Fraud detection system for high and low voltage electricity consumers based on data mining

This work presents two computational system for fraud detection for both high and low voltage electrical energy consumers based on data mining. This two kinds of consumers demanded different approaches and methodologies. The first is based on SOM (Self-Organizing Maps), which allows the identification of the consumption profile historically registered for a consumer, and its comparison with present behavior. The second is based on a hybrid of data mining techniques. From the consumer behavior pre-analysis, electrical energy companies will better direct its inspections and will reach higher rates of correctness. The validation and results showed that the two systems are efficient on the cases of lower consumption resulted by fraud.