Fraud in Private Health Insurance – Developments and Countermeasures

We all want to stay healthy—or at least get there. But how, when the healthcare system is already under such strain?

Even in private health insurance, where policyholders typically pay upfront and are reimbursed afterwards, not only genuine healthcare services are costly, but also potential fraud. Not every submitted invoice is genuine. Or more precisely: not every invoice shown in submitted images of invoices is authentic.

The use of AI-generated forgeries and professional templates from fake shops is driving a sharp increase in image-based fraud.

What challenges will private health insurers face in 2026 when it comes to fraud?

- AI tools generate forged documents extremely quickly.  
- Straight-through processing without fraud detection allows even poorly forged documents to pass through easily.  
- Perpetrators (and statistically, to a lesser extent, female perpetrators) repeat their methods once they realise that forged, printed, and poorly photographed documents can be successful.  
- The number of unreported cases in the private health insurance market is very high, making the true cost of fraud difficult to quantify.  

Which solution strategies are effective?

- In 2026, fraud detection must be integrated into insurers’ straight-through processing to distinguish genuine from forged documents and identify fraud more accurately.  
- The ability to analyse hundreds of features per image is essential—fonts, logos, metadata, deviations from reference data, and content plausibility.  
- “Explainable AI” should be prioritised over black-box approaches: how does the fraud detection software arrive at its conclusions?  
- How does the software detect AI-generated documents? For example, by scanning metadata for suffixes such as “Made with #NanoBanana” or by identifying inconsistencies in content and layout—thereby staying one step ahead of AI-generated imagery.  
- It is important to avoid lengthy training phases with sample documents and instead enable immediate deployment.  
- Crucially, the solution must operate in full compliance with data protection requirements for health data (in our case, this has even been confirmed by expert assessment).  

So, which fraud detection software is the right choice for private health insurers in Germany?

Encouragingly, most private health insurers believe it is ours: ICO.Fraud by ICO-LUX is already used by over 65% of private health insurers—including Debeka, HUK-COBURG, Allianz, and many more.

A reason for us to celebrate—for insurers as well, and certainly for honest policyholders. Only the fraudsters are likely to be less pleased.

Image: AI-generated (of course): created with media.io and Nano Banana 2, manually prompted by ICO-LUX