A system for Operational Risk management based on the computational paradigm
of Bayesian Networks is presented. The algorithm allows the construction of a
Bayesian Network targeted for each bank using only internal loss data, and
takes into account in a simple and realistic way the correlations among
different processes of the bank. The internal losses are averaged over a
variable time horizon, so that the correlations at different times are removed,
while the correlations at the same time are kept: the averaged losses are thus
suitable to perform the learning of the network topology and parameters. The
algorithm has been validated on synthetic time series. It should be stressed
that the practical implementation of the proposed algorithm has a small impact
on the organizational structure of a bank and requires an investment in human
resources limited to the computational area
