compensation schemes where the insurer has information on accidents occurring ‘at work’ as well as ‘not at work’, policyholders having policies in multiple lines of business (e.g. flood, theft etc.) at the same company, or policyholders holding multiple contracts e.g. in a motor insurance context. The latter is the situation we will explore in this paper, using a data set from a European insurance company. The combination of a priori rating (through risk classification based on a priori measurable characteristics) and a posteriori rating is considered. A posteriori the claim experience of a policyholder is taken into account. In a multivariate context the pricing actuary should be able to use claim history from all business lines or risk components a policyholder is holding within the company. Intuitively, the number of claims a policyholder has reported on a particular business line or risk component reveals his general risk proneness and is as such relevant when pricing
other lines or components. In contrast to the analytical approaches developed for multivariate experience rating in the literature, our approach is data-driven using Bayesian statistics. Empirical evidence has been found of positive dependence between different contracts belonging to the same policy holder, which is an intuitively appealing result. Focus is on applications of the multivariate Bayesian experience rating, as well as on graphical representations of the results.
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