  We propose a dynamical model for the estimation of Operational Risk in
banking institutions. Operational Risk is the risk that a financial loss occurs
as the result of failed processes. Examples of operational losses are the ones
generated by internal frauds, human errors or failed transactions. In order to
encompass the most heterogeneous set of processes, in our approach the losses
of each process are generated by the interplay among random noise, interactions
with other processes and the efforts the bank makes to avoid losses. We show
how some relevant parameters of the model can be estimated from a database of
historical operational losses, validate the estimation procedure and test the
forecasting power of the model. Some advantages of our approach over the
traditional statistical techniques are that it allows to follow the whole time
evolution of the losses and to take into account different-time correlations
among the processes.
