Neural Networks in Credit Risk Classification of Companies in the Construction Sector

Aleksandra Wójcicka-Wójtowicz

Abstract

The financial sector (banks, financial institutions, etc.) is the sector most exposed to financial and credit risk, as one of the basic objectives of banks’ activity (as a specific enterprise) is granting credit and loans. Because credit risk is one of the problems constantly faced by banks, identification of potential good and bad customers is an extremely important task. This paper investigates the use of different structures of neural networks to support the preliminary credit risk decision-making process. The results are compared among the models and juxtaposed with real-world data. Moreover, different sets and subsets of entry data are analyzed to find the best input variables (financial ratios).
Author Aleksandra Wójcicka-Wójtowicz (WIiGE / KBO)
Aleksandra Wójcicka-Wójtowicz,,
- Department of Operations Research
Journal seriesEconometric Research in Finance, ISSN , e-ISSN 2451-2370, (0 pkt)
Issue year2017
Vol2
No2
Pages63-77
Publication size in sheets0.7
Keywords in Englishcredit risk, neural networks, financial ratios, credit risk decision-making process
DOIDOI:10.33119/ERFIN.2017.2.2.1
URL http://erfin.org/journal/index.php/erfin/issue/view/5
Languageen angielski
Score (nominal)5
Score sourcejournalList
ScoreMinisterial score = 5.0, 03-04-2020, ArticleFromJournal
Citation count*1 (2020-09-22)
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