The main aim of this work is to propose and develop a methodology base don fuzzy models for segmentation and characterization of segments comprising the bank segment, allowing broad knowledge of client profiles, better suiting market needs, hence offering better financial results. The methodology proposed in this work may be divided into three main modules: data collection and treatment; definition of segments; and characterization and classification of segments. The first module, denominated data collection and treatment, encompasses marketing research used in data collection and application of techniques for pre-processing of data, for data trimming (removal of outliers and missing values) and normalization. The definition of segments adopts the Fuzzy C-Means (FCM) grouping model in identifying groups of clients with similar characteristics. The choice for this grouping model is due to the possibility of analyzing the membership coefficient of each client in connection with the different groups, thus identifying clients among segments and consequently elaborating effective actions for their transition to or maintenance in the segments of interest. The module of characterization and classification of segments is based on a Fuzzy Inference System. In the first stage, the most relevant variables from the information standpoint are selected, for application in the process of rule extraction. The rules extracted are then used in the construction of a fuzzy inference system dedicated to classifying new clients. This system allows marketing analysts to contribute with new rules or modify those already extracted, making the model more robust and the turning market segmentation into a tool accessible to all using it. This methodology was applied in the market segmentation of Banco da Amazônia, stte- contrlled bank acting in the Amazon region, with main focus of which is fostering the region´s development. The application of fuzzy models in the case study generated good results in the definition of segments, with average silhouette value of 0.7, and accuracy of 100% for client base classification. Furthermore, the use of these models in market segmentation allowed the analysis of clients classified between segments and the characterization of those segments by means of a set of rules, improving the analyses made by marketing analysts.