Analysis of Methods for Generating Classification Rules Applicable to Credit Risk


  • Patricia Jimbo Santana School of Administration Science, Central University of Ecuador, Quito, Ecuador
  • Augusto Villa Monte III LIDI, School of Computer Science, UNLP, La Plata, Bs.As., Argentina
  • Enzo Rucci III LIDI, School of Computer Science, UNLP, La Plata, Bs.As., Argentina
  • Laura Cristina Lanzarini III LIDI, School of Computer Science, UNLP, La Plata, Bs.As., Argentina
  • Aurelio Fernández Bariviera Departament of Business, Universitat Rovira i Virgili, Reus, Spain


classification rules, credit scoring, competitive neural networks, particle swarm optimization


Credit risk is defined as the probability of loss due to non-compliance by the borrower with the required payments in relation to any type of debt. When financial institutions select their customers correctly, they can reduce their credit risk. To achieve this, they use various classification methodologies to sort customers based on their risk, analyzing a set of variables such as reputation, leverage, income and so forth. The extensive analysis and processing of these variables is quite time-consuming, partly because the data to be analyzed are not homogeneous. In this paper, we present an alternative method that operates on nominal and numeric attributes, which allows obtaining a predictive model that uses a reduced set of classification rules aimed at reducing credit risk. When the number of rules used decreases, credit analysts need less time to make their decisions, which will also result in better customer service. The methodology proposed here was applied to two databases of the UCI repository and two real databases of Ecuadorian banks that grant various types of credit. The results obtained have been satisfactory. Finally, our conclusions are discussed and future research lines are suggested.


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How to Cite

Jimbo Santana, P., Villa Monte, A., Rucci, E., Lanzarini, L. C., & Fernández Bariviera, A. (2017). Analysis of Methods for Generating Classification Rules Applicable to Credit Risk. Journal of Computer Science and Technology, 17(01), p. 20–28. Retrieved from



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