{"id":7768,"date":"2025-09-18T18:36:50","date_gmt":"2025-09-18T16:36:50","guid":{"rendered":"https:\/\/actuary.hu\/weblap3\/?p=7768"},"modified":"2025-09-19T17:25:36","modified_gmt":"2025-09-19T15:25:36","slug":"eaas-spotlight-your-upcoming-learning-opportunities-q4-2025","status":"publish","type":"post","link":"https:\/\/actuary.hu\/weblap3\/eaas-spotlight-your-upcoming-learning-opportunities-q4-2025\/","title":{"rendered":"EAA\u2019s Spotlight: Your Upcoming Learning Opportunities – Q4 2025"},"content":{"rendered":"\t\t
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The European Actuarial Academy (EAA) is back from the summer break and is excited to present its professional development opportunities for November and December. A special highlight: on 4 December 2025<\/strong>, the EAA will celebrate its 20th anniversary<\/strong> with a free online conference<\/strong>.
The EAA looks forward to welcoming the actuarial community to its autumn programme and to this special milestone event.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Web Session 'Hands-on Adaptive Learning of GLMs for Risk Modelling in R'<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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on 10\/11 November 2025 | 9:00-15:00 CET<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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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.

From a theoretical point of view, we first substantially widen the GLM modelling space to reduce (or even eliminate) any model bias. This implicitly introduces a higher model variance which we counteract by continuously increasing the penalty term. Since the reduction in variance comes again at the cost of allowing some bias, the algorithm effectively allows us to control for the bias-variance trade-off in the GLM modelling space, and we aim for the GLM that simultaneously minimises the prediction error.

Applying the algorithm results in a simple but generally more accurate model in which we adaptively learned the relevant effects in a data-driven, simultaneous and automated way. A actuarial-academy.com key feature is that we can account for all common types of explanatory variables (continuous, ordinal, nominal) both at the same time and in the same way. The desired balance between model simplicity and forecast accuracy can be set by means of a single control parameter. The final model has a proven GLM structure that is still explainable and allows seamless integration into existing pricing workflows.

During the web session, we will first explore the theoretical foundations of both the biasvariance 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Web Session 'Applying & Governing AI in Actuarial Work: Implications Assessed'<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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on 12 November 2025 | 10:00-12:00 CET<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The online seminar will explore selected AI applications relevant to the actuarial profession, focusing not only on potential use cases but also on how actuaries can responsibly design, validate, and monitor AI models. Participants will learn how to build compelling business cases for AI initiatives and understand where actuarial expertise offers unique value in ensuring model quality, transparency, and alignment with ethical and regulatory standards. By combining technical actuarial skills with a deep understanding of AI methodologies, actuaries can play a key role in shaping and governing AI solutions, thereby creating significant value in a rapidly evolving landscape.

Finally, the session will explore how to move from a general definition of harm to effectively detecting and managing it. It will incorporate both legal and quantitative perspectives\u2014an approach especially valuable when addressing significant harm in AI systems. This will help bridge the gap between legal definitions and statistical evidence.

All in all, the web session will provide a better understanding of what it takes to implement AI models in practice, covering topics relating to definitions of AI and models, risk management practices, infrastructure set-ups and implementation standards.

The participants will build a tool-kit of knowledge that will help them address an array of business cases in practice. The session will also help in increasing the know-how of implementing AI models, their impact within the organisations, given their challenges around explainability to the stakeholders, potential harm and relevance as a whole.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Web Session 'Emerging Risks: Statistical Analysis and Scenario Building'<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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on 13\/14 November 2025 | 9:30-13:00 CET<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This session will notably have two main angles. Firstly, it will raise the question of data, their quality, and the necessary reliance on expert judgment, crucial for risks where experience is limited. Bayesian analysis notably allows for this integration, but does not exempt from a critical examination of the quality of expert data, and we will thus present some methods to attempt to evaluate it.<\/p>

Secondly, we will examine the shift from a “historical data” approach to a “scenario-based” approach. Because while the notion of scenario allows for anticipating situations never encountered before, their design must adhere to scientific standards to avoid being purely speculative.<\/span><\/p>

A simplified illustration of these issues will be provided during the training. An R notebook will allow participants to follow and adapt the implementation without needing an in-depth knowledge of the software.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Web Session 'Stochastic Projection Models in Life Insurance'<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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on 19 November 2025 | 9:00-13:30 CET<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This web session presents the general structure and components that projection models must have for the valuation of life insurance liabilities including their embedded options and guarantees within an economic \/ market-consistent balance sheet (MCBS). It explains the need for a simplified modelling of the life insurance contracts \/ portfolio in a stochastic context and introduces three conceptually different approaches currently observable in the market. The online training compares their key underlying ideas and introduces – based on concrete examples – the mathematical \/ actuarial methodologies used. This introduction also includes a qualitative discussion of their individual strengths & challenges with regards to both external financial reporting and internal business steering including aspects like movement analyses, planning and performance management. Concerning the application of the comparably little-known Liability-2-Step approach, the session will present the operational experience of an Austrian insurance company.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Web Session 'Quantifying the Prospective Impact of Air Pollution on Mortality'<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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on 20 November 2025 | 9:30-12:45 CET<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This lecture will aim to understand the current and future effects of air pollution, particularly on mortality, and its impact on insurance activities. The lecture will also provide participants with tools to reimplement their own model to integrate environmental factors into risk analysis and challenge conventional assumptions.<\/p>

The lecture will provide the ability to reach several objectives such as:<\/span><\/p>