# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
## 4 The Proposed System's Goals, Approach, and Architecture
The shaded gray area indicates a long-term reduction period observed in fraudulent cases. Key differences in consumption patterns reveal distinct trends. Medium consumption follows a relatively smooth decline, with a gradual decrease and a recovery starting in the final quarter of the year. In contrast, fraud-related consumption exhibits a sharper and more pronounced drop, occurring much earlier and remaining consistently low over  
Figure 2:1 Anomaly detection for Client VII and Client VIII. several months (July to November). This prolonged reduction may indicate sustained fraudulent behavior or unauthorized interventions affecting energy usage. This graphical representation focuses on the gradual reduction in consumption, providing insights into cases where fraud manifests over an extended period rather than through sudden drops, helping to identify sustained fraudulent activity. Furthermore, as Figure 8 illustrates, the average consumption of consumers who engage in unauthorized interventions on the measuring equipment is substantially less than the average for the area, especially in Iasi County. However, differences in consumption from the average for the area by itself do not prove that inappropriate intervention has taken place. Analyzing the differences in a consumer's gas and electricity usage offers a more reliable approach to detecting unauthorized actions. This method provides clearer indications of potential fraud, as highlighted by the following observations. A reduction in gas consumption should typically correspond to an increase in electricity consumption. If gas usage decreases while electricity consumption remains constant or also declines, further investigation is necessary, as this anomaly may indicate unauthorized interference with metering equipment. Additionally, zero electricity consumption while gas is being used is highly unusual. In households where alternative energy sources are not available, such a pattern suggests possible meter tampering or fraudulent activity. These statements are corroborated when pre-checks confirm that no alternative energy sources, such as solar or electric heating, are present at the location. By analyzing both gas and electricity consumption trends, unauthorized interventions can be identified with greater precision. Figure 4 demonstrates this long period of reduced consumption, signaling potential fraudulent activity. This combined approach provides a more effective method for detecting irregularities, ensuring that anomalies are not overlooked by focusing solely on electricity or gas consumption in isolation. Furthermore, the integration of data mining techniques, as proposed in this thesis, enhances detection accuracy by applying supervised learning models that assign fraud risk scores based on historical anomalies. These models analyze deviations in gas and electricity consumption patterns, correlating them with known fraud cases to establish predictive indicators. By incorporating factors such as abrupt consumption shifts, inconsistencies between utility usages, and unexplained billing anomalies, the models generate fraud probability scores for each consumer. This scoring system serves as a crucial tool for prioritizing inspections, enabling authorities to focus on high-risk cases rather than conducting manual or randomized verifications. For example, cases where gas consumption decreases while electricity usage remains stable may be flagged as lower risk, while those showing simultaneous reductions in both could trigger immediate alerts. By ranking suspicious activities based on severity, energy providers can optimize resource allocation, reduce operational costs, and enhance the efficiency of fraud detection efforts. Additionally, the integration of external data sources--such as household size, weather conditions, and historical billing trends--further refines the accuracy of fraud assessments.