Classification of entities in trade sector in credibility assessment using neural networks

Aleksandra Wójcicka-Wójtowicz

Abstract

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.
Author Aleksandra Wójcicka-Wójtowicz (WIiGE / KBO)
Aleksandra Wójcicka-Wójtowicz,,
- Department of Operations Research
Journal seriesOptimum. Studia Ekonomiczne, ISSN 1506-7637, (B 10 pkt)
Issue year2017
No3(87)
Pages153-161
Publication size in sheets0.5
Keywords in Englishcredit risk, default, bankruptcy, neural networks
DOIDOI:10.15290/ose.2017.03.87.11
URL http://optimum.uwb.edu.pl/index.php/osj/article/view/60/37
Languageen angielski
Score (nominal)10
Score sourcejournalList
ScoreMinisterial score = 10.0, 24-03-2020, ArticleFromJournal
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