Data are the foundation of our business. An in-depth understanding of data enables us to appreciate the risks being taken, to price and select profitable business accordingly and to actively manage our portfolios. Data analytics contributes to the sound decision-making of the Hannover Re Group, our partners and our clients, ready to shape the future of the industry, develop data-driven strategies and to provide risk-adequate offerings at a fair price to consumers.

Data analytics encompasses the systematic onboarding and preparation of data, as well as processing and modelling. State-of-the-art IT systems enable us to systematically store and prepare data, enrich it with additional information. They also facilitate a multitude of methods of descriptive and predictive analysis, and their efficient use from product development and pricing to experience monitoring and risk management. As data collection and onboarding are the most labour-intensive steps in any data analysis, automation has become key in improving this process, freeing us from tedious routine and allowing us to focus on the challenges of contextualising the results, and deriving meaningful and actionable insights to discuss and share across all disciplines within the Hannover Re Group.

Throughout this process, we make use of all the recent innovations of data science. However, for us, the tools and algorithms, predictive models and machine learning methods never are an end unto themselves. Rather, we focus on finding the best solution for the problem at hand to improve our actual understanding of the risks taken together with our partners.

We support our clients in drawing the right conclusions from their data to form an improved understanding of consumer behaviour and motivation. Consequently, our data analysts work hand-in-hand with the business development, market representatives, valuation and risk management teams at Hannover Re to identify risks and opportunities for our clients.

Insightful visualisations

The life expectancies of population subgroups differ: Statistically speaking, women live longer than men, and non-smokers live longer than smokers. Another frequently discussed predictor for life expectancy is an individual’s financial situation. A close look at the data however suggests that personal finances are not such an optimal predictor after all. Other socio-economic factors such as education or lifestyle (including e.g. smoking and nutrition) can show a much clearer correlation with life expectancy. The following visualisations, which mirror observations we regularly make in our business, give a clear indication that the average socio-economic status of a region’s population shows a much stronger correlation to average life expectancy than the average per-capita GDP. Fair (re)insurance offerings for pension funds and sound risk management are based on improved understanding of such interdependencies.

GDP per capita (in GBP)

Residual life expectancy at 65 years

Average socio-economic status

Useful tools

We use our expertise in data analytics to support partners around the globe. In this project, we supported an Australian partner setting up a life expectancy calculator for their internet presence.

Innovative solutions

There are many great tools for specific data analytic tasks. Thanks to our start-up network hr | equarium, we can connect you with many ready-to-use innovations in the market.


We team up with you on your data analytics journey. We create insightful visualisations, provide useful tools and introduce you to innovative data analytics solutions.