Fraud Detection in Utilities Using Data Analytics and Geospatial Analysis

Abstract

One of the great challenges of any utilities around the world is the control of losses, which have different causes that are usually classified as technical (accuracy of equipment, leaks and breaks, construction and maintenance procedures) and NON-technical (thefts and frauds), affecting issues such: as investments for the expansion and maintenance of the networks, the profitability of the shareholders and even the continuity of the service. This article proposes a methodology for the detection of NON-technical losses common to any utility company, based on data analytics on business information enriched with data from third parties through geospatial analysis from its geographic location and market segmentation, which allows finding patterns on anomalous situations through supervised (on historical information) and unsupervised (if no information is available) machine learning models. The results of different classification algorithms used in data analytics were analyzed and the one with the highest accuracy and lowest type two error rates (false negatives) was selected to perform field verification work. The methodology was implemented in a natural gas distribution company and was contrasted with methodologies proposed by other authors for electric energy distribution companies, who consider that the problem should be addressed based on an analysis of historical consumption and its deviations. The results obtained with the proposed methodology improve the accuracy and sensitivity of the models by more than 20% and decreases false negatives by the same percentage, facilitating the verification and normalization of customers in anomalous situations and/or fraudulent conditions.