A quality control method for fraud detection on utility customers without an active contract
Document typeConference report
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectMODELOS Y METODOS BASADOS EN GRAFOS PARA LA COMPUTACION EN GRAN ESCALA (AEI-TIN2017-86727-C2-1-R)
Fraud detection in energy consumption has proven to be a difficult problem for current techniques. In general, the approaches used in this area are restricted to compute a fraud score for each client based on its behaviour. The problem gets much more complicated on customers with no contract, since the company does not have enough information from them to compute an accurate profile. On this paper, we introduce a semi-autonomous method that combines different machine learning algorithms and human knowledge to alleviate the lack of information to build a framework that detects fraud nimbly.
CitationComa-Puig, B., Carmona, J. A quality control method for fraud detection on utility customers without an active contract. A: ACM Symposium on Applied Computing. "The 33rd Annual ACM Symposium on Applied Computing: Pau, France: April 9-13, 2018". New York: Association for Computing Machinery (ACM), 2018, p. 495-498.