EEA Web Session: Hands-on Adaptive Learning of GLMs for Risk Modelling in R
Announcement from the EEA organiser:
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.
During this web session, we will first explore the theoretical foundations of both the bias-variance trade-off in predictive modelling and general GLM regularisation. We will then study the explicit design of the algorithm. The remainder will be hands-on as we provide extensive code that implements the algorithm in the statistical programming language R. We will discuss and run the code using a realistic case study in actuarial claims frequency modelling. You will learn how to use the programme and apply the algorithm to non-life claims data for pricing. Further focus will be on the visualisation of the results, especially on the insights gained from the learned meta-results of the algorithm, e.g., the implicit way how we selected, prioritised and pre-processed variables.
Click here to register. Your early-bird registration fee is € 540.00 (net) / € 642.60 (incl. VAT, if applicable) for bookings by 3 October 2024. After this date, the fee will be € 700.00 (net) / € 833.00 (incl. VAT, if applicable).
Click here (Note: timing via that link is in CEST [Central European Summer Time].)
Dr Lukas Hahn
Lukas is a certified actuary and works as a Lead Data Scientist at SV SparkassenVersicherung in Stuttgart, Germany. The focus of his work lies on both the development and productive deployment of statistical and machine learning models in SV's big data ecosystems on various use cases ranging from actuarial non-life pricing to customer lead management. As a key component of his work, he maintains a self-service tool with a dynamic web-based user interface that builds upon the algorithm that we will use in this web session. The tool is now deployed company-wide as a software as a service for data-driven explainable data analyses.
Before joining SV in 2019, Lukas worked at the Institute of Finance and Actuarial Science (ifa) in Ulm as a senior consultant on data analytics in insurance with projects in all lines of business.
Lukas holds degrees in business mathematics (M.Sc.) from Ulm University and statistics (M.Math.) from the University of Waterloo in Waterloo, Canada. In 2019, he received his doctorate from Ulm University.
Lukas is a member of the German Actuarial Association (DAV) and a Certified Actuarial Data Scientist (CADS). He is lecturer for the German certification programme on actuarial data science for the German Actuarial Academy (DAA).