  This paper aims to present a general idea of method comparison of Credit
Scoring techniques. Any scorecard can be made in various methods based on
variable transformations in the logistic regression model. To make a comparison
and come up with the proof that one technique is better than another is a big
challenge due to the limited availability of data. The same conclusion cannot
be guaranteed when using other data from another source. The following research
challenge can therefore be formulated: how should the comparison be managed in
order to get general results that are not biased by particular data? The
solution may be in the use of various random data generators. The data
generator uses two approaches: transition matrix and scorings. Here are
presented both: results of comparison methods and the methodology of these
comparison techniques creating. Before building a new model the modeler can
undertake a comparison exercise that aims at identifying the best method in the
case of the particular data. Here are presented various measures of predictive
model like: Gini, Delta Gini, VIF and Max p-value, emphasizing the
multi-criteria problem of a "Good model". The idea that is being suggested is
of particular use in the model building process where there are defined complex
criteria trying to cover the important problems of model stability over a
period of time, in order to avoid a crisis. Some arguments for choosing Logit
or WOE approach as the best scorecard technique are presented.
