Abstract
The fraudulent behavior in drinking water consumption is a major problem faced by water treatment companies due to generates significant economic losses. Characterizing fraudulent drinking water consumption is a complex task, based mainly on experience, and which presents the challenge of the constant incorporation of new clients and the variation in monthly consumption. In this research, data mining techniques are used to characterize and predict fraud in the consumption of drinking water. For this, historical information on consumption was used. The techniques applied showed high predictive performance and its application will allow focusing efficiently resources oriented to avoid this type of fraud.
Keywords: data mining, machine learning, drinking water, fraud detection.
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