  We investigate the optimal reinsurance problem under the criterion of
maximizing the expected utility of terminal wealth when the insurance company
has restricted information on the loss process. We propose a risk model with
claim arrival intensity and claim sizes distribution affected by an
unobservable environmental stochastic factor. By filtering techniques (with
marked point process observations), we reduce the original problem to an
equivalent stochastic control problem under full information. Since the
classical Hamilton-Jacobi-Bellman approach does not apply, due to the infinite
dimensionality of the filter, we choose an alternative approach based on
Backward Stochastic Differential Equations (BSDEs). Precisely, we characterize
the value process and the optimal reinsurance strategy in terms of the unique
solution to a BSDE driven by a marked point process.
