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A wide range of strategies has been proposed to identify and prevent non-technical electricity losses resulting from fraudulent activities. Among these, machine learning algorithms and data-driven methods have become increasingly prominent compared to conventional approaches, owing to their superior performance, leading to growing adoption in recent years. This paper introduces a novel two-step method for detecting fraudulent Non-technical losses (NTLs) in smart grids. The initial step is to transform the time-series data by incorporating additional features extracted from the publicly available State Grid Corporation of China (SGCC) dataset. These features are derived following the detection of sudden changes in electricity consumption patterns, using the sum of finite differences, the Auto-Regressive Integrated Moving Average model, and the Holt-Winters model. Subsequently, five classification models are applied to train and evaluate a fraud detection system using the SGCC dataset. The performance evaluation shows that the Gradient Boosting Machine delivers the best results among the five models. This two-step procedure enables classification models to outperform previously reported top-performing techniques in terms of accuracy, F1-score, and other key metrics for non-technical loss detection.