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.

翻译：随着各类线上与移动支付系统的兴起，交易欺诈已成为金融安全的重大威胁。本研究探讨了基于XGBoost与LightGBM的先进机器学习模型在构建更精准、更鲁棒的支付安全保护模型中的应用。为提升数据可靠性，我们对数据源进行了细致处理，并应用SMOTE（合成少数类过采样技术）以解决类别不平衡问题并改善数据表征。通过筛选高相关性特征，我们旨在强化训练过程并提升模型性能。我们对所提出的模型进行了全面的性能评估，并将其与包括随机森林、神经网络和逻辑回归在内的传统方法进行比较。利用精确率、召回率与F1分数等指标，我们严格评估了其有效性。详细的对比分析表明，SMOTE与XGBoost及LightGBM的结合为支付安全防护提供了一种高效且强大的机制。此外，在局部集成模型中整合XGBoost与LightGBM进一步展现出卓越性能。引入SMOTE后，新组合模型相较传统模型实现了近6%的显著提升，较其子模型亦有约5%的改进，取得了突出成果。