Online transaction fraud detection method based on entity relationship

A technology of entity relationship and transaction, applied in the information field, can solve the problems of unable to classify tasks to extract feature information, not using node label information, etc., to achieve the effect of improving effect and good performance effect

Pending Publication Date: 2019-12-10
DONGHUA UNIV
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  • Application Information

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Problems solved by technology

However, node2vec is an unsupervised algorithm that does not use the label information of the no

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  • Online transaction fraud detection method based on entity relationship
  • Online transaction fraud detection method based on entity relationship
  • Online transaction fraud detection method based on entity relationship

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Embodiment Construction

[0046] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0047] The network transaction fraud detection method involved in the present invention mainly has the following three parts:

[0048] (1) Based on the consideration of the relationship network, the entity relationship is extracted from the original data set, and a heterogeneous disconnected sparse original network bipartite graph is constructed.

[0049] (2) Referring to the universality of homogeneous network research, a met...

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Abstract

The invention relates to an online transaction fraud detection method based on an entity relationship, which is characterized in that the entity relationship is extracted according to transaction data, a relational network bipartite graph is constructed, and a heterogeneous network homogenization method based on node contraction and a neighborhood information aggregation promotion tree classification model mechanism based on ensemble learning and graph representation learning are provided. According to the method provided by the invention, from the perspective of practicability, the attentionis converted from the transaction node to the multi-order neighborhood information of the transaction in the relational network, and the potential association relationship between the transactions isfully considered, so that the possibility is provided for mining gang fraud. The gradient promotion model improves the fraud identification effect through continuously fitting the residual error of the model, and has a very good performance effect. Meanwhile, the method expands the ensemble learning from the application of the grid-type data to the application field of the graph data. Based on theabove aspects, a framework of the loan transaction fraud detection method is established, and technical support is provided for solving fraud transaction detection.

Description

technical field [0001] The invention relates to a network transaction detection method, which belongs to the field of information technology. Background technique [0002] In recent years, with the development of Internet technology, a large number of new businesses have been spawned in the financial field. While online transactions bring convenience to users, they also provide an opportunity for online black production. The current Internet black industry presents the characteristics of industrialization, precision, mobility, and technology, especially in the field of online credit and financial business, which not only requires the evaluation of the credit qualification of the borrower, but also requires special attention to potential fraudulent behavior. Fighting against online black production has become a core research topic of Internet financial companies. [0003] Analogous to the traditional offline loan process, it is necessary to collect the relationship informat...

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Application Information

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IPC IPC(8): G06K9/62G06Q20/38G06Q20/40G06F16/28
CPCG06Q20/401G06Q20/4016G06Q20/382G06F16/288G06F18/24
Inventor 章昭辉蒋昌俊王鹏伟孟盈
Owner DONGHUA UNIV
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