BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//AKTUARVEREINIGUNG ÖSTERREICHS (AVÖ) - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Vienna:20260617T100000
DTEND;TZID=Europe/Vienna:20260617T120000
DTSTAMP:20260424T155813
CREATED:20260210T190801Z
LAST-MODIFIED:20260210T190801Z
UID:10000621-1781690400-1781697600@avoe.at
SUMMARY:EAA Web Session 'Causal AI for Actuarial Models'
DESCRIPTION:For years\, correlation has been central to the actuarial profession’s practice. Actuaries have mastered the art of identifying patterns in historical data to price risk and predict future losses. However\, in the era of „Big Data“ and increasingly complex algorithms\, there is a critical limitation: traditional statistical models can tell us what is happening\, but they often struggle with why. While a predictive model may identify a strong association between a specific variable and a loss\, it does not establish a direct cause-and-effect relationship. Building on Judea Pearl’s ideas\, Causal AI helps actuaries move from correlations in historical data to causal relationships. \nIs causal inference a fashion of the moment? No—for the modern actuary\, it represents a step toward actively influencing outcomes. Instead of asking\, „What usually happens?“ actuaries can now answer\, „What would happen if we changed this specific factor?“. By using causal diagrams (DAGs) and the „Ladder of Causation\,“ actuaries can distinguish true risk drivers from collider bias. \nCausal AI enables product designs that reflect contemporary lifestyle habits. For instance\, it can support creating adaptive premiums\, improving transparency and fairness in underwriting by embedding causal reasoning directly into the sales process\, and leveraging IoT data with updated causal analysis to provide a flexible subscription that reacts to the user’s changing risk profile. \nIn this seminar\, we will explore the formal scientific discipline of causal inference and the transition into the actuarial field. Will explore new actuarial toolkits needed to address challenges in the incoming actuarial work.\nAnmeldeschluss: 2026-06-15\nLink: https://actuarial-academy.com/en/continuing-education/upcoming-trainings/detail/eaa-web-session-causal-ai-for-actuarial-models-e0563/
URL:https://avoe.at/event/eaa-web-session-causal-ai-for-actuarial-models/
LOCATION:Online/Streaming
CATEGORIES:European Actuarial Academy (EAA)
END:VEVENT
END:VCALENDAR