There are those trends in the world of insurance that develop quietly, stay under the radar, and only become apparent when it is really too late. "Template farms" is one such phenomenon. A term that may not yet be familiar to everyone, but which is causing considerable headaches behind the scenes at claims departments, fraud investigators and data analysts.
In this article I take you into a world where fraud is becoming increasingly sophisticated, thanks to technology. But also how that same technology — and AI in particular — can help us fight back more effectively. Not a doom story, but a wake-up call with an optimistic undertone.
What are template farms exactly?
Imagine a digital farm, but instead of cows or potatoes, documents are produced. Standard documents. Templates. Always slightly different, but with the same core.
A template farm is nothing more than an organised collection of pre-filled documents used to submit fraudulent claims to insurers. Think of:
- Damage forms with the same descriptions every time ("windscreen hit by a stone thrown up during a motorway journey").
- Medical reports from non-existent clinics.
- Invoices from garage companies that exist only on paper.
These templates are often reused, adjusted, and resubmitted under different names, different addresses, or even under synthetic identities (combinations of real and fake data).
It is becoming more and more common. Not only in the major cities or via classic criminal networks. No, also among the self-employed, students, or clever young people who spend an afternoon combining AI tools with a Photoshop course. It seems harmless. "The insurance company can afford it." But cumulatively the damage runs into the millions.
Why this is a growing problem
The danger lies not only in the fraud itself, but in its scalability and speed. Thanks to AI and automation, fraudsters today can generate thousands of variants of a claim with minimal effort. Where previously one fraudster submitted five claims per month, hundreds can now be submitted simultaneously at the touch of a button.
Moreover, the amounts are often low enough to stay under the radar. €800 for a screen repair. €1,250 for a stolen electric bicycle. Claims of that nature often receive automated processing. That is precisely what fraudsters are targeting.
A familiar pattern:
- Multiple claims through different insurers.
- Slightly different data, but exactly the same damage description.
- Manipulated invoices and forged signatures.
- Use of AI to make language and imagery appear realistic.
The role of AI: blessing or curse?
Let us be honest. The rise of AI makes this form of fraud possible on a scale that was unthinkable five years ago. ChatGPT, Claude, Google Gemini, ElevenLabs — you can use them to generate convincing damage statements, simulate fake phone calls, even fabricate email exchanges between customers and 'repair companies'.
But the story does not end there. Because AI is not only the fraudster's weapon. It is also the insurer's shield. Provided we deploy it correctly.
How AI can actually help combat fraud
AI also offers us a number of powerful tools to tackle this type of sophisticated fraud. Below are a number of practical examples:
1. Text recognition and pattern analysis
AI models can scan tens of thousands of claims documents for recurring patterns. If a particular sentence structure appears suspiciously often ("whilst parking I heard a bang..."), a flag is raised. Not with certainty that it is fraud, but as a signal to investigate.
2. Image verification with machine vision
A claimed photo of vehicle damage can be analysed for traces of Photoshop editing, reuse of stock photos, or unrealistic lighting. AI sees things the human eye does not.
3. Voiceprint matching and transcription analysis
In telephone claims notifications, voice and language use can be analysed to detect whether the same person is posing as multiple customers. This is still in its early stages, but promising.
4. Behavioural analysis of the customer journey
AI can track customer behaviour patterns — from application to claim — and flag anomalies. For example: someone who claims damage within 24 hours of taking out a policy, who provides all documents at the touch of a button, and who is never heard from again afterwards.
From control to culture
Technology is a tool. But the core remains: culture and ethics. Within your organisation, you need to have the conversation about what is responsible. Fraud management is not an IT issue — it is an integral part of customer strategy, risk management and brand trust.
The fight against template farms does not start with software, but with awareness. And ends with action.