# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
## 5 Results and Discussions  
In the investigation presented in this article, energy fraud detection was conducted using a dual approach that combined advanced machine learning models with field inspections to analyze discrepancies in gas and electricity consumption. The anomaly detection models (Client V, VI, IX, and X) were applied during field investigations, guiding five specialized teams from DELGAZ GRID--comprising field inspectors and data analysts--to assess regions where anomalous consumption behaviors had been identified. Over a three-week period, these teams carried out on-site verifications during standard work hours (8:30 a.m.-4:00 p.m.), focusing on 150 high-risk areas flagged by data-driven models as potential fraud cases. The methodology targeted specific fraud indicators, such as prolonged low consumption followed by sudden spikes, simultaneous drops in electricity and gas usage, and inconsistencies between the two, which suggested potential meter tampering. To validate these anomalies, field teams performed real-time inspections, prioritizing clients whose consumption patterns exhibited a high probability of unauthorized intervention based on both historical and real-time data. By directly verifying these flagged anomalies, the teams ensured that machine learning predictions were rigorously tested under operational conditions, reinforcing the reliability and effectiveness of the fraud detection system described in this study. To evaluate the effectiveness of different machine learning models in fraud detection, we conducted comparative experiments measuring accuracy, precision, recall, F1-score, and AUC-ROC. Table 3 presents the performance metrics for each model. The results indicate that random forest and artificial neural networks achieved higher accuracy, while the proposed hybrid model significantly improved detection performance by combining ensemble learning with anomaly detection techniques. The following machine learning models were used throughout the data analysis phase: k-nearest neighbors (kNN), decision trees, random forest, and artificial neural networks (ANNs). The analysis showed significant discrepancies in energy usage that were used to prioritize which clients should be inspected. For example, in Client IX, where gas consumption remained stable but electricity consumption dropped to zero, the field team identified meter tampering in 70% of cases. Similarly, in Client X, where gas consumption showed an unexpected spike, further investigation uncovered bypassing systems installed to avoid accurate readings. The field inspections revealed numerous fraudulent practices, confirming the effectiveness of the data-driven fraud detection models. Among the most prevalent methods identified was meter tampering, where physical meters were manipulated to under-report electricity usage. This tactic was widely used to artificially lower consumption readings, enabling consumers to evade full payment for their actual energy usage. Another significant method detected was bypassing and stuffing, in which meters were deliberately circumvented, leading to a complete cessation of recorded electricity consumption. This was particularly evident in cases such as Client IX, where no measurable energy usage was recorded despite clear indications of ongoing consumption. Additionally, inspectors encountered false readings, where meter data was intentionally altered to reflect artificially high or low values. This was especially noticeable among clients whose consumption patterns displayed sudden spikes following extended periods of unusually low usage. Such anomalies suggested deliberate manipulation of meter readings, further confirming the presence of fraudulent activities. These findings underscore the necessity of integrating machine learning-based anomaly detection with real-world inspections, ensuring that fraudulent practices can be identified and mitigated more effectively.