  According to different typologies of activity and priority, risks can assume
diverse meanings and it can be assessed in different ways. In general risk is
measured in terms of a probability combination of an event (frequency) and its
consequence (impact). To estimate the frequency and the impact (severity)
historical data or expert opinions (either qualitative or quantitative data)
are used. Moreover qualitative data must be converted in numerical values to be
used in the model. In the case of enterprise risk assessment the considered
risks are, for instance, strategic, operational, legal and of image, which many
times are difficult to be quantified. So in most cases only expert data,
gathered by scorecard approaches, are available for risk analysis. The Bayesian
Network is a useful tool to integrate different information and in particular
to study the risk's joint distribution by using data collected from experts. In
this paper we want to show a possible approach for building a Bayesian networks
in the particular case in which only prior probabilities of node states and
marginal correlations between nodes are available, and when the variables have
only two states.

Physics and Society