We analyse a large telematics data with 65 driving variables (DVs) and insurance claim counts for 14,157 drivers. Our aim is to predict future claims according to driving behaviour measured by the DVs, to identify important DVs that differentiate driving behaviours, classify drivers according to...

In automobile insurance, it is common to adopt a Poisson regression model to predict the number of claims as part of the actuarial pricing process. The Poisson assumption can rarely be justified, often due to overdispersion, and alternative modeling is often considered, typically zero-inflated...

Actuaries are often faced with the task of estimating tails of loss distributions from just a few observations. Thus estimates of tail probabilities (reinsurance prices) and percentiles (solvency capital requirements) are typically subject to substantial parameter uncertainty. We study the bias...