One of the biggest problems faced by all companies that provide sanitation and water distribution services is that of losses arising from (commercial) irregularities. Among the countries with more than 20 million inhabitants that suffer the most from this type of loss, Brazil occupies the 14th position with 40% of losses in dis-tribution. Company A, the case study of this work, is a Brazilian company that ope-rates in the sanitation and water distribution sector and operates mainly in 3 regions, with average percentage values of losses, in 2021, of 19%, 30 % and 43%, respec-tively. These losses derive from many problems, but the main ones arise from fraud in the connections of water meters, for example: clandestine connections, by-pass and branch derivation. The main way to combat this type of fraud is through custo-mer inspections. Generally, a set of heuristics is used to identify the suspect of such fraud or irregularity, but these methods do not return good accuracy. At Company A, the accuracy achieved through inspections varies from 3% to 17% from region to region. Thus, it is concluded that the procedure is not effective. Therefore, the objective of this work is to develop an intelligent system that can identify, with greater accuracy, the consumption profile of the customer who has the fraud. The developed system is composed of two methodologies based on several supervised machine learning algorithms. The first uses a filter in order to group customers with similar profiles. The second makes use of an evolutionary algorithm inspired by quantum computing to search for hyperparameters and attributes. In addition, both consider committees and explore the use of historical and exogenous variables re-levant to the context. The results obtained were superior in the evaluations, when compared to those verified in Company A, reaching up to 44% of success rate.