Statistical Concepts of a Priori and a Posteriori Risk Classification in Insurance

AStA Advances in Statistical Analysis, 2012, 96(2), 187-224.

35 Pages Posted: 25 Aug 2010 Last revised: 17 May 2017

See all articles by Katrien Antonio

Katrien Antonio

KU Leuven; University of Amsterdam

Emiliano A. Valdez

University of Connecticut - Department of Mathematics

Date Written: August 25, 2010

Abstract

Everyday we face all kinds of risks, and insurance is in the business of providing us a means to transfer or share these risks, usually to eliminate or reduce the resulting financial burden, in exchange for a predetermined price or tariff. Actuaries are considered professional experts in the economic assessment of uncertain events, and equipped with many statistical tools for analytics, they help formulate a fair and reasonable tariff associated with these risks. An important part of the process of establishing fair insurance tariffs is risk classification, which involves the grouping of risks into various classes that share a homogeneous set of characteristics allowing the actuary to reasonably price discriminate. This article is a survey paper on the statistical tools for risk classification used in insurance. Because of recent availability of more complex data in the industry together with the technology to analyze these data, we additionally discuss modern techniques that have recently emerged in the statistics discipline and can be used for risk classification. While several of the illustrations discussed in the paper focus on general, or non-life, insurance, several of the principles we examine can be similarly applied to life insurance. Furthermore, we also distinguish between “a priori” and “a posteriori” ratemaking. The former is a process which forms the basis for ratemaking when a policyholder is new and insufficient information may be available. The latter process uses additional historical information about policyholder claims when this becomes available. In effect, the resulting “a posteriori” premium allows to correct and adjust the previous “a priori” premium making the price discrimination even more fair and reasonable.

Keywords: Actuarial Science, Regression and Credibility Models, Bonus-Malus Systems

JEL Classification: C10, G22

Suggested Citation

Antonio, Katrien and Valdez, Emiliano A., Statistical Concepts of a Priori and a Posteriori Risk Classification in Insurance (August 25, 2010). AStA Advances in Statistical Analysis, 2012, 96(2), 187-224.. Available at SSRN: https://ssrn.com/abstract=1665463

University of Amsterdam ( email )

Roetersstraat 11
Amsterdam, 1018 WB
Netherlands

Emiliano A. Valdez

University of Connecticut - Department of Mathematics ( email )

341 Mansfield Road U-1009
Storrs, CT 06269-1009
United States

HOME PAGE: http://www.math.uconn.edu/~valdez

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