Bayesian Modelling of Outstanding Liabilities Incorporating Claim Count Uncertainty Bayesian Modelling of Outstanding Liabilities Incorporating Claim Count Uncertainty

This paper deals with the prediction of the amount of outstanding claims that an insurance company will pay in the near future. We consider various competing models using Bayesian theory and Markov chain Monte Carlo methods. Claim counts are used in order to add a further hierarchical stage in the usual log-normaland state-space models. By this way, we incorporate information from both the outstanding claim amounts and counts data resulting to new model formulations. We provide implementation details and illustrations with real insurance data.