Managerial Applications of Neural Networks: The Case of Bank Failure Predictions

Kar Yan Tam and Melody Y. Kiang
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Kar Yan Tam: Department of Business Information Systems, School of Business and Management, The Hong Kong University of Science and Technology, Hong Kong
Melody Y. Kiang: Department of Decision and Information Systems, Arizona State University, Tempe, Arizona 85287-4206

Management Science, 1992, vol. 38, issue 7, 926-947

Abstract: This paper introduces a neural-net approach to perform discriminant analysis in business research. A neural net represents a nonlinear discriminant function as a pattern of connections between its processing units. Using bank default data, the neural-net approach is compared with linear classifier, logistic regression, kNN, and ID3. Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness. Limitations of using neural nets as a general modeling tool are also discussed.

Keywords: neural networks; artificial intelligence; discriminant analysis; bank failure predictions (search for similar items in EconPapers)
Date: 1992
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