Defining cause and effect

One should analyze cause and effect of an operational loss. For example, failure to
have an independent group vet all mathematical models is a cause, and a loss event
arising from using erroneous models is the effect (see Table 12.5).
Loss or effect data is easier to collect than the causes of loss data. There may be
many causes to one loss. The relationship can be highly subjective and the importance
of each cause difficult to assess. Most banks start by collecting the losses and
then try to fit the causes to them. The methodology is typically developed later, after
the data has been collected. One needs to develop a variety of empirical analyses to
test the link between cause and effect.