• classifier
• financial data
• fuzzy logic
• genetic algorithm
• prediction

The amount of information that companies have to manage at the same time to get the best value for their business is growing very fast through the use of IT systems. Usually is not obvious what to do with all this amount of information. For this reason there were been developed many theories and “intelligent” application that are able to give aggregate useful data to the end user.
In this work, we developed a Fuzzy Logic Classification Evolutionary System (FLCES) that is able to get a dataset as input and classify the inputs as “bad” customer or “good” customer, in order to help the management of the company to develop the appropriate strategy. We developed two different approaches that can be used individually or can be used in a cascade structure. The proposed FLCES approaches are based on a genetic algorithm that aims to increase the performance of the fuzzy classifier as well as to increase the interpretability of the rule base belonging to the fuzzy classifier. The implemented approach optimizes the length of the rules, making them more interpretable, and hence, improving the performance. A new approach for gene encoding has also been implemented reducing memory, speed and time consumption.