# Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms
## 4 The Proposed System's Goals, Approach, and Architecture
Figure 4: Billing amounts over time. Figure 3: Consumption anomalies. Figure 5 presents the process of gathering and processing data, illustrating the key stages involved in data collection, preprocessing, and analysis. These steps are essential for ensuring data quality and reliability, which are crucial for accurately identifying fraudulent activities. To enhance the detection of energy fraud, we employed several machine learning algorithms. These models are known for their robustness in dealing with large datasets and are widely used for both classification and anomaly detection tasks:  
K-Nearest Neighbors (kNN): Using the distance between data points as its basis, this algorithm effectively classifies instances by placing them in the most prevalent class among their closest neighbors. It works well in detecting consumption patterns similar to previous fraudulent behaviors. Decision Trees: An easy-to-use yet effective approach that divides data recursively according to feature values to make decisions. It provides easily interpretable results, which is particularly useful for understanding the logic behind fraud detection decisions. Random Forest: Many decision trees are combined in this ensemble learning technique to increase classification accuracy and reduce overfitting. For feature significance analysis, it performs exceptionally well and is more resilient to dataset noise. Artificial Neural Networks (ANNs): Complex patterns and correlations in data can be captured by neural networks. They are particularly effective for modeling nonlinear interactions and can learn intricate consumption patterns that may indicate fraudulent activity. To improve the detection of fraudulent energy consumption, this study employed an artificial neural network (ANN) model trained on historical consumption patterns. The ANN is structured as a feedforward neural network with three hidden layers, optimized for binary classification (fraudulent vs. non-fraudulent consumption). The input layer consists of multiple features extracted from customer consumption data, including historical usage trends, anomaly scores, and meter tampering indicators. The hidden layers utilize ReLU (rectified linear unit) activation functions to introduce non-linearity, allowing the model to capture complex fraud patterns. Specifically, the ANN comprises three hidden layers with 64, 32, and 16 neurons, respectively, ensuring a balance between model complexity and computational efficiency. Figure 5: Gathering and processing data. For fraud classification, the output layer consists of a single neuron with a Sigmoid activation function, which converts outputs into probability scores between 0 and 1, where values closer to 1 indicate a higher likelihood of fraud. Ensemble Methods: These techniques enhance overall performance by combining several machine learning models. Well-known ensemble methods like Boosting and Bagging (Bootstrap Aggregating) can increase the accuracy and resilience of fraud detection. The machine learning models developed in this study were designed to identify fraudulent energy consumption patterns by classifying electricity usage records as either fraudulent or non-fraudulent. The classification was based on a combination of historical billing behavior, consumption trends, and anomaly detection techniques. Among the tested approaches, the best-performing model achieved an accuracy of 89.5%, demonstrating a significant improvement over conventional fraud detection methods. These findings highlight the potential of machine learning in improving fraud detection rates, reducing financial losses, and optimizing resource allocation for energy providers. To validate the effectiveness of these models, the testing dataset from 2021 to 2023 was used. This dataset contained electricity consumption records that had not been seen during training, ensuring that the models' predictions reflected real-world scenarios.