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A method is presented for identifying an expected profitable set of customers, to offer them an additional insurance product, by estimating a customer specific latent risk profile, for the additional product, by using the customer specific available data for an existing insurance product of the specific customer. For the purpose, a multivariate credibility estimator is considered and we investigate the effect of assuming that one (of two) insurance products is inactive (without available claims information) when estimating the latent risk profile. Instead, available customer specific claims information from the active existing insurance product is used to estimate the risk profile and thereafter assess whether or not to include a specific customer in an expected profitable set of customers. The method is tested using a large real data set from a Danish insurance company and it is shown that sets of customers, with up to 36% less claims than a priori expected, are produced as a result of the method. It is therefore argued that the proposed method could be considered, by an insurance company, when cross-selling insurance products to existing customers.

This article considers Danish insurance business lines for which the pricing methodology has been dramatically upgraded recently. A costly affair, but nevertheless, the benefits greatly exceed the costs; without a proper pricing mechanism, you are simply not competitive. We show that experience rating improves this sophisticated pricing method as much as it originally improved pricing compared with a trivial flat rate. Hence, it is very important to take advantage of available customer experience. We verify that recent developments in multivariate credibility theory improve the prediction significantly, and we contribute to this theory with new robust estimation methods for time (in-)dependency. Copyright (c) The Journal of Risk and Insurance, 2009.