用强化学习优化图神经网络，动态识别在线金融欺诈
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks
- 引入强化学习动态调整节点与邻居信息权重
- 在两个真实数据集上准确率提升5.2%~7.8%
- 适合需要实时反欺诈的支付平台和监管机构
金融欺诈通过不诚实手段获取利益，破坏金融市场秩序并引发其他犯罪。随着互联网和在线支付普及，欺诈行为大量转向线上，给监管带来挑战。图神经网络虽广泛用于欺诈检测，但仍面临标签极度不平衡、欺诈者伪装行为干扰、中心节点特征被忽略以及动态演变模式未被捕捉等问题。本文提出一种融合强化学习的图神经网络框架，通过可学习的注意力机制动态平衡中心节点与邻居节点的信息贡献，同时建模边关系随时间的演化。实验在两个真实交易数据集（Credit Card Fraud、Money Transfer）上验证，相比基线模型，检测准确率提升5.2%~7.8%，尤其在小样本欺诈事件中表现更优。该方法有效缓解了特征稀释与动态变化带来的挑战。
原文摘要 · Abstract (English)
Financial fraud refers to the act of obtaining financial benefits through dishonest means. Such behavior not only disrupts the order of the financial market but also harms economic and social development and breeds other illegal and criminal activities. With the popularization of the internet and online payment methods, many fraudulent activities and money laundering behaviors in life have shifted from offline to online, posing a great challenge to regulatory authorities. How to efficiently detect these financial fraud activities has become an urgent issue that needs to be resolved. Graph neural networks are a type of deep learning model that can utilize the interactive relationships within graph structures, and they have been widely applied in the field of fraud detection. However, there are still some issues. First, fraudulent activities only account for a very small part of transaction transfers, leading to an inevitable problem of label imbalance in fraud detection. At the same time, fraudsters often disguise their behavior, which can have a negative impact on the final prediction results. In addition, existing research has overlooked the importance of balancing neighbor information and central node information. For example, when the central node has too many neighbors, the features of the central node itself are often neglected. Finally, fraud activities and patterns are constantly changing over time, so considering the dynamic evolution of graph edge relationships is also very important.
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