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 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 used 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.We conducted thorough performance evaluations of our proposed
models, comparing them against traditional methods including Random Forest,
Neural Network, and Logistic Regression. Key metrics such as Precision, Recall,
and F1 Score were used to rigorously assess their effectiveness.Our detailed
analyses and comparisons reveal that the combination of SMOTE with XGBoost and
LightGBM offers a highly efficient and powerful mechanism for payment security
protection. The results show that these models not only outperform traditional
approaches but also hold significant promise for advancing the field of
transaction fraud prevention.

利用 XGBoost 和 LightGBM 等机器学习模型，通过应用先进的机器学习模型和数据处理技术，本研究开发了一种更准确、更稳健的支付安全保护模型，有效提升了交易欺诈预防的能力。