BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//AKTUARVEREINIGUNG ÖSTERREICHS (AVÖ) - ECPv6.16.3//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/Amsterdam
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20210328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20211031T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20220327T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20221030T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20230326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20231029T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220216T090000
DTEND;TZID=Europe/Amsterdam:20220216T121500
DTSTAMP:20260531T091131
CREATED:20210828T092809Z
LAST-MODIFIED:20210831T113545Z
UID:10000222-1645002000-1645013700@avoe.at
SUMMARY:EAA Web session: Practical Application of Clustering in Insurance
DESCRIPTION:Kurzbeschreibung: Actuarial analytics found its way into several areas of the insurance value chain\, mostly through the use of tools from supervised learning such as linear or tree-based regression. On the other hand\, unsupervised learning\, such as partitional clustering\, seems to be used rather less despite its potential to gain insights into high-dimensional insurance data sets. \nCluster analysis is the task of grouping a set of objects (often data points) in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups. In contrast to simple segmentation (e.g. by geographical location only)\, Clustering uses several features to differentiate among those groups. Potential applications are manifold and centred around questions such as\, for example: \n\nIn which customer segments do we mainly generate new business?\nWhich typical customer should we have in mind while designing new insurance products?\nHow can we make use of granular information\, such as diagnose or treatment codes\, for example\, while dealing with a limited number of observations or claims?\n\nThe course provides an introduction into clustering that does not require any previous knowledge in this area and shall give the participant a jump start to work on his/her own problems. Thus we put a focus on typical stumbling blocks arising when clustering techniques are applied in practice such as interpretability\, missing values and mixed data types. \nThe web session is open to all interested persons. Previous knowledge about partitional clustering is not required\, however\, basic statistical knowledge is recommended. Familiarity with the R programming language would be helpful to follow the practical example. \nRegistration deadline: 14 February 2022
URL:https://avoe.at/event/eaa-web-session-practical-application-of-clustering-in-insurance-2/
LOCATION:Wien
CATEGORIES:European Actuarial Academy (EAA)
END:VEVENT
END:VCALENDAR