On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data

On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. We use a nonparametric approach based on a combination of kernel logistic regression and ε-support vector regression which both have good robustness properties. The strategy is applied to a data set from motor vehicle insurance companies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Mathematicae Applicatae Sinica Springer Journals

On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data

On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data

Acta Mathematicae Applicatae Sinica, English Series Vol. 21, No. 2 (2005) 193–208 On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data Andreas Christmann Department of Statistics, University of Dortmund, D-44221 Dortmund, Germany (E-mail: christmann@statistik.uni-dortmund.de) Abstract The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. We use a nonparametric approach based on a combination of kernel logistic regression and ε−support vector regression which both have good robustness properties. The strategy is applied to a data set from motor vehicle insurance companies. Keywords Data mining, kernel logistic regression, robustness, statistical machine learning, support vector regression 2000 MR Subject Classification 62G08, 62G35, 62G32 1 Introduction Insurance companies need estimates for the probability of a claim and for the expected sum of claim sizes for each customer to construct insurance tariffs. In this paper we consider statistical aspects for analyzing such data sets from motor vehicle insurance companies. Some of the results may also be useful for other areas, e.g. in credit risk scoring, customer relationship management (CRM) or for CHURN analyses. Robustness is an important aspect in analyzing insurance data sets, because some explana- tory variables can only be observed in an imprecise manner and some reported claim sizes are only estimates and not the true values. On the other hand, in contrast to some other areas of applied statistics, extreme high claim sizes can not be dropped from the data set because this would systematically underestimate the expected premium values. From a theoretical and also from an applied point of view the impact of a few extreme claims on the whole...
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Publisher
Springer-Verlag
Copyright
Copyright © 2005 by Springer-Verlag Berlin Heidelberg
Subject
Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
ISSN
0168-9673
eISSN
1618-3932
D.O.I.
10.1007/s10255-005-0229-8
Publisher site
See Article on Publisher Site

Abstract

The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. We use a nonparametric approach based on a combination of kernel logistic regression and ε-support vector regression which both have good robustness properties. The strategy is applied to a data set from motor vehicle insurance companies.

Journal

Acta Mathematicae Applicatae SinicaSpringer Journals

Published: Jan 1, 2005

References

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