Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integrated

Understand

With the rise of various online and mobile payment systems, transaction fraud has become a significant threat to financial security.

• This study explores the application of advanced machine learning models, specifically based on XGBoost and LightGBM, for developing a more accurate and robust Payment Security Protection Model.
• To enhance data reliability, we meticulously processed the data sources and applied SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance and improve data representation.
• By selecting highly correlated features, we aimed to strengthen the training process and boost model performance.