用可解释AI和集成学习提升金融欺诈检测的准确率与透明度。
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
- 融合XGBoost、LightGBM、CatBoost的堆叠集成模型
- 在59万条数据上达99%准确率和0.99 AUC-ROC
- 结合SHAP、LIME等工具实现决策可解释，适合监管合规场景
传统机器学习模型常以预测精度为先，牺牲模型透明性，导致机构难以满足监管要求并失去利益相关方信任。本文提出一种欺诈检测框架，采用XGBoost、LightGBM和CatBoost的堆叠集成方法，并引入可解释人工智能（XAI）技术增强决策透明度。通过SHAP进行特征选择，识别关键特征；利用LIME、部分依赖图（PDP）和置换重要性（PFI）解释模型预测。基于包含超过59万条真实交易记录的IEEE-CIS欺诈检测数据集进行评估，模型达到99%准确率和0.99 AUC-ROC，优于多个近期方法。结果表明，高精度与高可解释性可兼得，有助于构建更伦理、可信的金融欺诈检测系统。
原文摘要 · Abstract (English)
Traditional machine learning models often prioritize predictive accuracy, often at the expense of model transparency and interpretability. The lack of transparency makes it difficult for organizations to comply with regulatory requirements and gain stakeholders trust. In this research, we propose a fraud detection framework that combines a stacking ensemble of well-known gradient boosting models: XGBoost, LightGBM, and CatBoost. In addition, explainable artificial intelligence (XAI) techniques are used to enhance the transparency and interpretability of the model's decisions. We used SHAP (SHapley Additive Explanations) for feature selection to identify the most important features. Further efforts were made to explain the model's predictions using Local Interpretable Model-Agnostic Explanation (LIME), Partial Dependence Plots (PDP), and Permutation Feature Importance (PFI). The IEEE-CIS Fraud Detection dataset, which includes more than 590,000 real transaction records, was used to evaluate the proposed model. The model achieved a high performance with an accuracy of 99% and an AUC-ROC score of 0.99, outperforming several recent related approaches. These results indicate that combining high prediction accuracy with transparent interpretability is possible and could lead to a more ethical and trustworthy solution in financial fraud detection.
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