Employing Gradient Boosting and Anomaly Detection for Prediction of Frauds in Energy Consumption - Albiero, Beatriz; Uyrá, Estevo; Vilarino, Ramon; Silva, Juliano; Souza, Tales; Santos, Ricardo dos; Yamouni, Sami; Vicente, Renato (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2019: Anais do XVI Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2019.9345, 2019)
Energy fraud is a critical economical burden for electric power organizations in Brazil. In this paper we present the application of cutting-edge Machine Learning algorithms, namely XGBoost and Isolation Forest, for prediction of irregularities in electrical energy consumption. By using a Logistic Regression model as a benchmark, we show that the use of XGBoost results in a significant improvement in the F1-score for fraud predictions in two different scenarios: with and without inspection history features. Moreover, we also propose the use of the Isolation Forest algorithm for detection of anomalies in electrical energy consumption. We show that this approach may be useful in the case of lack of inspection history features, surpassing dummy classifiers.