Mathematical models commonly used to identify irregularities in measurement are based on percentile reduction analysis of the monthly consumption (normally from 20% to 30%) in relation to the previous months. This method tends to generate imprecise results, since it considers the value of the consumption as a rigid value and, therefore, it does not incorporate the seasonal effect in the loads topology of the consumer units. This work has intention to improve the identification of fraudulent customers using artificial intelligence methods, such as Neural Networks and Fuzzy Logic, implemented to a database of consumers of ELEKTRO a distributing utility of São Paulo State, Brazil. It also uses information on appliances ownership obtained via market research in ELEKTRO area, named PPH (Portuguese for this particular type of market research). In this dissertation, the main objective was to classify a group of consumers as solvent, insolvent and fraudulent. In order to achieve this task, a clustering was initially made using a Neural Network framework, more specifically a Kohonen Network, for the database available. It was then checked which of the groups had a minimum number of clients interviewed in the PPH. In order to have the classification of the clients in the three categories it was used Fuzzy Analysis. Selected data is also presented, considering the available database of the Company as well as the research environment, which had been taken from the PPH. Finally, it was checked the performance of the method against real data obtained from the utility and the results were very satisfactory.