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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Altman, E., “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance, XXIII, 4, 1968, pp. 589–603.
—. The Prediction of Corporate Bankruptcy, New York: Grandland, 1988.
—. Corporate Financial Distress and Bankruptcy, New York, Chichester: Wiley, 1993.
Back, B.; Laitinen, T.; Sere, K.; van Wazel, M. “Choosing Bankruptcy Prediction Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms,” Proceedings of the First Nordic Workshop on Genetic Algorithms and their Applications, Vaasa, 1995.
Baetge, J.; Krause, C. “The Classification of Companies by Means of Neural Networks,” Journal of Information Science and Technology, 3, 1, October 1993, pp. 96–112.
Berry, R. H.; Trigueiros, D. “Applying Neural Networks to the Extraction of Knowledge from Accounting Reports: A Classification Study,” in Neural Networks in Finance and Investing, Trippi, R. R., Turban, E. (eds.), Chicago—London: Probus, 1993, pp. 103–24.
Lacher, R. C.; Coast, P. K.; Sharma, S. C.; Fant, L. F. “A Neural Network for Classifying Health of a Firm,” European Journal of Operational Research, 85, 1995.
Odom, M. D.; Sharda, R. “A Neural Network Model for Bankruptcy Prediction,” in Neural Networks in Finance and Investing, Trippi, R. R., Turban, E. (eds.), Chicago—London: Probus, 1993, pp. 177–85.
Raghupathi, W.; Schkade, L. L.; Raju, B. S. “A Neural Network Approach to Bankruptcy Prediction,” in Neural Networks in Finance and Investing, Trippi, R. R., Turban, E. (eds.), Chicago—London: Probus, 1993, pp. 141–58.
Rahimian, E.; Singh, S.; Thammachote, T.; Virmani, R. “Bankruptcy Prediction by Neural Network,” in Neural Networks in Finance and Investing, Trippi, R. R., Turban, E. (eds.), Chicago, London: Probus, 1993, pp. 159–76.
Rehkugler, H.; Poddig, T. “Neuronale Netze im Bankbetrieb,” Die Bank, 7, 1992.
Tam, K.; Kiang, M. “Predicting Bank Failures: A Neural Network Approach,” Applied Artificial Intelligence, 4, 1990, pp.265–82.
—. “Managerial Applications of Neural Networks: The Case of Bank Failure Prediction,” in Neural Networks in Finance and Investing, Trippi, R. R., Turban, E. (eds.), Chicago—London: Probus, 1993, pp. 193–228.
Wilson, R. L.; Sharda, R. “Bankruptcy Prediction Using Neural Networks,” Decision Support Systems, 11, 1994, pp. 545–57.
Witkowska, D. “Application of Artificial Neural Networks to Bank Decision Simulations,” International Advances in Economic Research, 5, 3, 1999, pp. 350–68.
Witkowska, D.; Staniec, I. “Credit Granting Procedure: Multilayer Perceptron and Classification Tree,” in Neural Networks and Soft Computing, Rutkowski, L., Kacprzyk, J. (eds.), New York, Heidelberg: Physica-Verlag, 2003, pp.748–53.
Witkowska, D.; Kaminski, W.; Kompa, K.; Staniec, I. “Neural Networks as a Supporting Tool in Credit Granting Procedure,” Information Technology for Economics and Management, 2004, (ITEM) e-journal, Vol. 2, No. 1, Paper 1. http://www.item.woiz.polsl.pl/issue2.1/journal2.1.htm.
About this article
Cite this article
Witkowska, D. Discrete Choice Model Application to the Credit Risk Evaluation. Int Adv Econ Res 12, 33–42 (2006). https://doi.org/10.1007/s11294-006-6124-0