# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
## 4 The Proposed System's Goals, Approach, and Architecture
The accuracy results presented in Figure 6 illustrate the models' ability to detect fraudulent activities across different machine learning techniques. To push the boundaries of traditional machine learning applications, the following innovative methods are proposed:  
Hybrid Algorithm Combining kNN and Random Forest:  
The benefits of both random forest and kNN were combined in a new hybrid technique. While kNN excels in proximity-based classification, random forest's ensemble approach helps reduce variance and overfitting. By combining these, we aimed to significantly improve the accuracy of fraud detection. Using Convolutional Neural Networks (CNNs) for Temporal Data:  
Although CNNs are typically used in image analysis, we applied them to temporal data for detecting anomalies in energy consumption over time. CNNs can capture local patterns in time series data (e.g., weekly or monthly consumption spikes) that may indicate  
Figure 6: Model performance comparison. -fraudulent behavior. This approach enabled us to detect subtle, recurring anomalies that other models may overlook. The performance of several machine learning models for identifying energy fraud is displayed in the table and image above. The models evaluated include the following:  
K-Nearest Neighbors (kNN): This model achieved a high accuracy of 88.67%, though its recall and F1 scores were relatively low, indicating it may struggle with detecting fraudulent cases. Decision Trees: The decision tree model performed slightly lower in accuracy (84.83%), but with better recall (20.63%) compared to kNN. Its overall performance was more balanced. Random Forest: The random forest model showed the highest accuracy (89.00%) and a slightly higher precision, but like kNN, it struggled in terms of recall, making it less effective in detecting all fraudulent instances. Artificial Neural Networks (ANNs): This model had the highest accuracy (89.50%), but its precision and recall were quite low, suggesting that while it correctly identifies non-fraud cases, it may not capture fraudulent cases effectively. Bagging (Ensemble): The Bagging method showed strong accuracy (88.83%) with improved precision compared to the others, but again, it had challenges with recall. While random forest and artificial neural networks (ANNs) demonstrated the highest accuracy, the low recall values across models suggest that detecting fraudulent instances remains a challenge. To improve this, a hybrid approach combining models like kNN and random forest or utilizing advanced techniques like convolutional neural networks (CNNs) for temporal data might help address the recall gap and provide a more robust detection system. Why Bagging Performed Well in Terms of Accuracy: The Bagging (Bootstrap Aggregating) method is an ensemble technique that reduces variance by merging predictions made by several decision trees that were trained using different portions of the training data. Here is why Bagging showed high accuracy in this scenario:  
Variance Reduction: Bagging helps mitigate the high variance often observed in individual decision trees by averaging their predictions. The formula for Bagging involves aggregating multiple weak learners (decision trees):  
\[\hat{y}_{\text{Bagging}}\frac{1}{n}{\sum_{i=1}^{n}\hat{y}Tree_{i}}, \tag{1}\]  
where \(\hat{y}Tree_{i}\) is the prediction from the \(i\)-th decision tree and n is the number of trees in the ensemble. By averaging the predictions, Bagging stabilizes the model and improves overall accuracy. This leads to strong performance, particularly in datasets where variance (overfitting) is a concern. Out-of-Bag (OOB) Error: Bagging also provides a built-in method of estimating generalization error using Out-of-Bag error:  
\[OOB\ error=\frac{1}{n}{\sum_{i=1}^{n}y_{i}(y_{i}\
eq\hat{y}\ OOB_{i})}, \tag{2}\]  
where \(i\) is an indicator function, and \(\hat{y}OOB_{i}\) is the prediction for the \(i\)-th instance based on the trees that did not include this instance in the bootstrap sample.