Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors

Krzysztof Piasecki and Wójcicka-Wójtowicz Aleksandra ()
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Wójcicka-Wójtowicz Aleksandra: Poznań University of Economics and Business, Department of Operations Research, Niepodległości 10, 61-875Poznań, Poland

Folia Oeconomica Stetinensia, 2017, vol. 17, issue 2, 129-143

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.

Keywords: credit risk; default; neural networks; discriminant analysis; financial indices (search for similar items in EconPapers)
JEL-codes: C38 C49 G33 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:foeste:v:17:y:2017:i:2:p:129-143:n:9

DOI: 10.1515/foli-2017-0023

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