用图神经网络减少信用卡欺诈误判，提升客户沟通效率。
Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow
- 引入关系图卷积网络捕捉交易间关联，优化欺诈识别
- 在IBM数据集上将误判率降低18.6%，保持高检测准确率
- 适合风控系统优化、反欺诈平台研发人员参考
信用卡欺诈自上世纪以来持续存在，给行业带来重大经济损失。最有效的防范方式是联系客户核实可疑交易，但现有系统常误判正常交易，导致不必要的拒绝，破坏用户体验并削弱客户信任。频繁的误报会引发客户不满、增加投诉，并降低安全感。为解决此问题，我们提出一种融合关系图卷积网络（RGCN）的欺诈检测框架，利用交易数据的关联结构提升欺诈识别的准确性和效率，减少对客户直接确认的需求，同时保持高检测性能。实验基于IBM信用卡交易数据集进行评估，验证了该方法的有效性。
原文摘要 · Abstract (English)
Credit card fraud has been a persistent issue since the last century, causing significant financial losses to the industry. The most effective way to prevent fraud is by contacting customers to verify suspicious transactions. However, while these systems are designed to detect fraudulent activity, they often mistakenly flag legitimate transactions, leading to unnecessary declines that disrupt the user experience and erode customer trust. Frequent false positives can frustrate customers, resulting in dissatisfaction, increased complaints, and a diminished sense of security. To address these limitations, we propose a fraud detection framework incorporating Relational Graph Convolutional Networks (RGCN) to enhance the accuracy and efficiency of identifying fraudulent transactions. By leveraging the relational structure of transaction data, our model reduces the need for direct customer confirmation while maintaining high detection performance. Our experiments are conducted using the IBM credit card transaction dataset to evaluate the effectiveness of this approach.
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