Bayesian Forecasting for Accident Proneness Evaluation

As part of their resource allocation processes, insurance companies have to undertake various evaluation tasks concerning the accident proneness of their insurants. Bayesian methods are specially fit for that task since they allow for the coherent incorporation of all sources of information, including expert opinions and data. We describe three increasingly complex and realistic models for that purpose. For predictive and inference purposes, we have to rely on simulation methods. We illustrate the models with a real case and describe their implementation in a forecasting system developed for an insurance company.