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Título : Analysis of methods for generating classification rules applicable to credit risk
Autor : Jimbo Santana, Patricia
Villa Monte, Augusto
Rucci, Enzo
Lanzarini, Laura
Fernández Bariviera, Aurelio
Palabras clave : PUNTUACIÓN DE CRÉDITO
REDES NEURONALES COMPETITIVAS
ENJAMBRE DE PARTÍCULAS
Fecha de publicación : 2017
Editorial : La Plata: Universidad Nacional de La Plata. Facultad de Informática
Citación : Jimbo Santana, Patricia y otros (abril, 2017). Analysis of methods for generating classification rules applicable to credit risk. Journal of Computer Science & Technology, 17(1): 20-28
Resumen : 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.
URI : http://www.dspace.uce.edu.ec/handle/25000/14736
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