# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
###### Abstract  
The increasing cost of energy and the prevalence of electricity theft pose significant financial and operational challenges for energy providers. Traditional fraud detection methods often fail to identify sophisticated unauthorized consumption, particularly in non-smart-grid environments. This study proposes an advanced machine learning-based methodology for detecting energy fraud, leveraging real-world data from energy distribution networks. This approach integrates multiple machine learning models--k-nearest neighbors (kNN), decision trees, random forest, and artificial neural networks (ANNs)--to improve detection accuracy and efficiency. Experimental results demonstrate an 89.5% fraud detection accuracy, significantly outperforming conventional methods. Furthermore, the implementation of this model led to an estimated financial loss reduction of EUR 45,200. By analyzing historical consumption patterns, anomaly detection techniques, and geospatial data, the proposed system enhances fraud detection capabilities across both smart and non-smart grids. Future research will focus on real-time detection, scalability, and the integration of external data sources to further refine predictive accuracy. e 2025 15 3361 10 January 2025 7 March 2025 15 March 2025 15 March 2025 15 March 2025 15 March 2025 15 March 2025 15 March 2025 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 1  
traditional fraud detection methods primarily relied on statistical approaches, modern systems have increasingly integrated ML-driven anomaly detection techniques to manage large datasets and identify sophisticated fraudulent activities. In the energy sector, fraud--especially electricity theft--poses a significant challenge. Several studies have explored techniques for detecting fraud in residential and industrial electricity consumption, highlighting the effectiveness of machine learning models and data-driven approaches in improving fraud detection rates [7; 8; 9]. For example, [PERSON] et al. [10] and [PERSON] and [PERSON] [11] proposed anomaly detection models to identify unusual energy usage patterns. However, a major limitation of these studies is their reliance on simulated or synthetic datasets, which may not accurately reflect real-world fraud cases. To bridge this gap, this study proposes a generalized methodology that uses real-world data to improve fraud detection rates while optimizing the use of resources such as manpower and financial costs. Similar to recent advancements in graph anomaly detection, where energy-based models (EBMs) are employed to identify anomalous points in complex structures, the methodology proposed in this study leverages machine learning techniques to detect irregular consumption patterns, thereby enhancing fraud detection accuracy and efficiency [12]. Energy-based maintenance (EBM) is a developing research field that applies anomaly detection techniques to energy consumption signals, primarily for predicting equipment failures and operational inefficiencies. Studies highlight its transition from predictive maintenance to EBM, emphasizing its role in cleaner production and improved reliability [13]. Its application in hydraulic machinery has demonstrated enhanced maintenance efficiency [14], while non-invasive data acquisition methods further improve resource efficiency in machine tools [15]. Although originally designed for maintenance purposes, EBM's fundamental approach to detecting anomalies in energy patterns can be leveraged for fraud detection by identifying irregularities indicative of unauthorized consumption. Recent studies have explored the potential of these techniques for fraud detection in the energy sector [16], highlighting how energy consumption deviations can be used to identify unauthorized interventions and electricity theft. Initially developed for industrial maintenance, EBM employs advanced signal analysis to identify deviations in energy consumption, a methodology that aligns closely with fraud-detection techniques.