. These are complementary functions, not substitutes.

Does integration disrupt existing systems?

No. The analysis integrates into existing claims processing flows. Images are analyzed automatically upon receipt. No change to business processes is required.

What your teams gain in concrete terms

Reduced payouts based on falsified evidence

Automated detection at scale, without overloading claims teams

Technical evidence usable in internal audits and legal proceedings

Documented traceability of anti-fraud decisions, compliant with regulatory requirements

Conclusion

Visual fraud in claims is no longer a marginal phenomenon. Manipulated images have become realistic enough to deceive human controls and pass through automated filters.

At the same time, regulatory requirements on insurers are tightening: decision traceability, GDPR compliance, and risk-control obligations.

When a falsified image is detected before a claim is paid, the fraud stops there. It does not become a financial loss, a dispute, or a precedent exploitable by others.