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

Abstract:

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 cbkp_redit 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 cbkp_redit placement. The second institution specializes in consumer cbkp_redit and business cbkp_redit lines. Finally, the third institution is a savings and cbkp_redit cooperative. According to our results, the incorporation of fuzzy logic generates rule sets with greater precision.