Data Analytics for life insurers

Is your in-house data base too small to achieve valid results or for use with complex models? In that case, Munich Re is the right partner for you. It is also who you should contact if you would like to look beyond your own data horizon, and objectively assess your business and risk potential. Because we can access comprehensive pool results in many markets around the world and use them as the basis to help you find answers to your questions using biometric data. This enables detailed benchmark comparisons, with which you can promptly identify the strengths and weaknesses of your portfolio, and draw the relevant conclusions – for example for the positioning of a product in competition. 

Pool data on a large scale
The potential to discover knowledge using data analytics increases with the size of the data base. We are continually expanding our data base in all markets, thereby enhancing the validity of benchmark comparisons. With its annual biometric portfolio analysis, Munich Re achieves by far the largest market coverage in the German market in many product lines.

It is essential to have as broad a data base as possible for complex and predictive analysis models. To be able to make reliable predictions with them, we bundle together huge datasets from various sources for you. Key term: Predictive analytics.

Warren Buffet once said, “Risk comes from not knowing what you’re doing.” It follows from this that certainty has to be the result of detailed analysis and deep insights into your own business and portfolio data.

In this context, greater focus is being placed on biometrics both as a key lever for insurers’ profitability, and as a basis for the legally prescribed derivation of best estimates for the risks in your own portfolio. Munich Re assists life insurers on both accounts, and has been carrying out biometric portfolio analyses (BPAs) annually in the German market since 2005. The collected data are separately evaluated for each client, and at the same time serve as a resource for market-wide pooling & benchmarking. 

Knowledge is the key to active portfolio management
For the BPA, we request company-specific data on defined parameters from the participating primary insurers. They provide the raw data, and in return they receive comprehensive biometric evaluations for their own portfolio. Our return gift is the key to active portfolio management, because the evaluations allow them to reliably identify major trends and risk developments in the portfolios. 

Access to detailed results further analysis
Munich Re developed the BPA results cube so that primary insurers could gain additional benefits from the results provided. The operating principle is both simple and reliable: the result cube works with pivot tables containing analysis results for each individual company in granular resolution. Depending on the particular question and objective, the individual data fields can be combined in various ways – for example to analyse only certain parts of portfolios, or obtain evaluations on individual risk characteristics. 

Predictive analytics makes it possible to make more accurate predictions using data modelling. It could be used in a number of ways: from improved customer selection and pitching, through risk assessment and loss adjustment, all the way to reduced claims settlement costs and lapse rates, distribution monitoring and internal process optimisation.

Leverage your data potential with Munich Re
Munich Re has purpose-built a high-performance infrastructure for extremely efficient and in-depth (predictive) analytics methods. With our expertise and state-of-the-art analyses, we support our clients worldwide in optimising their business models and processes.

Benefit from expert consulting and a wealth of data
We possess a wealth of data which we can use to enrich data analyses. For example, we develop analysis models for life insurers who do not possess sufficient data themselves. We consult closely with our clients on every aspect of innovative data analytics methods, support them in selecting the appropriate tools, and in planning and implementing tailored applications.

The quality of an analysis can only be as good as the underlying data base. It is key to obtaining informative and applicable results. The challenge for insurers is that their datasets are often highly fragmented, and stored in isolated data bases along the value chain. This makes holistic analyses almost impossible.

We would be happy to create the conditions to enable you to do this upon request: in joint data integration projects. We combine your internal datasets along the value chain, linking together distribution data, application data, portfolio data and claims data. This creates an integrated data base that allows, for example, a holistic analysis of the different factors that influence customer behaviour. If required, we can also integrate additional data from external sources to further enhance the informational value of the results, and realise more wide-ranging data models.

The key term in this context is data engineering. Data engineering plays just as crucial a role in the successful integration of the various data sources as it does in capturing data that has not yet been digitalised. Our highly specialised data engineering team can assist you all the way from design to implementation, contributing the necessary expertise and a comprehensive understanding of the requirements of life and health insurance.