Data Analytics

Data Analytics

Data Analytics, Big Data, Business Intelligence, Data Science… These are all buzzwords that describe what algorithms can do with data. Nowadays, most of the client’s financial and administrative processes are being digitized. As a result, the amount of data available for analysis is vastly increased. 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 perception and exposes any potential for improvement. By adding new data perspectives to existing views, organizations gain surprising insights into previously obscured areas that include, for example, client behaviour, risks and opportunities, hidden costs, and unexpected connections. This newly developing playing field is of interest, not only to insurers, banks, and pension funds but to every business out there.

Data Analytics in practice

Data analytics can be applied anywhere in the value chain of any organization. Two important questions arise:

  • What is the business concern or business challenge?
  • What data is currently available?

We also see the following questions:

  • How can I optimize my client service process?
  • Where lies the waste in our claims 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?
  • How to better manage my claims process?
  • Can I improve the differentiation of my premium model with external data?
  • Can I develop a more data-driven strategy?
  • Can I substantiate my suspicions with facts?

Domain knowledge

Being able to utilize Machine Learning is not sufficient for the effective use of Data Analytics in the financial sector. While it’s possible to automate many things, automation also creates new risks. An actuary, however, brings a great deal of added value to the table due to their domain knowledge. For example, professional knowledge is vital in determining which specific variable you are going to include. 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 query without losing the overview in an abundance of data. Start with the available data and then decide what other external data or insights you may need. Then you can determine the steps towards 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 are capable of serving client relations in all financial sectors.

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