Fuzzy model Takagi Sugeno with structured evolution for determining consumer credit score

The rating (score) and knowledge of the payment behavior of a client to reduce the time of granting of consumer credit is one of the requirements that financial institutions have dedicated to providing these services. For qualifying customers, these entities are based on qualitative and quantitative information of a client, making it difficult a homogeneous rating. Because of the need to reduce response times regarding the approval or rejection of a credit application is important to use models that help to analyze a real-time credit. Hence, this paper develops and analyzes based on the principles of evolutionary computation model, and the principles of a fuzzy Takagi Sugeno type model, to estimate the score in the allocation of consumer loans. To optimize learning, the proposed model is subjected to a process of evolution, based on the EVOP model, which guides learning model, based on two parameters such as: the generation parameter and the parameter mutation thus generating a structured evolution that will take the model to different states in learning. The results obtained by the proposed model allows to decrease the time of granting of consumer credit, as allowed demonstrate the sensitivity of the model against the score, according to the variation of the amount requested by a customer.