{"id":8557,"date":"2026-08-17T13:25:05","date_gmt":"2026-08-17T11:25:05","guid":{"rendered":"https:\/\/actuary.hu\/weblap3\/?p=8557"},"modified":"2026-08-18T11:56:14","modified_gmt":"2026-08-18T09:56:14","slug":"eaas-spotlight-professional-development-for-todays-actuarial-challenges","status":"publish","type":"post","link":"https:\/\/actuary.hu\/weblap3\/eaas-spotlight-professional-development-for-todays-actuarial-challenges\/","title":{"rendered":"EAA\u2019s Spotlight: Professional Development for Today\u2019s Actuarial Challenges"},"content":{"rendered":"\t\t
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After a short summer break, the European Actuarial Academy returns with a strong line-up of upcoming trainings for the autumn period.<\/p>

Starting at the end of August, the upcoming events include key qualification modules, including courses from the EAA Certificate in Actuarial Data Science<\/a> and CERA<\/a>, followed by a wide range of web sessions and an in-person seminar covering highly relevant topics such as generative AI, climate risk, life insurance modelling, and regulatory developments.<\/p>

Between August and October, we are pleased to present a total of eleven upcoming events, offering both structured qualification paths and targeted CPD opportunities for actuaries at different stages of their careers.<\/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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CERA, Module B: Taxonomy, Modelling and Mitigation of Risks<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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7\u201310 September 2026 | 9:00\u201317:00 & 15:00 CEST<\/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 European Actuarial Academy is one of the main providers of actuarial education \u2013 especially when it comes to Enterprise Risk Management (ERM). The concept of ERM has gained significant momentum in the insurance industry and beyond.<\/p>

We offer a series of four training courses and exams (through DAV) to all actuaries who want to deepen their knowledge in Enterprise Risk Management and gain the international ERM-credential CERA.<\/p>

The web session ‘CERA, Module B: Taxonomy, Modelling and Mitigation of Risks’ is the second of four courses and can either be booked as part of the full series to fulfil the requirements for the CERA designation or individually as CPD training. A written exam is offered subsequently.<\/p>

It focuses on quantitative analyses of financial and non-financial risks of an insurance company and the effect and possible applications of risk mitigation techniques and has been designed for experienced practitioners who use model results in practice and seek guidance for management decisions. Therefore, the focus is not on technical details but on the understanding of risk models and their results, and on the derivation of management actions.<\/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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Actuarial Data Science \u2013 Advanced<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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16\u201318 September 2026 | 9:00\u201317:00 CEST<\/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 is part two of four courses required to obtain the EAA Certificate in Actuarial Data Science<\/a>.<\/p>

In this three-day training, we cover a wide range of topics. This includes an advanced introduction to the concepts and terms of artificial intelligence, modern data management concepts (with a special look at insurance companies), aspects of data protection and the mathematical and statistical concepts of data mining. On our way, we touch different use cases in the actuarial environment. To this end, we provide a brief insight into the widely used language Python. The training rounds off with principles for the ethical handling of artificial intelligence in the insurance environment.<\/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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Seminar in Vilnius, Lithuania\nAdvanced Applications of Generative AI in Actuarial Science<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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1\u20132 October 2026 | 8:45\u201317:00 & 9:00\u201315:00<\/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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Organised by the EAA – European Actuarial Academy GmbH in cooperation with the Lietuvos Aktuar\u0173 Draugija.<\/em><\/p>

Generative artificial intelligence (GenAI) is rapidly changing how actuarial work can be done: from turning unstructured information into model-ready data, to accelerating analysis, documentation, and communication. Large language models (LLMs) enable workflows that go far beyond \u201cchatting with ChatGPT\u201d \u2013 but using them responsibly in actuarial practice requires a clear understanding of capabilities, limitations, evaluation, and robust integration into existing processes.<\/p>

This two-day, hands-on seminar focuses on advanced, practically implementable applications of GenAI in actuarial science. After establishing a practical foundation (how modern LLMs work, where they succeed and fail, and how to assess output quality), participants will work through a series of case studies that reflect typical insurance realities: messy data, document-heavy processes, and the need for auditability, traceability, and human oversight. Throughout the programme, we will connect concepts such as prompting patterns, structured outputs, function calling, retrieval-augmented generation (RAG), fine-tuning, multimodal capabilities, and agentic AI to concrete actuarial use cases.<\/p>

The seminar will be held in person, giving participants the opportunity to learn on site alongside other actuarial professionals, exchange ideas directly, and receive immediate support from the lecturers. The venue is the 4-star hotel Courtyard Vilnius City Centre. The evening event on the first day is giving participants the opportunity to connect, discuss practical questions, and build their professional network in an informal setting.<\/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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Climate Change Scenarios: Application, Evolution, and Reporting<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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5 October 2026 | 9:30\u201313:00 CEST<\/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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Climate Risk scenarios are commonly used in the insurance industry for stress testing, but interpreting and communicating results is often challenging, given strong limitations and complex assumptions. This session will provide practical guidance on stress testing application in the ORSA context, focusing on financial risks, and provide context and foundations necessary in order to communicate and interpret the results. We will discuss key evolutions in recent years with a particular emphasis on NGFS scenarios and the modelling of physical risks.<\/p>

The session will be based around a case study for a generic insurer, where we calculate impacts on the insurer\u2019s capital position under different climate change scenarios, thereby illustrating the practical elements of stress testing. In this context, we will specifically talk about: Key steps to practical implementation of stress tests, models for key financial variables relevant for insurance stress testing (e.g. interest rates, credit spreads), NGFS scenarios and their recent evolution, impact of\u00a0scenario updates, modelling of physical risks, as well as key limitations and considerations for reporting.<\/span><\/p>

