Research Progress in Application of Graph Anomaly Detection in Financial Anti-Fraud

doi:10.3778/j.issn.1002-8331.2203-0233

Abstract

Abstract: With the rapid development of digital finance, fraud presents new characteristics such as intellectualization, industrialization and strong concealment. And the limitations of traditional expert rules and machine learning methods are increa-
singly apparent. Graph anomaly detection technology has a strong ability to deal with associated information, which provides new idea for financial anti-fraud. Firstly, the development and advantages of graph anomaly detection are briefly introduced. Secondly, from the perspectives of individual anti-fraud and group anti-fraud, graph anomaly detection technology is divided into individual fraud detections based on feature, proximity, graph representation learning or community division, and gang fraud detections based on dense subgraph, dense subtensor or deep network structure. The basic idea, advantages, disadvantages, research progress and typical applications of each anomaly detection technology are analyzed and compared. Finally, the common test data sets and evaluation criteria are summarized, and the development prospect and research direction of graph anomaly detection technology in financial anti-fraud are given.

Key words: financial anti-fraud, graph anomaly detections, digital financial service

摘要： 随着数字金融的快速发展，欺诈呈现出智能化、产业化以及强隐蔽性等新特点，传统的专家规则和机器学习方法局限性日益显现。图异常检测技术对关联信息具有强大的处理能力，为金融反欺诈提供了新的思路。简要介绍了图异常检测的发展历程和优势；着重从个体反欺诈和群体反欺诈两个视角，将图异常检测划分为基于特征、基于邻近性、基于图表示学习和基于社区划分的个体欺诈检测，以及基于稠密子图、基于稠密子张量和基于深层网络结构的团伙欺诈检测，并对每类技术的基本思想、优缺点、研究进展和典型应用进行对比分析；同时归纳总结了常用的数据集和评价指标，并给出图异常检测在金融反欺诈中的发展前景和研究方向。

关键词: 金融反欺诈, 图异常检测, 数字化金融服务

LIU Hualing, LIU Yaxin, XU Junyi, CHEN Shanghui, QIAO Liang. Research Progress in Application of Graph Anomaly Detection in Financial Anti-Fraud[J]. Computer Engineering and Applications, 2022, 58(22): 41-53.

刘华玲, 刘雅欣, 许珺怡, 陈尚辉, 乔梁. 图异常检测在金融反欺诈中的应用研究进展[J]. 计算机工程与应用, 2022, 58(22): 41-53.

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