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 the...

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Bibliographic Details
Published in:Jurnal Teknik Industri 2014-06, Vol.16 (1), p.17-24
Main Authors: Halim, Siana, Humira, Yuliana Vina
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian logit models
Credit scoring
Datasets
Gini coefficient
Online Access:Get full text
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Summary: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.
ISSN:1411-2485
2087-7439
DOI:10.9744/jti.16.1.17-24