Credit card fraud prediction method based on signal transmission and link mode

A technology of link mode and signal transmission, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as classifier performance degradation

Inactive Publication Date: 2016-08-24
TIANYUN RONGCHUANG DATA TECH BEIJING CO LTD
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Problems solved by technology

For this case, traditional classifiers suffer from poor

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  • Credit card fraud prediction method based on signal transmission and link mode
  • Credit card fraud prediction method based on signal transmission and link mode
  • Credit card fraud prediction method based on signal transmission and link mode

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Embodiment Construction

[0016] The credit card fraud prediction method based on signal transmission and link mode of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments:

[0017] Collaborative classification applies a cooperative reasoning mechanism. When classifying an unlabeled node, it not only utilizes the label dependency between it and the linked labeled nodes, but also utilizes the label dependency between it and the linked unlabeled nodes. By considering both labeled and unlabeled nodes, the algorithm's dependence on label information can be reduced, which helps to deal with sparsely labeled networks.

[0018] The idea of ​​collaborative reasoning: Assuming that there is a relational network of credit card user information, they are connected through node attribute matching information, the similarity of node attributes is used as the edge value, {legal, fraud} is the network node label, and the purpose of collaborative c...

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Abstract

The invention discloses a credit card fraud prediction method based on a signal transmission and link mode. The credit card fraud prediction method includes the steps of firstly, constructing a structure attribute classifier based on a signal transmission idea, and initializing a label for each unlabelled node; then, conducting iterative calculation for the following process until the labels of the unlabelled nodes are stabilized or the maximum number of iterations is reached; extracting a sub-graph of each label from a graph, and constructing intra-class and inter-class link mode matrixes; next, combining a mean aggregate function, and calculating a feature vector of each label and a feature vector of each unlabelled node; and for each unlabelled node, calculating the cosine similarity between the feature vector thereof and the feature vector of each label, wherein the label of each node is assigned as a label corresponding to the feature vector with the maximum similarity. By applying a collaborative inference mechanism and meanwhile considering the label and unlabelled node information, the invention reduces the dependence on the label information, and has a very important practical significance in the study of credit card fraud prediction.

Description

technical field [0001] The invention relates to the field of complex network classification prediction, in particular to a credit card fraud prediction method based on signal transmission and link mode. Background technique [0002] In recent years, despite the sluggish development of the global economy, my country's credit card industry has developed rapidly driven by the sustained and rapid development of my country's economy. The number of credit cards issued continued to grow. Due to the substantial increase in the number of credit card transactions and the transaction amount, credit card risk management is still a matter of great concern to the industry. As an important part of risk management, credit card fraud risk is of great practical significance to study credit card fraud identification. Credit card fraud has the characteristics of low frequency of occurrence and great harm, and it is difficult to effectively identify and prevent it by general means. Therefore, it...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24147
Inventor 雷涛吕慧高红霄
Owner TIANYUN RONGCHUANG DATA TECH BEIJING CO LTD
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