One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borrower. Processing this information can be time consuming, and presents some difficulties due to the heterogeneous structure of data. We offer in this paper an alternative method that is able to classify customers' profiles from numerical and nominal attributes. The key feature of our method, called LVQ+PSO, is the finding of a reduced set of classifying rules. This is possible, due to the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method not only useful for credit officers aiming to make quick decisions about granting a credit, but also could act as borrower's self selection. Our method was applied to an actual database of a credit consumer financial institution in Ecuador. We obtain very satisfactory results. Future research lines are exposed.
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 credit scoring, each analyzing a large number of microeconomic and/or macroeconomic variables selected mostly depending on the type of credit to be granted. Since these variables are heterogeneous, the review process carried out by credit 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 credit granting. To achieve this, linguistic variables were used to help the analyst interpret the information available from the credit 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 credit and savings cooperative and two banks that grant various types of credits. 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.
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