Online transaction-oriented fraud detection method based on individual behavior modeling

A technology for online transactions and detection methods, applied in payment systems, semantic tool creation, unstructured text data retrieval, etc., can solve problems such as poor rule adaptability and inability to detect novel fraudulent behaviors

Active Publication Date: 2019-05-14
TONGJI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, there are often intricate potential relationships between transaction data. Only by efficiently capturing the internal potential relationships and maintaining the original structural relationships can more accurate detection of fraudulent transactions occur. This problem has a great impact on the accuracy and robustness of the model. stickiness present

Method used

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  • Online transaction-oriented fraud detection method based on individual behavior modeling
  • Online transaction-oriented fraud detection method based on individual behavior modeling
  • Online transaction-oriented fraud detection method based on individual behavior modeling

Examples

Experimental program
Comparison scheme
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Embodiment

[0050] A system structure diagram of an individual behavior modeling method for real-time detection of online transaction fraud, such as figure 2 shown. The whole program is divided into two parts:

[0051] In the first part, the heterogeneous information network is generated by using the relationship graph and the vector representation that can mine the connection between transaction attributes is obtained by learning the representation of the heterogeneous network;

[0052] The second part is the process of establishing an individual behavior model and predicting the possibility of abnormal transactions in the case of learning the vector representation of the node.

[0053] In the first part, the relationship map generates heterogeneous information network and heterogeneous network representation learning, the process is as follows:

[0054] enter:

[0055] The raw data field of the user network payment transaction,

[0056] Adjust the weight hyperparameters α, β,

[0...

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Abstract

The invention discloses an online transaction-oriented fraud detection method based on individual behavior modeling, and relates to anti-fraud detection of Internet financial network transactions. Themethod is divided into two parts: a first part, generating a heterogeneous information network by using a relational graph and obtaining a vector representation capable of mining a connection betweentransaction attributes by using heterogeneous network representation learning; And in the second part, under the condition that the vector representation of the node is learned, an individual behavior model is established, and the transaction abnormity possibility is predicted. The defects of a traditional fraud detection method are overcome, the mining capacity of the method for potential data connection is improved, and better guarantees are provided for detecting fraud transactions, intercepting the fraud transactions and protecting the fund safety of users and enterprises.

Description

technical field [0001] The invention relates to anti-fraud detection of Internet financial network transactions. Background technique [0002] With the rise of the mobile Internet, various traditional businesses are gradually transferred online. Internet finance and e-commerce are developing rapidly. The generation of online transactions will bring a large amount of electronic transaction data, accompanied by a large number of online payment fraud transactions. Increase. Attackers complete fraud by stealing user accounts, stealing personal privacy information, and even maliciously attacking servers. In order to protect the security of users and the company's business, it is necessary to establish an effective network transaction fraud detection system. [0003] At present, traditional network transaction fraud detection systems usually perform feature transformation for transaction attributes. Fraud detection systems that use these feature transformations often ignore many...

Claims

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Application Information

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IPC IPC(8): G06Q20/40G06F16/36
Inventor 王成朱航宇
Owner TONGJI UNIV
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