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

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  • Aleksandra Wójcicka

    (Poznań University of Economics and Business, Poland)

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).

Suggested Citation

  • Aleksandra Wójcicka, 2017. "Neural Networks in Credit Risk Classification of Companies in the Construction Sector," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(2), pages 63-77, December.
    • Handle: RePEc:sgh:erfinj:v:2:y:2017:i:2:p:63-77
    DOI: 10.33119/ERFIN.2017.2.2.1
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    References listed on IDEAS

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    1. Sihem Khemakhem & Younes Boujelbene, 2015. "Credit Risk Prediction: A Comparative Study between Discriminant Analysis and the Neural Network Approach," Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 14(1), pages 60-78, March.
    2. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    3. Adel KARAA & Aida KRICHENE, 2012. "Credit–Risk Assessment Using Support Vectors Machine and Multilayer Neural Network Models: A Comparative Study Case of a Tunisian Bank," Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 11(4), pages 587-620, December.
    4. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
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    Cited by:

    1. Bruno Reis & António Quintino, 2023. "Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis," Journal of Economic Analysis, Anser Press, vol. 2(3), pages 94-112, May.

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    Keywords

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    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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