Risk Classification by Means of Clustering

Bernhard Christian Kübler

ISBN 10: 3631597592 / ISBN 13: 9783631597590

Published by Peter Lang, Peter Lang, 2010

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Determining risk-adequate insurance premiums is a core issue in actuarial mathematics. This study is specifically concerned with identifying convenient partitions of (general) insurance collectives such that the resulting tariff classes are homogeneous to a maximum extent and - on the other hand - yet large enough to allow for the occurrence of the group balance concept and to end up with reliable estimates of the moments of the claim size distributions. Therefore, the author develops an innovative classification algorithm utilizing a multidimensional cluster approach combined with credibility-theoretical implications. Its construction stems from involving the entire claim information of risks simultaneously and in a suitable manner, and particulary from obtaining optimality regarding the cluster criterions. Under certain conditions, commonly used cross classification schemes are shown to be a particular case of the new approach. Besides desirable theoretical benefits like its generalizing established cross classification systems, an empirical investigation also suggests the practical superiority of the new algorithm.; Dissertationsschrift.

Seller Inventory # 9783631597590

Bibliographic Details

• Title: Risk Classification by Means of Clustering
• Publisher: Peter Lang, Peter Lang
• Publication Date: 2010
• Language: English
• ISBN 10: 3631597592
• ISBN 13: 9783631597590
• Binding: Buch
• Condition: Neu
• Dimensions: 216W x 153H x 14D millimeters 216 millimeters width by 153 millimeters height by 14 millimeters depth
• Weight: 378 grams

About this title

Synopsis

Determining risk-adequate insurance premiums is a core issue in actuarial mathematics. This study is specifically concerned with identifying convenient partitions of (general) insurance collectives such that the resulting tariff classes are homogeneous to a maximum extent and – on the other hand – yet large enough to allow for the occurrence of the group balance concept and to end up with reliable estimates of the moments of the claim size distributions. Therefore, the author develops an innovative classification algorithm utilizing a multidimensional cluster approach combined with credibility-theoretical implications. Its construction stems from involving the entire claim information of risks simultaneously and in a suitable manner, and particulary from obtaining optimality regarding the cluster criterions. Under certain conditions, commonly used cross classification schemes are shown to be a particular case of the new approach. Besides desirable theoretical benefits like its generalizing established cross classification systems, an empirical investigation also suggests the practical superiority of the new algorithm.

About the Author

The Author: Thomas Schoberth, geboren 1969 in Kulmbach, hat an der Universität Bayreuth Physik studiert und sein Studium 1998 mit Diplom abgeschlossen. Im Anschluss war er als Softwareentwickler im Verlagswesen tätig. Ab 2000 war der Autor im Rahmen des Forschungsverbundes Wirtschaftsinformatik Nordbayern (forwin) Mitarbeiter der Universität Bayreuth am Lehrstuhl für Wirtschaftsinformatik. Seit 2003 begleitet er die Einführung des elektronischen Prüfungsverwaltungssystems der Universität Bayreuth und leitet dort seit 2009 das Dezernat für Informationsmanagement.