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.
hr | bluebox: Practical insights into machine learning
Our hr | bluebox service combines the best of human data analytical skills with AI techniques to enable insurance companies to identify potential poor risk in advance. In this edition of ReCent Actuarial News, Lukas Herrmann dives into algorithms – and in particular the Classification and Regression Tree (CART), to explain its role in predicting early policy lapse characteristics:
ReCent Actuarial News
hr | bluebox: Practical insights to machine learning
Insightful visualisations: Example
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
Insightful visualisations: How-to
In the insurance industry, we are bombarded with data and making sense of it can be challenging. Visualising data is very helpful – but how can we use charts and graphics to ensure we achieve our intended outcome?
As information becomes more available and more detailed and audiences become more sophisticated and more demanding, it is becoming increasingly important to ensure we think carefully about how to present data.
For insights into effective data presentation, see this outline of a presentation from two of our data analytics specialists at the Australian Actuaries Institute Virtual Insights Session. It gives helpful tips on what you can do to present your information in a more meaningful fashion, as well as things you may want to think about when you are at the receiving end of a graphic presentation. These include:
- What type of chart works best for your audience and your data?
- Who is your audience? Do you need it for your own purposes, for a specialist team or for a general audience?
- What is the purpose of the information? For comparisons? Observations? Discovery? Convincing people?
Some important points to think about are the precise use of language in titles, descriptions and annotations. How should you present the data in context? The range of data presented can significantly alter people’s perceptions of its relevance. The choice of colours used can make the information easier to pick out and regions and countries have differing cultural expectations linked to colour.
Current approaches to machine learning pay an important contribution to the success of your business. In a key takeaway from our talk, our two experts discuss state-of-the-art visualisations of predictive models, as part of your toolset for convincing decision makers.
Creating effective visualisations for descriptive and predictive analytics (Outline)
If you have any questions, our data analytics specialists will be delighted to discuss how you can take data visualisations to the next level.
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.
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.