BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//AKTUARVEREINIGUNG ÖSTERREICHS (AVÖ) - ECPv6.17.4//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:AKTUARVEREINIGUNG ÖSTERREICHS (AVÖ)
X-ORIGINAL-URL:https://avoe.at
X-WR-CALDESC:Veranstaltungen für AKTUARVEREINIGUNG ÖSTERREICHS (AVÖ)
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Vienna
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20270328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20271031T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20280326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20281029T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Vienna:20261214T093000
DTEND;TZID=Europe/Vienna:20261214T130000
DTSTAMP:20260909T085728Z
CREATED:20260909T085728Z
LAST-MODIFIED:20260909T085728Z
UID:10000689-1797240600-1797253200@avoe.at
SUMMARY:Agentic AI for Actuaries: Practical Use Cases with Claude Code
DESCRIPTION:Actuarial work is full of tasks that are technically routine but stubbornly manual: rebuilding a Solvency II balance sheet\, regenerating year-end reporting and QRTs\, reconciling a cashflow model against the numbers it is supposed to reproduce\, refreshing an MI dashboard\, or standing up a small tool to answer a one-off question from the business. Each is individually straightforward\, yet together they consume a large share of an actuary’s time and are a recurring source of key-person and spreadsheet risk. \nAgentic AI describes an emerging class of coding tools that do more than answer questions. Rather than returning a snippet to paste\, an agentic tool reads your files\, writes and runs code\, inspects the output\, and iterates towards a working result under your direction. Used well\, it lets an actuary automate genuinely complex workflows – end-to-end reporting\, model reconciliation\, assumption-setting\, validation harnesses\, model and process documentation\, and internal tools – without a dedicated software-engineering team. This session uses Claude Code\, one example of this class of tools\, to demonstrate the ideas throughout; the principles transfer to comparable agentic tools. \nAll examples are illustrative and deliberately generic – the focus is on the techniques and the working method\, not on any organisation’s proprietary models\, data or results. The emphasis throughout is practical and honest – what works\, what does not\, and how to keep agentic AI safe\, validated and defensible in a regulated actuarial setting. \nAfter this web session\, participants will be able to: \n\nExplain what agentic AI coding tools such as Claude Code are\, and how they differ from general-purpose chatbots.\nIdentify actuarial workflows – reporting\, reconciliation\, modelling\, assumption-setting and internal tooling – that are strong candidates for this kind of automation.\nFollow a repeatable pattern for scoping a task\, directing the tool\, and reviewing its work.\nUse the tool to generate and maintain model and process documentation that stays in step with the underlying code and satisfies Solvency II evidence requirements.\nApply validation and governance techniques (fail-closed checks\, reconciliation to a trusted source\, golden-master tests\, documentation) so that AI-assisted work is safe and defensible.\nRecognise the limitations and risks – model risk\, key-person risk\, professional and regulatory responsibility – and judge where agentic AI is and is not appropriate.\n\nThe approach is applied and example-led\, grounded in real actuarial applications. Demonstrations are kept short and purposeful – each chosen to illustrate a specific Claude Code capability in a couple of minutes – and any longer-running task is shown through its prepared inputs and results rather than by watching the tool work\, so the session keeps moving. Throughout there is candid discussion of what works\, what does not\, and the pitfalls to avoid. No prior software-development experience is required\, though familiarity with actuarial modelling will help participants get the most from the session.
URL:https://avoe.at/event/agentic-ai-for-actuaries-practical-use-cases-with-claude-code/
LOCATION:Online/Streaming
CATEGORIES:European Actuarial Academy (EAA)
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Vienna:20270305T093000
DTEND;TZID=Europe/Vienna:20270305T130000
DTSTAMP:20260909T091507Z
CREATED:20260909T091507Z
LAST-MODIFIED:20260909T091507Z
UID:10000690-1804239000-1804251600@avoe.at
SUMMARY:Inside LLMs: How They Work and Why They Scale
DESCRIPTION:Deep learning has become the core engine of modern AI\, enabling models to learn rich\, non linear patterns directly from data. In recent years\, this paradigm has scaled into a new generation of systems capable of generating text\, solving problems\, and interacting in natural language: Large Language Models (LLMs). \nParticipants will revisit\, during this 3-hour online training\, the core ingredients of deep learning\, i.e. embeddings and non linear transformations\, and see how these principles extend to models trained on massive text corpora. We then introduce the conceptual foundations behind modern LLM architectures\, explaining at a high level how they process sequences\, capture relationships between tokens\, and scale with data and compute. \nThe web session highlights the key intuition behind the transformer framework without committing to a full technical deep dive. Attendees will gain a clear sense of why transformers replaced earlier approaches\, how they enable efficient parallel processing\, and discuss the practical implications of these models: what they are good at\, where they struggle\, and how their behavior is shaped by the underlying architecture. \nThe goal is to provide a compact\, accessible\, technically informed introduction to how modern language models work. \nThe objective of this 3 hour session is to build a solid\, intuitive understanding of how modern Large Language Models (LLMs) are structured and why their architecture unlocks such powerful capabilities.
URL:https://avoe.at/event/inside-llms-how-they-work-and-why-they-scale/
LOCATION:Online/Streaming
CATEGORIES:European Actuarial Academy (EAA)
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Vienna:20270310T090000
DTEND;TZID=Europe/Vienna:20270312T123000
DTSTAMP:20260703T091334Z
CREATED:20260703T091334Z
LAST-MODIFIED:20260703T091334Z
UID:10000667-1804669200-1804854600@avoe.at
SUMMARY:EAA Web Session 'Assets and Liabilities Management Part 2: Advanced'
DESCRIPTION:The aim of this training is to\nDefine what ALM is and describe the typical missions of an ALM department in an insurance company\nPresent the financial risks on which ALM classically focus as well as the requirements of the Solvency II regulation for insurance companies\nDescribe the essential quantitative ALM tools and methods used by insurance companies to evaluate and mitigate the risks\nIllustrate the different concepts through numerical examples and case studies to make it practical and not just theoretical \nThis second part is more advanced and intended for those wishing to gain more in-depth expertise on the topics. It includes some mathematical technicity\, but nothing that goes further than a solid high school level.
URL:https://avoe.at/event/eaa-web-session-assets-and-liabilities-management-part-2-advanced-4/
LOCATION:Online/Streaming
CATEGORIES:European Actuarial Academy (EAA)
END:VEVENT
END:VCALENDAR