Bankruptcy prediction for credit risk using neural networks: A survey and new results

The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. S...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 12; no. 4; pp. 929 - 935
Main Author Atiya, A.F.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.07.2001
Subjects
Accuracy
Asia
Bankruptcies
Banks
Decisions
Economic indicators
Indicators
Mathematical models
Neural networks
Nonhomogeneous media
Portfolios
Predictive models
Profitability
Regulators
Risk
Risk management
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Summary:The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast).
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ISSN:1045-9227
1941-0093
DOI:10.1109/72.935101