Credit-Risk Decision Process Using Neural Networks in Industrial Sectors

  • Aleksandra Wójcicka
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


Due to increased bankruptcies noted among companies (debtors) banks pay more attention to credit risk management. 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. The research focuses on determining which of the factors have the biggest impact on company’s solvency and which are redundant and therefore can be removed from future analysis. The other purpose of the research is to investigate and compare the results of two different structures of neural networks—the most common Multi-Layer Perceptron (MLP) and Radial Basis Function neural network (RBF). The conducted research bases on the financial reports of Polish companies in industrial sector and a credit risk analysis method applied in one of the banks operating on Polish market. The results of two different NN models are juxtaposed and compared with the real-world data. Moreover, the vulnerability analysis of entry data is carried out to find the most beneficial set of variables.


Credit risk Neural networks Financial ratios Credit risk decision-making process 


  1. Angelini E, Tollo G, Roli A (2008) A neural network approach for credit risk evaluation. Q Rev Econ Finance 48:733–755CrossRefGoogle Scholar
  2. Atiya AF (2001) Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Trans Neural Networks 12(4)Google Scholar
  3. Baesens B, Setiono R, Mues C, Vanthienen J (2003) Using neural network rule extraction and decision tables for credit-risk evaluation. Manage Sci 49(3)Google Scholar
  4. Huang Z, Chen H, Hsu CJ, Chen WH, Wu S (2004) Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis Support Syst 37:543–558. Available online at
  5. Karaa A, Krichene A (2012) Credit-risk assessment using support vectors machine and multilayer neural network models: a comparative study case of a Tunisian bank. Acc Manage Inf Syst 11(4):587–620Google Scholar
  6. Khemakhem S, Boujelbènea Y (2015) Credit risk prediction: a comparative study between discriminant analysis and the neural network approach. Acc Manage Inf Syst 14(1):60–78Google Scholar
  7. Linder R, Geier J, Kölliker MJ (2004) Artificial neural networks, classification trees and regression: which method for which customer? Database Mark Customer Strategy Manage 11:344–356. Scholar
  8. Oreski S, Oreski D, Oreski G (2012) Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment. Expert Syst Appl 39(16):12605–12617. Scholar
  9. Ogwueleka FN, Misra S, Colomo-Palacios R, Fernández-Sanz L (2015) Neural network and classification approach in identifying customer behaviour in the banking sector: a case study of an international bank. Hum Factors Ergonomics Manufacturing Serv Ind 25(1):28–42. Scholar
  10. Pacelli V, Azzollini M (2011) An artificial neural network approach for credit risk management. J Intell Learn Syst Appl 103–112.
  11. Tollo G (2006) Credit risk: a neural net approach.
  12. Wójcicka A (2012) Calibration of a credit rating scale for Polish companies. In: Operations research and decisions 3/2013. Uniwersytet Ekonomiczny we WrocławiuGoogle Scholar
  13. Wójcicka A (2017a) Neural networks in credit risk classification of companies in the construction sector. Econometric Research in Finance 2(2):Fall IssueGoogle Scholar
  14. Wójcicka A (2017b) Classification of trade sector entities in credibility assessment using neural networks. ​Optimum 87(3).
  15. Wójciak M, Wójcicka A (2008) Zdolności dyskryminacyjne wskaźników finansowych w ocenie kondycji finansowej podmiotów gospodarczych. In: Taksonomia 15: Klasyfikacja i analiza danych - teoria i zastosowania. Uniwersytet Ekonomiczny we WrocławiuGoogle Scholar
  16. Wójciak M, Wójcicka A (2009) The discriminative ability of financial ratios to evaluate the credit risk level. In: Chrzan P, Czernik T (eds) Metody matematyczne, ekonometryczne i komputerowe w finansach i ubezpieczeniach 2007. Wydawnictwo Akademii Ekonomicznej (AE) w KatowicachGoogle Scholar
  17. Wójcicka A, Wójtowicz T (2009) Wykorzystanie analizy wskaźnikowej w ocenie zdolności kredytowej przedsiębiorstwa - szanse i zagrożenia. In: Zeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego (SGGW) w Warszawie: Ekonomika i Organizacja Gospodarki Żywnościowej 2009 nr 78. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego (SGGW) w WarszawieGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Poznań University of Economics and BusinessPoznańPoland

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