Comparative Study of Efficient Machine Learning Models for Real-Time Fraud Detection: CatBoost, XGBoost and LightGBM

Abstract Real-time performance is a key indicator in the design of fraud detection systems. This study focuses on three gradient boosting algorithms (CatBoost, XGBoost and LightGBM) and evaluates their performance in real-time fraud detection scenarios in terms of prediction accuracy, inference latency, and resource consumption. The experiments were conducted on a simulated high-frequency trading environment with a data stream platform. The results showed that LightGBM was the most advantageous in latency control, while CatBoost provided more stable responses while maintaining accuracy. This study offers a reference for building efficient and deployable online fraud detection systems.