Analysis of Methods for Generating Classification Rules Applicable to Credit Risk

Authors
Jimbo Santana, Patricia; Villa Monte, Augusto; Rucci, Enzo; Lanzarini, Laura Cristina; Fernández Bariviera, Aurelio
Publication Year
2017
Language
English
Format
article
Status
Published version
Description
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.
Fil: Jimbo Santana, Patricia. Universidad Central del Ecuador; Ecuador
Fil: Villa Monte, Augusto. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
Fil: Rucci, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
Fil: Lanzarini, Laura Cristina. Universidad Nacional de la Plata. Facultad de Informatica. Instituto de Investigación En Informatica Lidi; Argentina
Fil: Fernández Bariviera, Aurelio. Universitat Rovira I Virgili; España
Subject
Classification rules
Credit scoring
Competitive Neural Networks
Particle Swarm Optimization
Ciencias de la Computación
Ciencias de la Computación e Información
CIENCIAS NATURALES Y EXACTAS
Access level
Open access
License
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repository
CONICET Digital (CONICET)
Institution
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identifier
oai:ri.conicet.gov.ar:11336/57326