  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.
