  Overrides of credit ratings are important correctives of ratings that are
determined by statistical rating models. Financial institutions and banking
regulators agree on this because on the one hand errors with ratings of
corporates or banks can have fatal consequences for the lending institutions
and on the other hand errors by statistical methods can be minimised but not
completely avoided. Nonetheless, rating overrides can be misused in order to
conceal the real riskiness of borrowers or even entire portfolios. That is why
rating overrides usually are strictly governed and carefully recorded. It is
not clear, however, which frequency of overrides is appropriate for a given
rating model within a predefined time period. This paper argues that there is a
natural error rate associated with a statistical rating model that may be used
to inform assessment of whether or not an observed override rate is adequate.
The natural error rate is closely related to the rating model's discriminatory
power and can readily be calculated.
