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http://dx.doi.org/10.7465/jkdi.2013.24.1.85

Building credit scoring models with various types of target variables  

Woo, Hyun Seok (Department of Statistics, Korea University)
Lee, Seok Hyung (Department of Statistics, Korea University)
Cho, HyungJun (Department of Statistics, Korea University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.1, 2013 , pp. 85-94 More about this Journal
Abstract
As the financial market becomes larger, the loss increases due to the failure of the credit risk managements from the poor management of the customer information or poor decision-making. Thus, the credit risk management also becomes more important and it is essential to develop a credit scoring model, which is a fundamental tool used to minimize the credit risk. Credit scoring models have been studied and developed only for binary target variables. In this paper, we consider other types of target variables such as ordinal multinomial data or longitudinal binary data and suggest credit scoring models. We then apply our developed models to real data and random data, and investigate their performance through Kolmogorov-Smirnov statistic.
Keywords
Credit risk; credit risk management; credit scoring model; longitudinal binary data; ordinal multinomial data;
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  • Reference
1 Agresti, A. (2002). Categorical data analysis, 2nd Ed., Wiley-Interscience, New York.
2 Bielecki, T. R. and Rutkowski, M. (2002). Credit risk: Modeling, valuation and hedging, Springer-Verlag, Berlin.
3 Gonsalves, M. H. and Azzalini, A. (2008). Using markov chains for marginal modelling of binary longitudinal data in an exact likelihood approach. Metron, 2, 157-181.
4 Gonsalves, M. H., Cabral, M. S. and Azzalini, A. (2012). The R package bild for the analysis of binary longitudinal data. Journal of Statistical Software, 9, 1-17.
5 Hand, D. J. and Adams, N. M. (2000). Defining attributes for scorecard construction in credit scoring. Journal of Applied Statistics, 27, 527-540.   DOI   ScienceOn
6 Jung, K. M. (2010). Development of educational software for coarse classifying and model evaluation in credit scoring. Journal of the Korean Data & Information Science Study, 21, 1225-1235.
7 Kang, H. C., Han, S. T., Choi, J. H., Lee, S. G., Kim, E. S., Um, I. H. and Kim, M. K. (2006), Methodology of data mining for C.R.M. : A case study on Enterprise Miner, Free Academy, Seoul.
8 Kim, E. N. and Ha, J. (2010). Study on the validation methods of calibration considering correlations. Journal of the Korean Data & Information Science Study, 21, 407-417.
9 Kim, M. J. (2004). Understanding and applying credit scores, ePharos, Seoul.
10 Koo, J., Park, C. and Jhun, M. (2009). A classification spline machine for building a credit scorecard. Journal of Statistical Computation and Simulation, 79, 681-689.   DOI   ScienceOn
11 Thomas, L. C., Edelman, D. B. and Crook, J. L. (2002). Credit scoring and its applications, SIAM, Philadelphia.