Are we in the era of actuarial data science modelling?

Are we in the era of actuarial data science modelling?

A huge increase in data generation, data capture and data storage combined with significantly increased computing power is providing insurers with a unique opportunity to re-evaluate the value that their data can provide; and the technologies available to do that.

Enabling actuaries to embrace modern day data science tools and to work closely with data scientists is an important link that could give strategic advantages to insurers in the further development of actuarial modelling software.

The current economic & pandemic situation is causing insurers to assess the impact quicker and to use modern day tools available to respond and adapt.

Looking forward, the actuary will continue to evaluate key sources of data and need to find ways to incorporate data science that uses state of the art machine- learning and data technologies together with the actuary’s business insights. We need to refresh our methods and make use of emerging technological advances.

Some are turning to programming languages like Julia, Python and R; among other. With the rise of open-source execution environments computational notebooks, programming is becoming more accessible and easy to use. 

This provides an interesting alternative for actuaries to execute large amounts of statistical calculations and see the results with the latest data visualisation techniques.

Join us for an exploration session on the use of data science in insurance companies today.

During this presentation we will be exploring among other:

• Examples of how insurers are making sense of and using new data sources and technologies, including an exploration of data strategy
• Exploration of practical applications of data science within actuarial work,
• The benefits of data-driven decisions to solve business problems using the power of data and technology,
• The role that actuaries can play to harness the benefits of data science including an overview of the ethical considerations for practitioners in data science.

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