Early bankruptcy detection using neural networks

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.

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INDEX TERMS

Primary Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.2 Language Classifications

          Nouns: APL

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Connectionism and neural nets

  J. Computer Applications
  J.1 ADMINISTRATIVE DATA PROCESSING
      Subjects: Financial (e.g., EFTS)

General Terms:
Algorithms, Languages, Verification

Keywords:
APL, artificial neural networks, backpropagation, balance sheet ratios, bankruptcy, discriminant analysis