AI 中文总结
研究针对银行交易欺诈问题，利用TabNet深度学习架构，结合SMOTE等方法，在Kaggle数据集上测试五种模型，TabNet性能最佳，其稀疏特征选择方法提升可解释性等，为欺诈检测提供关键见解，支持可持续发展目标及银行实时应用。
AI 中文摘要
网上银行发展使欺诈操作增加，成为银行重大问题。本研究利用TabNet在印度实际银行交易的Kaggle数据集上，深入探讨对可解释、可扩展和一流欺诈检测系统的迫切需求。目标是通过提高交易异常检测准确性最大化运营风险管理，并通过透明模型确保合规。利用监督学习管道结合SMOTE平衡类，进行探索性数据分析。测试了五种深度学习架构，TabNet在预测性能上显著优于其他模型，其稀疏特征选择方法提高了可解释性等，研究结果为运营欺诈检测系统提供关键见解，支持可持续发展目标，对银行基础设施实时实施有实际用途。
英文摘要
The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal. We utilize a supervised learning pipeline that incorporates the Synthetic Minority Oversampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives. Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.
Comments27 pages, 15 figures, 5 tables
DOI：10.1016/j.iref.2026.104916
