Credit Scoring Modeling
Abstract: 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.
Author: Siana Halim, Yuliana
Vina Humira
Journal Code: jptindustrigg140014
