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Credit scoring analysis using a fuzzy probabilistic rough set model

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  • Capotorti, Andrea
  • Barbanera, Eva

Abstract

Credit scoring analysis is an important activity, especially nowadays after a huge number of defaults has been one of the main causes of the financial crisis. Among the many different tools used to model credit risk, the recent development of rough set models has proved effective. The original development of rough set theory has been widely generalized and combined with other approaches to uncertain reasoning, especially probability and fuzzy set theories. Since coherent conditional probability assessments cope well with the problem of unifying these different approaches, a merging of fuzzy rough set theory with this subjectivist approach is proposed. Specifically, expert partial probabilistic evaluations are encompassed inside a gradual decision rule structure, with coherence of the conclusion as a guideline. In line with Bayesian rough set models, credibility degrees of multiple premises are introduced through conditional probability assessments. Nonetheless, discernibility with this method remains too fine. Therefore, the basic partition is coarsened by equivalence classes based on the arity of positively, negatively and neutrally related criteria. A membership function, which grades the likelihood of default, is introduced by a peculiar choice of t-norms and t-conorms. To build and test the model, real data related to a sample of firms are used.

Suggested Citation

  • Capotorti, Andrea & Barbanera, Eva, 2012. "Credit scoring analysis using a fuzzy probabilistic rough set model," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 981-994.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:4:p:981-994
    DOI: 10.1016/j.csda.2011.06.036
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    References listed on IDEAS

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    Cited by:

    1. Constantin Zopounidis & Michael Doumpos, 2013. "Multicriteria decision systems for financial problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 241-261, July.
    2. Hamidreza Arian & Seyed Mohammad Sina Seyfi & Azin Sharifi, 2020. "Forecasting Probability of Default for Consumer Loan Management with Gaussian Mixture Models," Papers 2011.07906, arXiv.org.
    3. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    4. Marco Locurcio & Francesco Tajani & Pierluigi Morano & Debora Anelli & Benedetto Manganelli, 2021. "Credit Risk Management of Property Investments through Multi-Criteria Indicators," Risks, MDPI, vol. 9(6), pages 1-23, June.
    5. Silvia Angilella & Maria Rosaria Pappalardo, 2021. "Assessment of a failure prediction model in the energy sector: a multicriteria discrimination approach with Promethee based classification," Papers 2102.07656, arXiv.org.
    6. Jiaming Liu & Jiajia Liu & Chong Wu & Shouyang Wang, 2024. "Enhancing credit risk prediction based on ensemble tree‐based feature transformation and logistic regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 429-455, March.
    7. Chrysovalantis Gaganis & Panagiota Papadimitri & Menelaos Tasiou, 2021. "A multicriteria decision support tool for modelling bank credit ratings," Annals of Operations Research, Springer, vol. 306(1), pages 27-56, November.
    8. Rogojan Luana Cristina & Croicu Andreea Elena & Iancu Laura Andreea, 2023. "Modern Approaches in Credit Risk Modeling: A Literature Review," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1617-1627, July.
    9. Mehdi Khashei & Akram Mirahmadi, 2015. "A Soft Intelligent Risk Evaluation Model for Credit Scoring Classification," IJFS, MDPI, vol. 3(3), pages 1-12, September.
    10. Doumpos, Michalis & Figueira, José Rui, 2019. "A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method," Omega, Elsevier, vol. 82(C), pages 166-180.

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