Research progress of digital financial fraud detection oriented to social relations network

doi:10.3785/j.issn.1008-9497.2024.01.006

Journal of Zhejiang University (Science Edition)

2024, Vol. 51

Issue (1): 41-54 DOI: 10.3785/j.issn.1008-9497.2024.01.006

Mathematics and Computer Science

Research progress of digital financial fraud detection oriented to social relations network

Hualing LIU(

),Junyi XU,Shijie CAO,Yaxin LIU,Liang QIAO

School of Statistics and Information，Shanghai University of International Business and Economics，Shanghai 201620，China

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Abstract

In the new era of fintech, digital technology is the core driving force of the future development of financial industry. With the new technology and the risk of financial fraud escalating, fraud detection based on digital technology has become a new research hot spot. Meantime the research direction of financial fraud detection technology has shifted from traditional methods of improving expert experience and optimizing machine learning models to exploring graph machine learning methods for social network. This article focuses on social network, based on the development process of network analysis, from different perspectives of detecting abnormal individuals, suspicious groups and unhealthy intermediaries, with different technical methods of digital financial fraud detection as the main line, the existing social-oriented relational network fraud identification methods are investigated, and the future research trends and directions of digital financial fraud detection technologies are highlighted.

Key words： big data artificial intelligence social network fraud detection anti-fraud

Received: 20 December 2021 Published: 10 January 2024

CLC:

TP 391

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Cite this article:

Hualing LIU,Junyi XU,Shijie CAO,Yaxin LIU,Liang QIAO. Research progress of digital financial fraud detection oriented to social relations network. Journal of Zhejiang University (Science Edition), 2024, 51(1): 41-54.

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https://www.zjujournals.com/sci/EN/Y2024/V51/I1/41

面向社会关系网络的数字金融欺诈检测研究进展

在金融科技兴起的新时代，数字技术是金融业未来发展的核心驱动力，基于数字技术的欺诈检测成为新的研究热点。金融欺诈检测技术研究由传统的提升专家经验、优化机器学习模型转向探索面向社会关系网络的图机器学习方法。聚焦社会关系网络，基于网络分析的发展历程，从检测异常个人、可疑团伙和不良中介3类主体的视角，对金融欺诈检测的核心工作、典型应用进行了综述；归纳分析了面向社会关系网络不同类别的数字金融欺诈检测技术；并给出了面向社会关系网络的数字金融欺诈检测研究的发展趋势和方向。

关键词： 大数据, 人工智能, 社会关系网络, 欺诈检测, 反欺诈

Fig.1 Flow chart of digital financial fraud detection

算法	优势	劣势	时间复杂度	适用范围
标签传播算法（LPA）	（1）时间复杂度低，接近线性复杂度，适用于大规模网络；（2）无须定义优化函数，无须指定社区个数	（1）雪崩效应：社区结果不稳定，随机性强；（2）振荡效应：社区结果振荡，不收敛	$O (m)$	非重叠社区
GN算法	（1）在明确划分社团数时，可较精确地给出社团划分结果；（2）给出不同层次的社团划分结果，揭示关于网络层次的信息	（1）时间复杂度高，在计算边介数时需多次计算最短路径；（2）若无法知道划分的社团数，算法结果相对较差	$O (m 2 n)$	非重叠社区
Louvain 算法	（1）第一次迭代以单个节点作为社区粒度，不受模块化分辨率的限制；（2）能够发现层次性社区结构	（1）挖掘社区的大小，随着图的增大而增大；（2）社区过大，不能及时收敛	$O (n ? l o g ? n)$	非重叠社区
SLPA	（1）能高效实现重叠社区检测；（2）算法过程易修改，可适应不同规则、不同类型的网络	（1）对于节点众多的大规模网络，分配标签会消耗大量计算资源；（2） SLPA的随机选择策略导致算法具有随机性和不稳定性	$O (T m)$	重叠社区

Table 1 Comparative analysis of typical community detection algorithms

Fig.2 Hierarchical tree generated by GN algorithm output

Fig.3 Visualization of Louvain algorithm steps

Fig.4 Network model diagram of electronic game payment transaction data

Table 2 Commonly used local structure characteristics


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