Variations of particle swarm optimization for obtaining classification rules applied to credit risk in financial institutions of Ecuador

Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval criteria....

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Bibliographic Details
Published in:Risks (Basel) 2020-03, Vol.8 (1), p.1-14
Main Authors: Jimbo Santana, Patricia, Lanzarini, Laura, Bariviera, Aurelio Fernández
Format: Article
Language:English
Subjects:
Accuracy
Classification
Credit risk
Decision making
Decision trees
Financial institutions
fuzzy classification rules
Fuzzy logic
Machine learning
Macroeconomics
Neural networks
Optimization techniques
particle swarm optimization
Regression analysis
Risk assessment
Support vector machines
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Summary:Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval criteria. In this direction, classification rules are an appropriate tool, provided that the rule set has low cardinality and that the antecedent of the rules has few conditions. This paper analyzes different solutions based on Particle Swarm Optimization (PSO) techniques, which are able to construct a set of classification rules with the aforementioned characteristics using information from the borrower and the macroeconomic environment at the time of granting the loan. In addition, to facilitate the understanding of the model, fuzzy logic is incorporated into the construction of the antecedent. To reduce the search time, the particle swarm is initialized by a competitive neural network. Different variants of PSO are applied to three databases of financial institutions in Ecuador. The first institution specializes in massive credit placement. The second institution specializes in consumer credit and business credit lines. Finally, the third institution is a savings and credit cooperative. According to our results, the incorporation of fuzzy logic generates rule sets with greater precision.
ISSN:2227-9091
2227-9091
DOI:10.3390/risks8010002