Think of contextual AI in the insurance sector, and your mind has a tendency to go straight to the automotive and perhaps home insurance markets. These, after all, are the types of insurance most commonly associated with fraudulent claims. However, insurance fraud goes way beyond these specific silos and Blockclaim’s software solutions similarly go beyond fraud detection and prevention.

This is plainly demonstrated in the latest pilot project, in which Blockclaim is working alongside one of Europe’s giants in risk consultancy and medical insurance to implement contextual AI solutions. For this particular case, the technology will be used to automatically match treatment and medication with patient conditions and illnesses.

More data means better results

Contextual AI seeks to enrich insurance claims data by using relevant contextual data to increase the information that Machine Learning algorithms use in order to learn. It is literally a training process, and the more they study, the better and more accurate they become at what they do.

It creates a virtuous circle, a little like a human in some specialist sphere, who inevitably gets better at what he or she does with practice and experience. The ultimate outcome is more accurate predictive performance compared with the conventional AI solutions currently in place.

To get the training process underway, the client has provided Blockclaim with more than 10,000 historical data entries, each of which has information on medical conditions and the medications prescribed. We have then been able to add further enrichment and detail to this data.


It goes without saying that automating any part of a medical diagnosis process needs to be approached extremely cautiously and methodically. As things stand at present, there is a standard requirement for the manual approval of any medical condition to illness correlation and that this step must take place three times for each patient before the process can be automated. Yes, that’s three times for every patient.

Extra use possibilities

During a workshop session, it soon became clear that use of Blockclaim’s technology could easily be expanded to bring additional side benefits. These include recognising different variants of similar medications, automation of new medication entries and analysis of both medical conditions and their treatments to bring yet richer data.

Potential savings

With the present level of AI usage, the client is able to automate around 564,000 claims per year. This corresponds to a financial saving of around €285,000. Just by enhancing these processes to contextual AI, the savings could be increased by around 38% to €388,000. However, that is only the beginning.

Further development of contextual AI into a second stage would see the savings increase to more than €1 million. Even this, however, does not take into consideration the extra use possibilities. When they are factored in, the anticipated savings mount to more than €2.3 million per year.

A world of possibilities

Faster and more accurate diagnoses that at the same time result in such significant cost savings represent a win / win for both the industry and the patients it serves. The findings so far represent only the tip of the iceberg in what can be achieved. There are exciting times ahead, and Blockclaim is proud to be at the heart of this innovative development.

Media Contact

Our social media links:


Book a demo with an expert in AI for insurers

Discover how our end-to-end claims automation can boost customer experience and improve operational efficiency.