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All Journal Jurnal Teknik Industri
Yuliana Vina Humira
Faculty of Industrial Technology, Industrial Engineering Department, Petra Christian University, Jl. Siwalankerto 121-131, Surabaya 60238
Industrial & Manufacturing Engineering

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Credit Scoring Modeling Halim, Siana; Humira, Yuliana Vina
Jurnal Teknik Industri Vol 16, No 1 (2014): JUNE 2014
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (384.616 KB) | DOI: 10.9744/jti.16.1.17-24

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.
Co-Authors Siana Halim