CREDIT SCORING MODEL BASED ON THE AFFINITY SET

Jerzy Michnik, Anna Michnik, Berenika Pietuch

2008

Abstract

The significant development of credit industry led to growing interest in sophisticated methods which can support making more accurate and more rapid credit decisions. The parametric statistical methods such as linear discriminant analysis and logistic regression were soon followed up by nonparametrical methods and other techniques: neural networks, decision trees, and genetic algorithms. This paper investigates the affinity set – a new concept in data mining field. The affinity set model was applied to credit applications database from Poland. The results are compared to those received by Rosetta (the rough sets and genetic algorithm procedure) and logistic regression.

References

  1. Chen, Y. and Larbani, M. (2006). Developing the affinity set and its applications. In Proceeding of the Distinguished Scholar Workshop by National Science Council, Jul. 14-18, 2006, Taiwan. National Science Council, Taiwan.
  2. Larbani, M. and Chen, Y. (2006). Affinity set and its applications. In Proceeding of the International Workshop on Multiple Criteria Decision Making, Apr. 14- 18, 2007, Poland. Publisher of The Karol Adamiecki University of Economics in Katowice.
  3. Lee, T.-S. and Chen, I.-F. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 28:743752.
  4. Lee, T.-S., Chiu, C.-C., Chou, Y.-C., and Lu, C.-J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis, 50:11131130.
  5. Lee, T.-S., Chiu, C.-C., Lu, C.-J., and Chen, I.-F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications, 23:245254.
  6. Øhrn, A., Komorowski, J., Skowron, A., and Synak, P. (1994). The design and implementation of a knowledge discovery toolkit based on rough sets: The rosetta system. In Polkowski, L. and Skowron, A., editors, Rough Sets in Knowledge Discovery 1: Methodology and Applications, volume 18 of Studies in Fuzziness and Soft Computing, chapter 19, page 376. Physica-Verlag, Heidelberg, Germany.
  7. West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27:1131-1152.
  8. Xiao, W., Zhao, X., and Fei, Q. (2006). A comparative study of data mining methods in consumer loans credit scoring management. J. Syst. Sci. Syst. Eng., 15(4):419-435.
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Paper Citation


in Harvard Style

Michnik J., Michnik A. and Pietuch B. (2008). CREDIT SCORING MODEL BASED ON THE AFFINITY SET . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: CIAS, (ICEIS 2008) ISBN 978-989-8111-39-5, pages 287-290. DOI: 10.5220/0001723902870290


in Bibtex Style

@conference{cias08,
author={Jerzy Michnik and Anna Michnik and Berenika Pietuch},
title={CREDIT SCORING MODEL BASED ON THE AFFINITY SET},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: CIAS, (ICEIS 2008)},
year={2008},
pages={287-290},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001723902870290},
isbn={978-989-8111-39-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: CIAS, (ICEIS 2008)
TI - CREDIT SCORING MODEL BASED ON THE AFFINITY SET
SN - 978-989-8111-39-5
AU - Michnik J.
AU - Michnik A.
AU - Pietuch B.
PY - 2008
SP - 287
EP - 290
DO - 10.5220/0001723902870290