Credit Scoring Modeling

It is generally easier to predict defaults accurately if a large data set (including
defaults) is available for estimating the prediction model. This puts not only small banks, which
tend to have smaller data sets, at disadvantage. It can also pose a problem for large banks that
began to collect their own historical data only recently, or banks that recently introduced a new
rating system. We used a Bayesian methodology that enables banks with small data sets to
improve their default probability. Another advantage of the Bayesian method is that it provides
a natural way for dealing with structural differences between a bank’s internal data and
additional, external data. In practice, the true scoring function may differ across the data sets,
the small internal data set may contain information that is missing in the larger external data
set, or the variables in the two data sets are not exactly the same but related. Bayesian method
can handle such kind of problem.