REPOZYTORIUM UNIWERSYTETU
W BIAŁYMSTOKU
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Tytuł: Classification of Trade Sector Entities in Credibility Assessment Using Neural Networks
Autorzy: Wójcicka, Aleksandra
Słowa kluczowe: credit risk
default
bankruptcy
neural networks
Data wydania: 2017
Data dodania: 5-gru-2017
Wydawca: Wydawnictwo Uniwersytetu w Białymstoku
Źródło: Optimum. Studia Ekonomiczne, Nr 3(87) 2017, s. 153-161
Abstrakt: One of the most valid tasks in credit risk evaluation is the proper classification of potential good and bad customers. Reduction of the number of loans granted to companies of questionable credibility can significantly influence banks’ performance. An important element in credit risk assessment is a prior identification of factors which affect companies’ standing. Since that standing has an impact on credibility and solvency of entities. The research presented in the paper has two main goals. The first is to identify the most important factors (chosen financial ratios) which determine company’s performance and consequently influence its credit risk level when granted financial resources. The question also arises whether the line of business has any impact on factors that should be included in the analysis as the input. The other aim was to compare the results of chosen neural networks with credit scoring system used in a bank during credit risk decision-making process.
Afiliacja: Wydział Informatyki i Gospodarki Elektronicznej, Uniwersytet Ekonomiczny w Poznaniu
E-mail: aleksandra.wojcicka@ue.poznan.pl
URI: http://hdl.handle.net/11320/6045
DOI: 10.15290/ose.2017.03.87.11
ISSN: 1506-7637
Typ Dokumentu: Article
Występuje w kolekcji(ach):Optimum. Studia Ekonomiczne, 2017, nr 3(87)

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