These days, nobody disputes the profound impact and yet-untouched potential of Machine Learning and Artificial Intelligence anymore. Yet, in the actuarial sciences, these breakthrough possibilities are hampered by regulation, the need for numerical confidence and insight into model decision making, the latter being subsumed as a "black box problem". Thus, the quest for explainability is in fact much more pressing than in any other industry.

This upcoming seminar on ML explainability aims to provide insights into the areas of unsupervised learning, supervised learning and artificial neural nets via model-agnostic explainability approaches, while providing opportunities to try out the methods yourself!

Organised by the EAA – European Actuarial Academy.

Participants are expected to have, apart from actuarial basics, a preliminary grasp on Machine Learning and hypothesis testing as well as the R and/or Python languages.

Technical Requirements
Please check with your IT department if your firewall and computer settings support web session participation (the programme Zoom will be used for this online training). Please also make sure that you are joining the web session with a stable internet connection.

Explainability is the cornerstone of practicability of ML and AI techniques – without thorough insights into the inner workings of the more advanced decision making and prediction methods, usage become hampered by the worst kind of information asymmetry: Unpredictable model outputs not fit for productive use. Ultimately, AI and ML methods will be well-suited for use in actuarial contexts, but to this end, more and better tools to understand these tools need to be procured and utilised.

The language of the web session will be English.

Professor Dr Fabian Transchel
Fabian Transchel is professor for Data Science at the Harz University of Applied Sciences, Department AI, where his research focus is Insurance and Finance, data-driven mobility and agile development methodologies. He is active in the development of the new Actuarial Data Science subjects at the German Actuarial Association (DAV) and a lecturer for the German Actuarial Academy (DAA).