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Author
Wójcicka Aleksandra (Uniwersytet Ekonomiczny w Poznaniu)
Title
Classification of Trade Sector Entities in Credibility Assessment Using Neural Network
Source
Optimum : studia ekonomiczne, 2017, nr 3(87), s. 153-161, rys., tab., bibliogr. s. 160-161
Keyword
Ryzyko kredytowe, Bankructwo, Sieci neuronowe
Credit risk, Bankruptcy, Neural networks
Note
JEL Classification: C45, G33, G11
summ.
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. (original abstract)
Accessibility
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
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Bibliography
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Cited by
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ISSN
1506-7637
Language
eng
URI / DOI
http://dx.doi.org/10.15290/ose.2017.03.87.11
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