Neural Networks in Credit Risk Classification of Companies in the Construction Sector
AbstractThe 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).
|Journal series||Econometric Research in Finance, ISSN , e-ISSN 2451-2370, (0 pkt)|
|Publication size in sheets||0.7|
|Keywords in English||credit risk, neural networks, financial ratios, credit risk decision-making process|
|Score||= 1.0, 08-10-2020, ArticleFromJournal|
|Citation count*||1 (2020-10-17)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.