Managerial applications of neural networks: The case of bank failure predictions. (English) Zbl 0763.90062
Summary: The 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, \(k\text{NN}\), 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.
MSC:
90B90 | Case-oriented studies in operations research |
92B20 | Neural networks for/in biological studies, artificial life and related topics |