  Graphical models have been widely used in applications ranging from medical
expert systems to natural language processing. Their popularity partly arises
since they are intuitive representations of complex inter-dependencies among
variables with efficient algorithms for performing computationally intensive
inference in high-dimensional models. It is argued that the formalism is very
useful for applications in the modelling of non-life insurance claims data. It
is also shown that actuarial models in current practice can be expressed
graphically to exploit the advantages of the approach. More general models are
proposed within the framework to demonstrate the potential use of graphical
models for probabilistic learning with telematics and other dynamic actuarial
data. The discussion also demonstrates throughout that the intuitive nature of
the models allows the inclusion of qualitative knowledge or actuarial judgment
in analyses.
