This research presents the development and implementation of an integrated artificial intelligence model for electricity theft detection, combining Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The primary objective was to create a more accurate, efficient, and scalable method for identifying fraudulent electricity consumption patterns. Our CNN-SVM hybrid model leverages CNNs for automatic feature extraction from complex consumption data and SVMs for effective classification. This synergy allows for superior performance in detecting subtle anomalies indicative of electricity theft. The methodology involved pre-processing a large dataset of electricity consumption records, training the CNN to extract relevant features, and optimising the SVM classifier to distinguish between normal and fraudulent patterns. We evaluated the model's performance using metrics including accuracy, precision, recall, F1-score, and ROC AUC. Results demonstrated that our integrated CNN-SVM model significantly outperformed conventional machine learning techniques and standalone models in electricity theft detection. The model achieved an accuracy of 96.6%, with a precision of 97.2% and a recall of 96.1%. Comparative analysis against other state-of-the-art algorithms revealed consistently superior performance across all evaluation metrics. To enhance practical applicability, we developed and deployed a web application that implements the model, allowing for user-friendly interaction and real-time theft detection. This addition bridges the gap between research and real-world implementation, providing utility companies with an accessible tool for fraud detection. The study also explored the model's potential for real-time application and scalability to large-scale utility operations. Our findings suggest that the computational efficiency of the CNN-SVM model, coupled with the web application, offers utility companies a powerful and accessible tool for rapid response to potential fraud. This research contributes to the field of electricity theft detection by introducing a novel, high-performance AI model with a practical web-based implementation. The proposed approach not only improves detection accuracy but also offers potential for immediate real-world application, paving the way for more effective fraud prevention in the utility sector.
