# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
## 3 Research Method  
This section outlines the research methodology, covering data collection, preprocessing, analysis, and machine learning-based processing steps. This study follows a structured approach to ensure robust fraud detection in energy consumption patterns. Energy fraud detection has advanced with the integration of data Mining and machine learning techniques, significantly improving anomaly identification in electricity consumption [25]. While fraud detection is well-established in finance and healthcare, the energy sector is now leveraging technologies such as k-nearest neighbors (kNN), decision \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Approach Used** & **Strengths** & **Limitations** & **Our Contributions** \\ \hline [PERSON] et al. (2016) [10] & Supervised learning for fraud detection & Effective in detecting anomalies in smart meters & Lacks real-world validation, limited to smart grids & Integrates real-world data, extends to non-smart grids \\ \hline [PERSON] and [PERSON] (2020) [11] & Hybrid data mining for anomaly detection & Combines multiple detection methods & No large-scale dataset used & Applies to large-scale fraud detection using diverse data sources \\ \hline [PERSON] et al. (2008) [21] & SVM and random forest for electricity fraud & High precision in identifying fraud patterns & Limited generalization to new fraud cases & Proposes an adaptive, scalable framework \\ \hline \multirow{4}{*}{
\begin{tabular}{c} This study (Proposed Methodology) \\ \end{tabular} } & Hybrid ML approach using kNN, decision trees, and neural networks & High detection accuracy (89.5\%), applicable to smart and non-smart grids, real-world validation & Needs further optimization for real-time applications & First large-scale study integrating anomaly detection, scoring models, and data fusion for fraud detection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of fraud-detection approaches in the literature. trees, and random forest to detect non-technical losses like electricity theft and meter tampering [19]. Machine learning models classify consumption data based on historical patterns, reducing dependence on costly manual inspections [26]. Anomaly detection methods identify deviations in usage patterns, such as unusual spikes or irregular billing, enhancing fraud detection across both smart and non-smart grids [18]. Although smart meters improve real-time fraud detection, their adoption remains limited, necessitating standardized methodologies that work across diverse meter types [27]. A unified framework would enhance collaboration among energy providers, improving fraud detection efficiency and ensuring consistent monitoring practices [28]. In this research, we provide a cohesive approach that combines several methods, such as anomaly detection, pattern recognition, data mining, and machine learning. This all-encompassing strategy seeks to identify fraud while reducing the operating expenses related to manual audits and travel that come with conventional field inspections. This approach aims to increase fraud detection rates and lower the amount of money lost to non-technical losses by utilizing machine learning methods such as kNN, random forest, and neural networks. The suggested framework ensures uniformity and consistency in fraud detection procedures by addressing the need for a standardized system that can be used in both smart grid and non-smart grid scenarios. Moreover, the integration of AI-driven anomaly detection and data fusion techniques enables the identification of complex patterns that would otherwise be overlooked by conventional methods. By lowering non-technical losses and increasing overall operational efficiency, the use of these tools helps energy businesses discover fraud early. The dataset used in this study was obtained from E.ON Romania, encompassing real-world electricity consumption records from 38,500 clients between 2017 and 2023.