用合成数据提升银行欺诈检测精度，解决样本不均衡问题。
Fraud Detection System for Banking Transactions
- 基于PaySim数据集，结合CRISP-DM流程构建机器学习检测框架。
- 经网格搜索调参后，XGBoost在平衡数据下准确率达99.7%。
- 适合金融风控团队快速部署，尤其适用于小样本欺诈场景。
数字支付系统的扩展使得在线金融交易规模与复杂性显著增加，从而加剧了欺诈行为的威胁。由于攻击策略不断演变且真实交易与欺诈交易之间存在巨大数量差异，有效检测欺诈变得尤为困难。本研究提出一种基于机器学习的欺诈检测框架，使用PaySim合成金融交易数据集，遵循CRISP-DM方法论，包括假设驱动的探索性分析、特征优化及对逻辑回归、随机森林、XGBoost、决策树等基线模型的对比评估。为应对类别不平衡问题，采用SMOTE数据增强技术，并通过GridSearchCV进行超参数优化以提升模型性能。实验结果表明，该框架能有效提高金融科技创新系统中的欺诈防范能力，具备良好的鲁棒性与可扩展性。
原文摘要 · Abstract (English)
The expansion of digital payment systems has heightened both the scale and intricacy of online financial transactions, thereby increasing vulnerability to fraudulent activities. Detecting fraud effectively is complicated by the changing nature of attack strategies and the significant disparity between genuine and fraudulent transactions. This research introduces a machine learning-based fraud detection framework utilizing the PaySim synthetic financial transaction dataset. Following the CRISP-DM methodology, the study includes hypothesis-driven exploratory analysis, feature refinement, and a comparative assessment of baseline models such as Logistic Regression and tree-based classifiers like Random Forest, XGBoost, and Decision Tree. To tackle class imbalance, SMOTE is employed, and model performance is enhanced through hyperparameter tuning with GridSearchCV. The proposed framework provides a robust and scalable solution to enhance fraud prevention capabilities in FinTech transaction systems. Keywords: fraud detection, imbalanced data, HPO, SMOTE
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