Open AccessNeural Networks in Credit Risk Classification of Companies in the Construction Sector
Author(s) -
Aleksandra Wójcicka
Publication year - 2018
Publication title -
econometric research in finance
Language(s) - English
Resource type - Journals
eISSN - 2451-2370
pISSN - 2451-1935
DOI - 10.33119/erfin.2017.2.2.1
Subject(s) - business , credit risk , identification (biology) , credit history , financial sector , credit enhancement , artificial neural network , finance , process (computing) , credit reference , financial risk , financial system , actuarial science , computer science , machine learning , botany , biology , operating system
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).