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Credit Scoring Modeling

Halim, Siana and HUMIRA, YULIANA VINA (2014) Credit Scoring Modeling. Jurnal Teknik Industri, Vol. 1 (N0. 1). pp. 17-24. ISSN 1411-2485

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        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.

        Item Type: Article
        Uncontrolled Keywords: Credit scoring, Bayesian logit models, Gini coefficient.
        Subjects: H Social Sciences > HA Statistics
        Divisions: Faculty of Industrial Technology > Industrial Engineering Department
        Depositing User: Admin
        Date Deposited: 23 Jun 2014 20:36
        Last Modified: 31 Jul 2018 11:14
        URI: http://repository.petra.ac.id/id/eprint/16588

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