Anti-fraud identification method and device based on big data and related equipment

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an anti-fraud identification method and device based on big data, computer equipment and a storage medium, and the method comprises the steps: obtaining the user information of each user in a user group, the user information comprises a user identifier and an attribute feature set corresponding to the user identifier, and inputting the user identifier, the attribute feature set and a preset abnormal attribute feature set into a trained isolated forest model for abnormal recognition to obtain a high-risk user identifier, importing each piece of user information into a graph database, generating a graph model, and querying the high-risk user identifier on the basis of the graph model and a preset data query request. The method comprises the steps of obtaining a community sub-network with each high-risk user identifier as a center node, calculating the high-risk probability of each node in the community sub-network, carrying out feature extraction on the community sub-network based on a neural network to obtain sample data of each node, inputting the sample data and the high-risk probability into a logistic regression model for training to obtain an anti-fraud identification group case model, and carrying out fraud identification on the group case model. And the identification accuracy of the fraudulent group is improved.

Description

technical field [0001] The present invention relates to the technical field of data processing, and in particular, to an anti-fraud identification method, device, computer equipment and storage medium based on big data. Background technique [0002] With the rapid development of the Internet, new credit transaction methods have also emerged. Online transactions have become a part of people's lives. Anti-fraud is one of the important links in the management and control of corporate credit risks. At present, in the process of building an anti-fraud model, it is necessary to obtain a large amount of historical data. These historical data not only include a large amount of historical default data and fraud data, but also a lot of useless data. Using the above historical data to build an anti-fraud model leads to the construction of The anti-fraud model of the company has low accuracy in identifying fraudulent groups. SUMMARY OF THE INVENTION [0003] Embodiments of the presen...

AI Technical Summary

Problems solved by technology

At present, in the process of building an anti-fraud model, it is necessary to obtain a large amount of historical data, which not only includes a large amount of historical default data and fraud data, but also has a lot of useless data. Using the above historical data to build an anti-fraud model leads to the construction Anti-fraud models have low accuracy in identifying fraudulent groups

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