Hands-on Adaptive Learning of GLMs for Risk Modelling in R
10/11 November 2025 | 9:00-15:00 CET
In recent years, machine learning techniques have found their way into the insurance world. While these methods generally improve model accuracy, both explainability and manual interventions continue to play a key role in risk and tariff modelling. This is why practitioners in many lines of business still apply Generalised Linear Models (GLMs) today for non-life pricing.
But conventional modelling with GLMs comes with downsides. It is a mostly manual and step-by-step process, which may result in overfitting or unrecognised main/interaction effects.
However, GLMs do offer variants in the flavour of machine learning that automatically adapt to patterns in the data. These techniques are known as regularised GLMs, and their most prominent versions are the Lasso, Ridge regression and elastic nets. Not only can these methods proactively prevent overfitting but also adaptively learn non-linear patterns in the data along with an implicitly integrated pre-processing and selection of variables.
In this web session, we will dive into a specific algorithm that uses GLM regularisation in an easy yet powerful way. In this algorithm, we first postulate a complex model structure that represents all potential linear and non-linear patterns for the main effects (and possibly interaction effects) in the data. We then introduce a global penalty term which we apply to reduce the model to only the statistically significant effects at which model accuracy on unseen data performs best.
Early-bird discount is available for bookings made by 29 September 2025.
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