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17/18 Jun 2019 in Cologne

Deep Learning in Insurance – Theory and Practice

In computational science, deep learning probably is one of the most heralded techniques of present time and recent history, mainly due to its versatility and impressive achievements likewise. Indeed, applications of deep learning range from beating the (human) world champion of the highly complicated Go game to the promise of deploying self-driving cars in the near future, on a large scale and all over the world.
Deep learning (DL) pertains to the field of artificial intelligence and is great at extracting and mastering the often highly non-linear patterns of a given process, whatever this process might be. The only main requirement is the availability of a large amount of data that describes the behaviour of the process under different conditions and a truckload of computational power. However, since the price of data storage and the effort of sampling data has dropped dramatically over the last years, and since Moore’s law on the increase of computational power does even nowadays not show any signs of a slowdown, fitting deep learning models that are able to produce extremely useful predictions are a reality and this already for some years now.
In other words, the time is high to also deploy this amazing technology in the insurance industry! However, the methodological framework that underlies this amazing technology is somewhat different from the statistical one that we’ve all grown accustomed to (mainly through our general love for GLM models), and the computational horsepower, needed to merely fit these models, is of an order of magnitude higher than the one needed to fit the classical statistical models.

Organised by the EAA - European Actuarial Academy GmbH.



The practical sessions will make use of Keras, Tensorflow and R(Studio). Guidelines on how to install these tools on your own laptop will be provided several weeks prior to the beginning of the seminar. As no practical hands-on exercise lesson will be provided, installation of these tools on your laptop is not a must, yet when the applications of DL will be discussed, participants will have the choice to run the code in real-time her-/himself on her/his own laptop during this part of the seminar. Technical support regarding the installation will be provided during the seminar, if necessary.

Attendees are encouraged to bring a laptop computer with Kera, Tensorflow and R(Studio) installed.

Purpose and Nature

The main purpose of this seminar is to get the participants acquainted with DL models. To this end, a healthy mix between theory and practice will be provided, however, it is important to note that some time will be spend to go through the theoretical foundations of neural networks and hence DL, as the inner workings of these models are a bit different from the ones of the classic statistical models. Note that quite a bit of time will also be spend on some real insurance applications, as to clearly illustrate how to use this amazing technology in practice.


The language of the seminar will be English.


Robin Van Oirbeek
Robin Van Oirbeek, after working as a statistical/actuarial consultant for different companies, is now working as the Lead Data Scientist at Allianz Benelux. He is also affiliated to the AFI – Research Centre Insurance of the Faculty of Economics and Business at the KU Leuven. He uses R, amongst others, on a daily basis and this for over 10 years now.

Participant Feedback

4.56 of 5 Points

Venue & Accommodation

The seminar will take place at the 

Mercure Hotel Severinshof Koeln City
Severinstraße 199
50676 Cologne, Germany
Phone: +49 221 2013 555
Hotel website

We have arranged special prices for accommodation. A single room costs € 105.00 per night, including breakfast and VAT, excluding tax for the promotion and advancement of culture in the City of Cologne (further information). This price is valid for bookings out of our allotment ''EAA'' until 2 June 2019. Please book your accommodation directly with the hotel. Kindly book early, as our allotment includes a limited number of rooms, and note the hotel’s cancellation policy.