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Fuzzy Cbkp_redit Risk Scoring Rules using FRvarPSO

Abstract:

There is consensus that the best way for reducing insolvency situations in financial institutions is through good risk management, which involves a good client selection process. In the market, there are methodologies for cbkp_redit scoring, each analyzing a large number of microeconomic and/or macroeconomic variables selected mostly depending on the type of cbkp_redit to be granted. Since these variables are heterogeneous, the review process carried out by cbkp_redit analysts takes time. The objective of this article is to propose a solution for this problem by applying fuzzy logic to the creation of classification rules for cbkp_redit granting. To achieve this, linguistic variables were used to help the analyst interpret the information available from the cbkp_redit officer. The method proposed here combines the use of fuzzy logic with a neural network and a variable population optimization technique to obtain fuzzy classification rules. It was tested with three databases from financial entities in Ecuador-one cbkp_redit and savings cooperative and two banks that grant various types of cbkp_redits. To measure its performance, three benchmarks were used: accuracy, number of classification rules generated, and antecedent length. The results obtained indicate that the hybrid model that is proposed performs better than its previous versions due to the addition of fuzzy logic. At the end of the article, our conclusions are discussed and future research lines are suggested.