  This project works with the risk model developed by Li et al. (2015) and
quests modelling, estimating and pricing insurance for risks brought in by
innovative technologies, or other emerging or latent risks. The model considers
two different risk streams that arise together, however not clearly separated
or observed. Specifically, we consider a risk surplus process where premia are
adjusted according to past claim frequencies, like in a Bonus-Malus (BM)
system, when we consider a classical or historical risk stream and an
unforeseeable risk one. These are unknown risks which can be of high
uncertainty that, when pricing insurance (ratemaking and experience rating),
suggest a sensitive premium adjustment strategy. It is not clear for the
actuary to observe which claim comes from one or the other stream. When
modelling such risks it is crucial to estimate the behaviour of such claims,
occurrence and their severity. Premium calculation must fairly reflect the
nature of these two kinds of risk streams. We start proposing a model,
separating claim counts and severities, then propose a premium calculation
method, and finally a parameter estimation procedure. In the modelling we
assume a Bayesian approach as used in credibility theory, a credibility
approach for premium calculation and the use of the Expectation-Maximization
(EM) algorithm in the estimation procedure.