Due to the high demand and very positive feedback on the session held on 21 October 2025, this session is being offered as a repeat.<\/u><\/p>

Participants of the session \u2018Climate Change Scenarios in Context \u2013 A Stress Testing Case Study\u2019 on 9 October 2024 are eligible for a 50% discount. Please send an email to <\/strong>contact@actuarial-academy.com<\/strong><\/a> to check your eligibility and allow a few days for processing.<\/strong><\/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 : \nFit4AI compact<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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6-7 October 2026 | 9:00\u201317:00 CEST<\/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 two-day online web session is aimed at actuaries interested in getting started in the broad field of artificial intelligence and has already been successfully delivered for the German Actuarial Academy DAA – Deutsche Aktuar-Akademie GmbH. The aim is to teach key terms in the field of data science and artificial intelligence, essential concepts of machine learning, and social and regulatory frameworks. Developments in generative artificial intelligence will also be discussed. In addition to methodological and mathematical background information, the seminar provides practical knowledge, suggestions, and assistance for participants’ own work. The procedures and concepts taught are clearly illustrated and motivated using actuarial use cases from various sectors of the insurance industry.<\/p>

Note: According to Article 4 of the European Union’s AI Regulation (AI Act), all persons working with AI must have the necessary AI competence. Participants in the seminar will receive confirmation of their AI competence for actuarial use cases in insurance.<\/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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Calculation of Life Insurance Products by Means of Markov Chains<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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8 October 2026 | 10:00\u201312:00 CEST<\/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 calculation of life insurance products is traditionally based on the approach of commutation values, whose table properties enable extensive actuarial calculations even without large computer capacities. However, especially for modern and more flexible life insurance tariffs, the calculation by means of commutation values reaches its limits, so that the calculation approach based on Markov chains is gaining in importance and has been used for some time in the mathematical cores of new portfolio administration systems.<\/p>

The goal of this web session is to give participants an insight into the Markov chain calculation approach in life insurance and its implementation in modern administration systems. Using examples specifically tailored to life insurance tariffs, participants will also gain first practical experience with the Markov approach. Finally, the theoretical and practical components of this web session should enable them to also create and calculate new life insurance tariffs or variations of existing products by using Markov chains.<\/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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GenAI: Is it all about Attention or also about Predictability?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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9 October 2026 | 10:00\u201312:00 CEST<\/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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Artificial Intelligence is rapidly moving from experimentation to infrastructure in actuarial work. AI systems are beginning to influence decisions that were historically driven by statistical models, expert judgment, and regulatory constraints. This session focuses on understanding what is happening under the hood of modern Generative AI systems, particularly large language models and AI agents. What does \u201cattention\u201d mean in technical terms, and why is it foundational to how these systems process information? How do agentic systems differ from classical predictive models? And critically for actuarial practice: where does predictability break down?<\/p>

We will examine both the capabilities and the limitations of AI. In domains characterized by uncertainty, feedback loops, and human behaviour, no system, human or machine, offers perfect foresight. Understanding these boundaries is essential for responsible adoption. The objective is not to replace actuarial judgement, but to augment it, while ensuring that humans remain accountable for decisions in high-stakes contexts.<\/p>

We aim to deepen the understanding of how GenAI models work and why and how they understand digital context in the way humans understand broader context. We also want to shed light on the limitations of GenAI capabilities, as well as human limitations, when it comes to predictability of systems of complex and dynamic nature, which occur in nature, societies and finance and why humans therefore need to stay in the loop when AI work results are used for that matter.<\/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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From Deep Learning to Transformers: Foundations of Modern LLMs<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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12\u201313 October 2026 | 9:00\u201313:30 CEST<\/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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Deep learning (DL) pertains to the field of artificial intelligence and is great at extracting and mastering the often highly non linear patterns of a given process, whatever this process might be. The only main requirement is the availability of a large amount of data that describes the behaviour of the process under different conditions and a truckload of computational power. With data collection becoming cheaper and computational power still following Moore\u2019s law, fitting DL models that produce extremely useful predictions has become a practical reality.<\/p>

While this family of models is broad, one particular architecture has reshaped the field of text analysis: the transformer. Transformers were originally introduced to overcome the limitations of earlier neural networks when dealing with sequential data such as text, where long range dependencies and contextual meaning matter. Their ability to process entire sequences in parallel and to model relationships between all words at once made them uniquely suited for language tasks.<\/p>

Large Language Models (LLMs) are essentially very large transformer networks trained on massive text corpora. They represent a natural continuation of deep learning, but with capabilities\u2014reasoning over text, summarising documents, generating explanations\u2014that go far beyond what earlier DL architectures could achieve. Understanding LLMs therefore benefits from first understanding the deep learning principles on which they are built.<\/p>

The main purpose of this web session is to get the participants acquainted with DL models, and applications on text analysis will help achieve this. To this end, a healthy mix between theory and practice will be provided, however, it is important to note that some time will be spent to go through the theoretical foundations of neural networks and hence DL, as the inner workings of these models are a bit different from the ones of the classic statistical models.<\/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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Assets and Liabilities Management Part 1: Introduction<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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14\u201316 October 2026 | 9:00\u201312:30 CEST<\/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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For an insurance company,\u00a0ensuring the proper coordination between assets and liabilities<\/strong>\u00a0in order to achieve targeted financial objectives is of paramount interest. A strategy used to reach such objectives is “asset and liability management” (ALM in short). ALM can therefore be viewed as any ongoing process that defines, implements, and monitors financial strategies to manage assets and liabilities together.<\/p>

In recent years, the modelling tools used in ALM strategies have become increasingly sophisticated and the technical aspects of current insurance regulation have increased. As a result, some ALM aspects have become more and more difficult to understand and master.<\/p>

The aim of this training is to<\/p>