Modeling Credit Risk: An Application of the Rough Set Methodology

Authors

  • Reyes Samaniego Medina Pablo de Olavide University, Spain
  • Maria Jose Vazquez Cueto Seville University, Spain

DOI:

https://doi.org/10.32890/ijbf2013.10.1.2

Keywords:

Rating, Credit risk, Basel Accords, Rough sets

Abstract

The Basel Accords encourages credit entities to implement their own models for measuring financial risk. In this paper, we focus on the use of internal ratings-based (IRB) models for the assessment of credit risk and, specifically, on one component that models the probability of default (PD). The traditional methods used for modelling credit risk, such as discriminant analysis and logit and probit models, start with several statistical restrictions. The rough set methodology avoids these limitations and as such is an alternative to the classic statistical methods. We apply the rough set methodology to a database of 106 companies that are applicants for credit. We obtain ratios that can best discriminate between financially sound and bankrupt companies, along with a series of decision rules that will help detect operations that are potentially in default. Finally, we compare the results obtained using the rough set methodology to those obtained using classic discriminant analysis and logit models. We conclude that the rough set methodology presents better risk classification results.

 

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Published

28-03-2013

Issue

Vol. 10 No. 1 (2013): International Journal of Banking and Finance (IJBF) Vol. 10, No.1, 2013

Section

Articles

How to Cite

Medina, R. S., & Cueto, M. J. V. (2013). Modeling Credit Risk: An Application of the Rough Set Methodology. International Journal of Banking and Finance, 10(1), 34-56. https://doi.org/10.32890/ijbf2013.10.1.2

Research impact

Harvested 2026-09-07
2 citations, from OpenAlex — the highest of the sources checked

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Identifiers DOI 10.32890/ijbf2013.10.1.2 OpenAlex W127542051