# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
## 5 Results and Discussions
The confusion matrix generated for this study, which recorded true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values for fraud detection, allowed us to quantify the success of the machine learning models. Table 4 below outlines the field inspection results and corresponding detection efficiencies. The overall detection efficiency across all groups reached 70.67%, marking a substantial improvement compared to earlier randomized inspections, which had an efficiency rate of only 3.27%. This highlights the significant impact of data-driven inspections in enhancing fraud detection rates. However, a key limitation of the field investigations was the restricted access to certain high-risk areas, with approximately 20% of targeted locations denying entry to field teams. This lack of access prevented the verification of suspected fraudulent activities in these areas. Based on prior experience, such refu \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **AUC-ROC (\%)** & **Execution Time (s)** \\ \hline k-nearest neighbors (kNN) & 85.20\% & 82.40\% & 79.60\% & 81\% & 87.50\% & 1.80 \\ Decision trees (DT) & 88.50\% & 86.20\% & 84.90\% & 85.50\% & 90.10\% & 0.90 \\ Random forest (RF) & 91.30\% & 89.70\% & 87.80\% & 88.60\% & 93.20\% & 2.50 \\ Artificial neural networks (ANNs) & 89.50\% & 88.10\% & 86.30\% & 87.20\% & 92\% & 4.30 \\ Proposed hybrid model & 94.20\% & 92.50\% & 90.80\% & 91.60\% & 96.10\% & 3.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of machine learning models for fraud detection. ciated with illicit energy consumption practices, as, historically, sites that deny inspections have exhibited a higher likelihood of unauthorized interventions. If these locations are considered potential fraud cases, as indicated by suspicious patterns detected by Client X, the overall fraud detection efficiency would increase further, as detailed in Table 5.  
By including \"no access\" cases, the overall detection efficiency improved to 75.83%, highlighting the importance of access in verifying suspected fraud. In addition to analyzing fraud detection efficiency based on field inspections and machine learning models, further investigations were conducted to understand the financial implications of unauthorized consumption. The results demonstrated that fraud detection in Client IX and Client X alone yielded a significant increase in detection accuracy when machine learning-driven field inspections were employed. However, expanding this methodology to include \"no access\" cases pushed detection efficiency even higher, as shown in Table 6.  
Field teams, after validating machine learning predictions, identified a false negative rate that indicated that some genuine cases of fraud might have been missed during the initial assessments. By correlating those missed cases with additional data fusion techniques and anomaly detection thresholds, the models were fine-tuned, yielding more precise future predictions. The findings indicate that incorporating cases in which access to meter inspections was denied led to a 7.37% increase in detection efficiency. This suggests that refusal to grant access may be a deliberate attempt to conceal unauthorized energy consumption, reinforcing the importance of monitoring such cases closely. The fraud detection methodology developed in this study was designed to be independent of meter type, ensuring its applicability to both smart and non-smart meters by focusing on consumption pattern analysis rather than relying on meter technology. Key fraud indicators included sudden drops in electricity consumption while gas usage remained stable, suggesting potential underreporting or diversion of electricity, as well as unexplained consumption spikes following extended periods of low usage, raising