动态欺诈检测:将强化学习融入图神经网络

Yuxin Dong, Jianhua Yao, Jiajing Wang, Yingbin Liang, Shuhan Liao, Minheng Xiao
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引用次数: 0

摘要

金融欺诈是指通过不正当手段获取金融利益的行为。这种行为不仅扰乱了金融市场秩序,而且危害了经济和社会发展,并滋生了其他违法犯罪活动。随着互联网和在线支付方式的普及,生活中的许多欺诈活动和洗钱行为已经从线下转移到了线上,这给监管部门带来了巨大的挑战。如何有效地检测这些金融欺诈活动已成为亟待解决的问题。图神经网络是一种深度学习模型,可以利用图结构中的交互关系,在欺诈检测领域得到了广泛应用。然而,目前仍存在一些问题。首先,欺诈活动只占交易转账的很小一部分,导致欺诈检测中不可避免地存在标签不平衡的问题。同时,欺诈者往往会伪装自己的行为,这会对最终预测结果产生负面影响。此外,现有研究还忽略了平衡邻居信息和中心节点信息的重要性。例如,当中心节点有太多邻居时,中心节点本身的特征往往会被忽略。最后,欺诈活动和模式会随着时间的推移而不断变化,因此考虑图边关系的动态演化也非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks
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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