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

Aleksandra Wójcicka

doi:10.33119/ERFIN.2017.2.2.1

Aleksandra Wójcicka Department of Operations Research, Poznan University of Economics, Poland

DOI: https://doi.org/10.33119/ERFIN.2017.2.2.1

Keywords: Credit Risk, Neural Networks, Financial Ratios, Credit Risk Decision-Making Process

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).

References

Angelini, E., di Tollo, G., and Roli, A. (2008). A neural network approach for credit risk evaluation. The Quarterly Review of Economics and Finance, 48(4):733–755.

Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on neural networks, 12(4):929–935.

Baesens, B., Setiono, R., Mues, C., and Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49(3):312–329.

Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York, NY, USA.

di Tollo, G. (2006). Credit Risk: A Neural Net Approach. mimeo, Universita Pescara.

di Chieti–Haykin, S. (2011). Neural networks and learning machines. Prentice Hall; 3 edition.

Hecht-Nielsen, R. (1988). Theory of the backpropagation neural network. Neural Networks, 1(Supplement 1):445–448.

Huang, Z., Chen, H., Hsu, C., Chen, W., and Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37(4):543–558.

Karaa, A. and Krichene, A. (2012). Credit–Risk Assessment Using Support Vectors Machine and Multilayer Neural Network Models: A Comparative Study Case of a Tunisian Bank. Journal of Accounting and Management Information Systems, 11(4):587–620.

Khemakhem, S. and Boujelbene, Y. (2015). Credit Risk Prediction: A Comparative Study between Discriminant Analysis and the Neural Network Approach. Journal of Accounting and Management Information Systems, 14(1):60–78.

Linder, R., Geier, J., and Kolliker, M. (2004). Artificial neural networks, classification trees and regression: Which method for which customer base? Journal of Database Marketing and Customer Strategy Management, 11(4):344–356.

Migdal Najman, K. and Najman, K. (2013). Samouczace sie sztuczne sieci neuronowe w grupowaniu i klasyfikacji danych. Teoria i zastosowania w ekonomii. Wydawnictwo Uniwersytetu Gdanskiego.

Ogwueleka, F. N., Misra, S., Colomo-Palacios, R., and Fernandez, L. (2015). Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank. Human Factors and Ergonomics in Manufacturing and Service Industries, 25(1):28–42.

Osowski, S. (2006). Sieci neuronowe do przetwarzania informacji. Oficyna Wydawnicza Politechniki Warszawskiej.

Pacelli, V. and Azzollini, M. (2011). An artificial neural network approach for credit risk management. Journal of Intelligent Learning Systems and Applications, 3(02):103.

Ripley, B. D. (2009). Pattern Recognition and Neural Networks. Cambridge University Press.

Skubalska-Rafaj-lowicz, E. (2011). Sieci neuronowe w przetwarzaniu strumieni danych: struktury sieci i algorytmy uczenia. Oficyna Wydawnicza Politechniki Wroclawskiej, 1 edition.

Wantoch-Rekowski, R. (2003). Sieci neuronowe w zadaniach: perceptron wielowarstwowy. Bel Studio.

West, D. (2000). Neural network credit scoring models. Computers and Operations Research, 27(11):1131–1152.

Witkowska, D. (2002). Sztuczne sieci neuronowe i metody statystyczne: wybrane zagadnienia finansowe. Studia Ekonomiczne - C. H. Beck. C. H. Beck.

Wojcicka, A. (2016a). Classification of trade sector entities in credibility assessment using neural networks. Econometric Research in Finance Workshop in Warsaw.

Wojcicka, A. (2016b). Neural networks in credit risk evaluation of construction sector. MZBO’16 conference in Czerniejewo.