  A novel dynamical model for the study of operational risk in banks and
suitable for the calculation of the Value at Risk (VaR) is proposed. The
equation of motion takes into account the interactions among different bank's
processes, the spontaneous generation of losses via a noise term and the
efforts made by the bank to avoid their occurrence. Since the model is very
general, it can be tailored on the internal organizational structure of a
specific bank by estimating some of its parameters from historical operational
losses. The model is exactly solved in the case in which there are no causal
loops in the matrix of couplings and it is shown how the solution can be
exploited to estimate also the parameters of the noise. The forecasting power
of the model is investigated by using a fraction $f$ of simulated data to
estimate the parameters, showing that for $f = 0.75$ the VaR can be forecast
with an error $\simeq 10^{-3}$.
