Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors

Krzysztof Piasecki , Aleksandra Wójcicka-Wójtowicz

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.
Author Krzysztof Piasecki (WZ / KIiN)
Krzysztof Piasecki,,
- Department of Investment and Real Estate
, Aleksandra Wójcicka-Wójtowicz (WIiGE / KBO)
Aleksandra Wójcicka-Wójtowicz,,
- Department of Operations Research
Journal seriesFolia Oeconomica Stetinensia, ISSN 1730-4237, e-ISSN 1898-0198, (B 11 pkt)
Issue year2017
Vol17
No2
Pages129-143
Publication size in sheets0.7
Keywords in Englishcredit risk, default, neural networks, discriminant analysis, financial indices
DOIDOI:10.1515/foli-2017-0023
URL https://www.degruyter.com/downloadpdf/j/foli.2017.17.issue-2/foli-2017-0023/foli-2017-0023.pdf
Languageen angielski
Score (nominal)11
Score sourcejournalList
ScoreMinisterial score = 11.0, 24-03-2020, ArticleFromJournal
Citation count*4 (2020-07-05)
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