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Discrete Choice Model Application to the Credit Risk Evaluation

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  • Dorota Witkowska

Abstract

The aim of the paper is to discuss the application of classification functions and artificial neural networks (such as multilayer perceptron and radial basis function) to recognize the risk category of investigated companies. The research is based on data from 295 enterprises that applied for credit in two regional banks operating in Poland. Each firm is described by 13 diagnostic variables and potential borrowers are classified into four classes. The efficiency of classification is evaluated in terms of classification errors calculated from the actual classification made by the credit officers. The results of the experiments show that application of artificial neural networks and classification functions can support the creditworthiness evaluation of borrowers. Copyright International Atlantic Economic Society 2006

Suggested Citation

  • Dorota Witkowska, 2006. "Discrete Choice Model Application to the Credit Risk Evaluation," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 12(1), pages 33-42, February.
    • Handle: RePEc:kap:iaecre:v:12:y:2006:i:1:p:33-42:10.1007/s11294-006-6124-0
    DOI: 10.1007/s11294-006-6124-0
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    References listed on IDEAS

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    1. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    2. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    3. Lacher, R. C. & Coats, Pamela K. & Sharma, Shanker C. & Fant, L. Franklin, 1995. "A neural network for classifying the financial health of a firm," European Journal of Operational Research, Elsevier, vol. 85(1), pages 53-65, August.
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    Cited by:

    1. Brad S. Trinkle & Amelia A. Baldwin, 2016. "Research Opportunities for Neural Networks: The Case for Credit," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 240-254, July.
    2. Waldemar Tarczynski & Malgorzata Tarczynska-Luniewska & Kinga Flaga-Gieruszynska, 2020. "The Problem of Bankruptcy in Listed Companies," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 3-15.
    3. Senol Emir & Hasan Dincer & Umit Hacioglu & Serhat Yuksel, 2015. "Comparative Study of Outlier Detection Algorithms via Fundamental Analysis Variables:An Application on Firms Listed in Borsa Istanbul," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 4(4), pages 45-60, October.
    4. Saba Moradi & Farimah Mokhatab Rafiei, 2019. "A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-27, December.

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    More about this item

    Keywords

    C10; C45;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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