Frequency-severity experience rating based on latent Markovian risk profiles

Bonus-Malus Systems traditionally consider a customer's number of claims irrespective their sizes, even though these components are dependent in practice. We propose novel joint experience rating approach based on latent Markovian risk profiles to allow for positive or negative individual frequency-severity dependence. The evolve over time Hidden Markov Model capture updates experience, making claim counts and sizes conditionally independent. show that the resulting premia lead dynamic, experience-weighted mixture standard credibility premia. proposed is applied Dutch automobile insurance portfolio identifies customer with distinctive claiming behavior. These profiles, turn, enable us better distinguish between risks.