| Autor: |
刘华玲(LIU Hualing), 许珺怡(XU Junyi), 曹世杰(CAO Shijie), 刘雅欣(LIU Yaxin), 乔梁(QIAO Liang) |
| Jazyk: |
čínština |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Zhejiang Daxue xuebao. Lixue ban, Vol 51, Iss 1, Pp 41-54 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1008-9497 |
| DOI: |
10.3785/j.issn.1008-9497.2024.01.006 |
| Popis: |
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.(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。) |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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