Early bankruptcy detection using neural networks

Authors:
Gottfried Rudorfer
Affiliations:
Department of Applied Computer Science, University of Economics and Business Administration, Augasse 2-6, A-1090 Vienna, Austria
Venue:
APL '95 Proceedings of the international conference on Applied programming languages
Year:
1995

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Abstract

In 1993, Austria had the highest number of bankruptcies since 1945. The total liabilities came to approximately US$3 billion.Powerful tools for the early detection of company risks are very important to avoid high economic losses. Artificial neural networks (ANN) are suitable for many tasks in pattern recognition and machine learning. In this paper we present an ANN for early detection of company failures using balance sheet ratios. The neural network has been successfully tested with real data of Austrian private limited companies. The research activities included the design of an APL application with a graphical user interface to find out the relevant input data and tune the ANN.The developed APL workspace takes advantage of modern windowing features running on IBM compatible computers.