Pilot#12 – Real World Data for Novel Health-Insurance products

11th December 2019, Giorgios Marinos, Singular Logic, Greece

In the era of digital disruption, services and products tend to be customized on meritocracy for the citizens, e.g. actual usage, exposure to risks, etc. At many industries, this has been already introduced e.g. financial industry personalized financial services, banking etc. Also, in Insurance industry for vehicle insurance, with various products based on IoT measurements. With a view to personalized services this pilot will aim to develop an accurate risk assessment tool that potentially will be used in Life Insurance Industry.

We daily produce a huge amount of unexploited but potentially very useful data e.g. activity tracking data, data produced by applications etc. This pilot (pilot #12) will demonstrate how Real-World Data can be utilized from Insurance companies to result in novel, more personalized insurance products that fit better customer needs. This approach will lead in the future to a win – win case between the insurer and the customer.

Healthentia, the e-clinical platform, will be used as a Real-World Data capturing infrastructure that will collect a various amount of data (such as activity data, demographics etc.). These data will be analyzed using novel machine learning/ deep learning approaches in order to stratify the risk level of users with respect to data privacy. Each user will be categorized according to his habits, measurements and needs. According to this approach both the insurer and the customer will benefit. Insurers will have a more accurate estimation of customer risk level and will not overestimate/underestimate customer’s undertaken risk. On the other side customers will be motivated to have better more healthy habits and increase their life expectancy.

We expect this outcome assist insurance market to offer new products that take into consideration the risk related to the health of the users.


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logo europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 856632.
The content reflects only the authors’ views, and the European Commission is not responsible for any use that may be made of the information it contains.

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