# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
## 4 The Proposed System's Goals, Approach, and Architecture
This reduces overfitting and improves accuracy, as reflected in the 88.83% accuracy. To conduct a deeper investigation into consumption irregularities, several key elements were analyzed. Sudden decreases in consumption, particularly significant and unexplained drops, may indicate potential tampering or unauthorized interventions. Comparing individual consumption against the locality's average helps identify discrepancies that warrant further scrutiny. Additionally, inconsistencies between gas and electricity consumption for the same consumer, especially large variations in usage trends, can signal anomalies in measurement systems or potential fraudulent activity. The classification of fraudulent energy consumption in this study was based on patterns learned by machine learning models from historical fraud cases, rather than relying solely on simple increases or decreases in consumption. The assumption that fraud correlates with sudden fluctuations is not arbitrary but rather grounded in the analysis of thousands of confirmed fraud cases. Figures 7-10 illustrate key consumption trends that were identified as fraudulent behaviors based on machine learning predictions. Instead of manually defining what constitutes fraud, the models were trained on a large dataset of confirmed fraud cases, allowing them to learn patterns that correlate with unauthorized energy consumption. The key indicators considered by the models include the following:  
Abrupt consumption drops despite no reported outages, often indicating the tampering or bypassing of the meter. Unusual consumption spikes following prolonged low usage, which can signal fraudsters temporarily reconnecting unauthorized lines. Irregular discrepancies between electricity and gas usage, suggesting manipulation of one utility while keeping the other stable. Repeated patterns of consumption anomalies, where usage deviates significantly from historical norms and neighboring households with similar profiles. To ensure scientific rigor, the ML models were validated using precision, recall, and F1-score metrics, confirming that the detected fraud cases were statistically significant and not just random fluctuations. Figure 7: Analysis of the evolution of consumption decrease. Figure 8: Cumulative analysis of consumption trends over time. Figure 2:0 Anomaly detection for Client V and Client VI. The decision-making process of the models was based on feature importance analysis, where certain attributes (e.g., monthly consumption changes, billing inconsistencies) were found to have the highest predictive power in fraud classification. The results shown in Figures 7-10 represent real-world cases where ML predictions identified high-risk consumers, which were later verified through field inspections. Unlike traditional rule-based methods, which rely on predefined thresholds, our approach learned dynamically from past fraudulent cases, adapting to new fraud tactics over time. By incorporating data-driven insights rather than manual assumptions, this approach improved fraud detection efficiency and reduced false positives, ensuring that legitimate consumption variations were not mistakenly flagged as fraud. Figure 7 shows that, beginning in September, there was a sharp decline in electricity use, perhaps suggesting that the measuring group was subjected to an unapproved intervention. Following this, there was a significant decrease in the amount of electricity used, almost reaching zero, which contrasted with the normal seasonal consumption pattern (where energy consumption tended to rise during colder months, as gas usage increased). This type of anomaly--where electricity usage does not align with typical seasonal variations--can be an early indicator of unauthorized manipulation of consumption data, which necessitates further investigation. This new approach analyzes cumulative consumption trends over time, highlighting discrepancies between medium locality consumption (blue line) and fraud-related consumption (red dashed line).