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
Date Written: August 25, 2010
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: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations
By Adam B. Jaffe, Rebecca M. Henderson, ...
Econometric Models for Count Data with an Application to the Patents-R&D Relationship
By Jerry A. Hausman, Bronwyn H. Hall, ...
Intellectual Capital and the Birth of U.S. Biotechnology Enterprises
By Lynne G. Zucker, Michael R. Darby, ...
Ivory Tower Versus Corporate Lab: An Empirical Study of Basic Research and Appropriability
By Manuel Trajtenberg, Rebecca M. Henderson, ...
The U.S. Productivity Slowdown: A Case of Statistical Myopia
Welfare Measurement Bias in Household and On-Site Surveying of Water-Based Recreation: An Application to Lake Sevan, Armenia
By Benoit Laplante, Craig M. Meisner, ...