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Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach

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  • Petr Hájek

Abstract

This paper presents an analysis of credit rating using fuzzy rule-based systems. The disadvantage of the models used in previous studies is that it is difficult to extract understandable knowledge from them. The root of this problem is the use of natural language that is typical for the credit rating process. This problem can be solved using fuzzy logic, which enables users to model the meaning of natural language words. Therefore, the fuzzy rule-based system adapted by a feed-forward neural network is designed to classify US companies (divided into the finance, manufacturing, mining, retail trade, services, and transportation industries) and municipalities into the credit rating classes obtained from rating agencies. Features are selected using a filter combined with a genetic algorithm as a search method. The resulting subsets of features confirm the assumption that the rating process is industry-specific (i.e. specific determinants are used for each industry). The results show that the credit rating classes assigned to bond issuers can be classified with high classification accuracy using low numbers of features, membership functions, and if-then rules. The comparison of selected fuzzy rule-based classifiers indicates that it is possible to increase classification performance by using different classifiers for individual industries. Copyright Springer-Verlag 2012

Suggested Citation

  • Petr Hájek, 2012. "Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(3), pages 421-434, September.
    • Handle: RePEc:spr:cejnor:v:20:y:2012:i:3:p:421-434
    DOI: 10.1007/s10100-011-0229-0
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    References listed on IDEAS

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    1. Amogh Deshpande & Srikanth Iyer, 2009. "The credit risk + model with general sector correlations," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 17(2), pages 219-228, June.
    2. Miroslav Hudec & Mirko Vujošević, 2010. "A fuzzy system for municipalities classification," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 18(2), pages 171-180, June.
    3. Malhotra, Rashmi & Malhotra, D. K., 2002. "Differentiating between good credits and bad credits using neuro-fuzzy systems," European Journal of Operational Research, Elsevier, vol. 136(1), pages 190-211, January.
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