# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
## 2 Related Work: The Importance of Fraud Detection and Existing Approaches  
Technical losses (TL) and non-technical losses (NTL) are the two main types of electricity losses that occur in power systems. When electricity is transmitted and distributed, technical losses inevitably occur. These losses are frequently brought on by transmission line resistance and other physical inefficiencies. On the other hand, illicit actions, like theft, meter tampering, and inaccurate invoicing, result in non-technical losses, also referred to as commercial losses. Among these, electricity theft represents the most significant portion of NTL, with increasingly sophisticated methods making it more challenging to detect. A report from the World Bank [20] highlights that electricity theft alone contributes to between 8 and 15% of total electricity distributed in developing countries. The expansion of fraudulent consumption has been exacerbated by the lack of advanced detection technologies. As shown in Figure 1, a typical electricity bill incorporates both technical and non-technical losses within categories such as distribution costs, system services, and transportation costs. The higher these losses, the more significant their impact on the final cost of electricity for consumers. Electricity prices play a pivotal role in incentivizing unauthorized consumption. In many countries, sharp increases in the price of electricity prompt some consumers to resort to tampering with meters in an attempt to lower their recorded usage and thereby reduce their bills. This illegal behavior places a burden on the entire power system by increasing the overall cost of energy distribution. For instance, in some regions of Latin America, electricity theft has led to an increase in costs by as much as 20%, a burden eventually passed onto legal users. The overall amount of electricity lost in power networks worldwide is made up of both economic losses and technical inefficiencies. While technical losses are inherent to  
Figure 1: Electricity Pricing Components. energy transmission, commercial losses--particularly those resulting from theft--pose a more significant threat to the financial and operational health of utilities. Commercial losses not only strain utility revenues but also undermine efforts to maintain efficient and reliable power systems. A McKinsey [21] report on energy theft estimates that global losses due to electricity theft amount to over USD 96 billion annually. Field inspections continue to be an essential method for identifying unauthorized energy consumption. Global studies have demonstrated that, when combined with advanced machine learning-based detection systems, targeted inspections significantly improve the accuracy of detecting both non-technical and technical losses. For instance, recent research has demonstrated that integrating smart metering with anomaly-detection models improves fraud detection accuracy and reduces commercial losses [9]. For example, in India, the introduction of smart meters and machine learning algorithms for analyzing consumption data reduced non-technical losses by up to 30%. Field inspections carried out in remote or hard-to-reach areas have uncovered substantial cases of energy theft, reinforcing the need for integrating data mining methodologies, exploratory data analysis (EDA), data aggregation, and data fusion techniques into fraud detection systems. By leveraging these advanced methodologies, utilities can not only improve the detection of fraudulent activity but also validate findings through physical inspections, ensuring the integrity and accuracy of their energy distribution networks. As utilities continue to implement more sophisticated fraud detection methods, the effectiveness of their operations improves, leading to reduced losses, enhanced financial stability, and greater fairness in the distribution of electricity. Based on recent data from ANRE [22], Romania's total electricity losses in 2023 were estimated at 8.75%, including both commercial and technical losses.