14/15 Sep 2021
Actuarial Data Science Introplication
Due to technological progress in connection with Data Science and Digitalization, summarized under the buzzword Big Data, a plethora of opportunities and challenges for the industry is arising.
Technological developments have now also reached the insurance industry and thus have a direct impact on the working world of actuaries.
Under the heading Actuarial Data Science, the procedures and methods of data mining are embedded in the actuarial context. These range from classical statistical methods of data analysis as well as regression and cluster methods to machine learning. As a result of almost unlimited computing capacity through cloud computing and extensive available training data, tried and tested methods of artificial intelligence, such as artificial neural networks, are experiencing a renaissance in theory and practice.
We have not yet fully understood the breadth and depth of the resulting future opportunities for the insurance industry. However, there are already numerous applications in the actuarial environment. We have been doing some of them for a long time (and now see them in the context of AI), we are trying out some of them.
Introplication is a compound artificial word consisting of Introduction and Application. And in the web session, we would like to combine both: a basic introduction to the subject area with applications in the actuarial environment.
Organised by the EAA - European Actuarial Academy GmbH in cooperation with the Česká společnost aktuárů.
This web session is suited for actuaries, interested persons and for everyone who wants to get to know the topic (more precisely). Previous knowledge in Actuarial Data Science is not necessary!
Technical RequirementsPlease check with your IT department if your firewall and computer settings support web session participation (the programme Zoom is used for the web session). Please also make sure that you are joining the web session with a stable internet connection.
Purpose and Nature
Introplication precisely defines the online program. We would like to give a basic and somewhat deeper introduction, showing the most important highlights from Actuarial Data Science and pretty applications. We start at the very beginning, so no prior knowledge is required.
In this two-day web session, we cover a wide range of topics from the basic concepts of artificial intelligence and machine learning, through modern data processing technologies and cloud computing, to the mathematical and statistical concepts of data mining. On our way, we will touch on important use cases in the actuarial environment and deepen one or the other business case. To this end, we provide a brief insight into the widely used languages (R, Python) and development environments (RStudio, Anaconda) in the data science context and take a look at innovative insurance products based on individualized risk assessments (e.g. pay how you drive). The online training will be rounded off with short and concise reflections on data protection issues and principles for the ethical handling of artificial intelligence in the insurance environment.
Dr Axel KaiserAxel is Mathematician & actuary (DAV) at SIGNAL IDUNA Insurance Group in Hamburg, Germany, where he is in charge of ALM and Solvency II in health business. He has been using Computer Science for many years and is a member of DAV’s Actuarial Data Science committee.
Dr Stefan NörtemannStefan is Mathematician & actuary (DAV) at msg life central europe gmbh in Cologne, Germany. He leads various projects in the context of Risk Management and Insurance Analytics. In addition, he is a lecturer at German Actuarial Academy (DAA) for Computer Science and for Actuarial Data Science and chair of the section Actuarial Data Science of DAV.
Prof Dr Fabian Transchel Fabian holds the endowed chair of e+s Rück for Data Science at Harz University of Applied Sciences, Wernigerode, Germany. He's an avid proponent of Machine Learning and Artificial Intelligence in the insurance sector and has been instrumental in innovating motor insurance through telematics technologies, these days also teaching Actuarial Data Science for DAA and EAA.