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Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors

Author

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  • Piasecki Krzysztof

    (Poznań University of Economics and Business, Department of Investment and Real Estate, Niepodległości 10, 61-875Poznań, Poland)

  • Wójcicka-Wójtowicz Aleksandra

    (Poznań University of Economics and Business, Department of Operations Research, Niepodległości 10, 61-875Poznań, Poland)

Abstract

Identifying potential healthy and unsound customers is an important task. The reduction of loans granted to companies of questionable credibility can influence banks’ performance. A prior identification of factors that affect the condition of companies is a vital element. Among the most commonly used methods we can enumerate discriminant analysis (DA), scoring methods, neural networks (NN), etc. This paper investigates the use of different structure NN and DA in the process of the classification of banks’ potential clients. The results of those different methods are juxtaposed and their performance compared.

Suggested Citation

  • Piasecki Krzysztof & Wójcicka-Wójtowicz Aleksandra, 2017. "Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors," Folia Oeconomica Stetinensia, Sciendo, vol. 17(2), pages 129-143, December.
    • Handle: RePEc:vrs:foeste:v:17:y:2017:i:2:p:129-143:n:9
    DOI: 10.1515/foli-2017-0023
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    credit risk; default; neural networks; discriminant analysis; financial indices;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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