  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. We conducted thorough performance evaluations of our
proposed models, comparing them against traditional methods including Random
Forest, Neural Network, and Logistic Regression. Using metrics such as
Precision, Recall, and F1 Score, we rigorously assessed 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. Moreover, the integration of XGBoost and LightGBM
in a Local Ensemble model further demonstrated outstanding performance. After
incorporating SMOTE, the new combined model achieved a significant improvement
of nearly 6\% over traditional models and around 5\% over its sub-models,
showcasing remarkable results.
