As e-commerce continues to grow rapidly, the threat of online fraud has become a critical concern, posing significant risks to businesses and consumers alike. Traditional fraud detection systems struggle to keep pace with the evolving strategies of fraudsters, especially given the challenges of high-dimensional data, class imbalance, and limited interpretability. In this work, we introduce LEMON, a Light and Efficient fraMework for e-cOmmerce Fraud DetectioN, that uses Discriminating Code Set (DCS) in graph theory to attain an intrinsic set of features for enhancing the performance and efficiency of fraud detection models. We evaluate LEMON on the IEEE-CIS Fraud Detection dataset, comparing it with baseline techniques including manual feature engineering (Kaggle Top-1), XGBoost-based feature importance, and a hybrid LEMON+XGBoost model. Our results show that the approach not only achieves the highest ROC-AUC (0.976) and F1-score (0.78) but also significantly reduces training time. These findings highlight the effectiveness of DCS in selecting a compact, highly discriminative feature subset that improves fraud detection in real-world, large-scale e-commerce systems.