Data Analytics

Data Analytics, Big Data, Business Intelligence, Data Science… These are all buzzwords that describe what algorithms can do with data. Most of the client, financial and administrative processes are now being digitised. As a result, the amount of data available for analysis is exploding. Data has become the primary source for determining client preferences, assessing risks, improving and automating business processes, measuring performance and developing new products and services. Data Analytics is the way to gain the competitive advantage in the digital age.

Data Analytics combines advanced methods and techniques, tooling, programming knowledge and data with domain knowledge. This creates data-driven understanding and identifies potential for improvement. By adding new data perspectives to existing ones, organisations gain surprising insights into areas including, for example, client behaviour, risks and opportunities, hidden costs and unexpected connections. This new playing field is of interest not only to insurers, banks, and pension funds but to every business.

Data Analytics in practice

Data analytics can be applied anywhere in the value chain of any organisation. Two important questions to ask are: what is the business concern or challenge? and what data is available now? We also see the following questions:

• How can I optimise my client service process?
• Where is the waste in our claim process?
• Which client groups are most attractive?
• What are the bottlenecks in our Solvency II process?
• What can we do about the depletion of our client base?
• How can we improve our commercial performance?
• Can I automate my fraud detection?
• Can I manage my claims better?
• Can I better differentiate my premium model with external data?
• Can I make my strategy more data-driven?
• Can I substantiate my suspicions with facts?

Domain knowledge

Knowledge of Machine Learning is not sufficient for the effective use of Data Analytics in the financial sector. While it’s possible to automate many things, this also creates risks. An actuary, however, has a great deal of added value thanks to their domain knowledge. For example, professional knowledge is vital in the choice of whether to take a specific variable with you. Using Data Analytics with no substantive knowledge can quickly lead to incorrect conclusions and a waste of both time and effort.

Desired insight

You should therefore use Data Analytics for your desired business insight. This allows you to work on an issue or research question without losing the overview in an abundance of data. Start with the available data and then see what other external data or insights you may need. Then you can determine the steps to the solution. This may involve data enrichment, dashboarding, the process of client journey mining, trend analysis, time series analysis, or developing a predictive model. Our Data Analytics experts have domain knowledge and practical experience in applying data-critical business processes. With this knowledge, we serve client relations in all financial sectors.

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Our solutions for Data Analytics

Claims Management

As an insurer, you want better returns from your portfolio and you are looking beyond just the pricing. After all, competition in the insurance market is increasing, putting pressure on your premiums and profitability.

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Premiums for Non-life Insurers

The non-life insurance market is shrinking and domestic and foreign competition is increasing. As a result, setting the premiums for your insurance products is crucial to stay in the race.

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Continuous Improvement Data Driven

Our Data Driven Continuous Improvement proposition helps you make strategic decisions based on data-driven insights. This allows you to more efficiently optimise and organise all processes in your value chain

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scenarios that are forward-looking

Our unique forward-looking scenarios enable clients to develop a long-term vision based on realistic data.

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All solutions