Transaction data exception detection method, medium, device and computing equipment

Abstract

An embodiment of the invention provides a transaction data exception detection method, a medium, a device and computing equipment. The method comprises the steps of generating a knowledge graph basedon transaction data obtained in advance; wherein nodes of the knowledge graph are used for representing account entities in the transaction data, and edges between the two nodes are used for representing transaction relationships between the account entities corresponding to the two nodes respectively; performing graph deep learning on the knowledge graph by using a graph neural network to obtainfeature representation of each edge in the knowledge graph, and determining the feature representation of the edge as a feature vector of transaction data corresponding to the edge; and inputting a predetermined feature vector of the transaction data to be detected into a neural network model obtained by training the feature vector of the transaction data, and outputting a detection result of thetransaction data to be detected after processing of the neural network model. According to the invention, exception detection of transaction data can be automatically completed, and compared with theprior art, the method is advantageous in that the problem of inaccuracy caused by a manual mode is avoided.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of the Internet, and more specifically, the embodiments of the present invention relate to a transaction data anomaly detection method, medium, device and computing equipment. Background technique [0002] This section is intended to provide a background or context for implementations of the invention that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] In financial systems such as banks, if transaction data is abnormal, it may lead to confusion in transaction rules and damage to the interests of banks. In order to reduce losses, the financial system needs to detect and deal with abnormal transaction data in a timely manner. [0004] At present, there are many anomaly detection methods for transaction data, but there are some problems. For example, in the commonly used detection method based on anomaly mod...

AI Technical Summary

Problems solved by technology

At the same time, because the parameters are dynamically changed according to the characteristics of the transaction, the configuration personnel need to configure the abnormal model frequently according to the dynamically changing parameters. It can be seen that the parameter configuration of the abnormal model is a complicated task, and at the same time, it is done manually Parameter configuration may cause inaccurate parameter configuration, resulting in inaccurate detection of transaction data anomalies by the abnormal model

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