Managerial applications of neural networks: the case of bank failure predictions

Article Abstract:

Managerial decisions often entail placing an observation under one of various categories. In the case of binary classification, observations may fall under one of only two categories. Most classification strategies that had been developed to address the binary classification problem fall under the approach called multivariate discriminant analysis (DA). A proposed alternative to DA is the neural-net approach. The effectiveness of this approach was compared against that of DA using bank default data. Findings indicated that the neural-net approach was a better method in examining bank conditions because it offers more accurate predictions, more flexibility and more robustness than such methods as linear classifier, logistic regression, kNN, and ID3. The limitations of the neural nets technique, as well as the conditions under which it works best, are discussed.

Usage, Computer networks, Neural networks, Bank failures

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Database models and managerial intuition: 50% model + 50% manager

Article Abstract:

Decision makers are faced with the need to incorporate more information into the decision making process as more and better data becomes available. A combination of simple database models with managerial intuition, in which the two are viewed as complementary rather than competitive, is the best method for improving decision quality. A 50% model plus 50% manager heuristic improves the quality of forecasting as statistical models offer more predictive accuracy and managerial intuition provides additional insight models cannot incorporate. The 50% model plus 50% manager heuristic offers the advantages of simplicity, palatability, and accuracy and can be easily incorporated into extant decision support systems.

Analysis, Management, Executives, Management research

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