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

Aleksandra Wójcicka
Additional contact information
Aleksandra Wójcicka: Poznań University of Economics and Business, Poland

Econometric Research in Finance, 2017, vol. 2, issue 2, 63-77

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).

Keywords: credit risk; neural networks; financial ratios; credit risk decision-making (search for similar items in EconPapers)
JEL-codes: C58 G21 G33 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://erfin.org/journal/index.php/erfin/article/view/11 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sgh:erfinj:v:2:y:2017:i:2:p:63-77

DOI: 10.33119/ERFIN.2017.2.2.1

Access Statistics for this article

Econometric Research in Finance is currently edited by Dobromił Serwa and Piotr Wdowiński

More articles in Econometric Research in Finance from SGH Warsaw School of Economics, Collegium of Economic Analysis Contact information at EDIRC.
Bibliographic data for series maintained by Dobromił Serwa ().