一种用于信用卡欺诈检测的注意力驱动异构图神经网络模型
An Attention-Driven Heterogeneous GNN Model for Credit Card Fraud Detection
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中文总结 AI 辅助
针对信用卡欺诈检测中数据不平衡和模式多变的问题，提出结合SMOTE-Tomek平衡技术与注意力驱动的异构图神经网络模型，实现高精度欺诈识别。
中文摘要 AI 辅助
全球向无现金经济的转型使信用卡成为数字交易的关键要素，因其使用便捷、速度快且在大多数地方被接受而备受赞誉。然而，对这种支付方式的日益依赖导致了与信用卡（CC）欺诈相关的风险升级。检测此类欺诈是一项困难的任务，因为模式不断变化，存在数据不平衡，并且需要同时识别合法交易和欺诈交易。本研究通过提出一个使用数据平衡技术和深度学习（DL）模型的信用卡欺诈检测（CCFD）框架来应对这一挑战。所提出的欺诈检测模型通过从Kaggle仓库收集名为“信用卡欺诈检测”的数据集进行训练和评估。由于数据集高度不平衡，我们利用合成少数类过采样技术（SMOTE）-Tomek技术来平衡数据集。此外，使用异构图神经网络（HGNN）模型对平衡后的数据集进行分类。HGNN模型使用异构图架构表示各种交易，并通过基于注意力的消息传递技术，能够考虑复杂关系、时间因素和用户行为。模型中SMOTE-Tomek的整合进一步增强了其识别欺诈交易的能力，同时降低了误报率。HGNN模型达到了99.97%的准确率、99.48%的F1分数、99.15%的精确率和98.97%的召回率。研究结果表明，该模型是有效的，可应用于现实世界的CCFD场景。
英文摘要
The global transition to a cashless economy has placed credit cards as the key element of digital transactions, acclaimed for their easy use, speed, and acceptance in most places. However, the growing dependence on this payment method has led to an escalation of the risks associated with credit card (CC) fraud. Detecting this type of fraud is a difficult task because the patterns are constantly changing, there is a data imbalance, and it is necessary to identify the legitimate transactions and the fraud ones at the same time. This study addresses this challenge by proposing a credit card fraud detection (CCFD) framework using a data balancing technique and a deep learning (DL) model. The proposed fraud detection model is trained and evaluated by collecting the dataset called Credit Card Fraud Detection from the Kaggle repository. As the dataset is highly imbalanced, we utilized the Synthetic Minority Oversampling Technique (SMOTE)-Tomek technique to balance the dataset. Further, the balanced dataset is classified using the Heterogeneous Graph Neural Network (HGNN) model. The HGNN model represent various transactions using a heterogeneous graph architecture and by using an attention based message passing technique, it managed to consider the complex relationships, time factors, and user behavior. The integration of SMOTE-Tomek in the model further boosted its capacity to identify fraudulent transactions, while lowering the rate of false positives. The HGNN model attained a 99.97% accuracy, a 99.48% F1-score, a 99.15% precision, and a 98.97% recall. The findings indicates that this model is effective and can be applied to real-world CCFD scenarios.
发表机构
- Sathyabama Institute of Science and Technology（萨蒂亚巴玛科学技术学院）
机构由 AI 辅助整理，请以论文原文为准。
