Invoice risk control management method and system based on multi-source data linkage

By integrating multi-source data and graph neural network analysis, combined with a federal collaborative processing mechanism, the problems of data isolation and low risk identification efficiency in traditional invoice management have been solved. This has enabled real-time identification and coordinated processing of false invoices and duplicate financing, ensuring information security and data privacy.

CN121120289AActive Publication Date: 2025-12-12GUANGZHOU LESHUI INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202511649154.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-12
Publication Date
2025-12-12
Estimated Expiration
2045-11-12

AI Technical Summary

Technical Problem

Traditional invoice management relies on a single data source, making it difficult to identify risks such as fraudulent invoices and duplicate financing. Data processing efficiency is low, and information is isolated between departments, making it impossible to share information in a timely manner and coordinate risk management.

Method used

The system collects real-time data from tax bureau ledgers, bank statements, logistics GPS signals, contract blockchain records, and ERP image data through a multi-source data fusion gateway. It generates an encrypted unified data view, performs minute-level analysis and multi-scale anomaly analysis using graph neural networks and heterogeneous time-series Transformer models, and securely shares risk information and triggers coordinated response actions using a federated collaborative handling mechanism.

Benefits of technology

It enables real-time monitoring of the entire invoice business process, accurately identifies fraudulent invoice issuance groups and risks of repeated financing, and takes timely measures to reduce tax losses and financial risks, while ensuring information security and data privacy.

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Abstract

The invention relates to the technical field of invoice risk control management, and discloses an invoice risk control management method and system based on multi-source data linkage. According to the method, multi-source data of the whole invoice business process is collected in real time through a multi-source data fusion gateway. And performing streaming ETL processing and encryption integration on the multi-source data to generate an encrypted unified data view. And based on the view, constructing an association hypergraph by utilizing a dynamic graph association technology, analyzing an entity relationship by virtue of a graph neural network, and identifying false invoice making gang and repeated financing risks. And calculating a dynamic risk score by adopting a risk quantification and traceability technology, and outputting a risk level, a responsibility node identifier and a capital flow thermodynamic diagram. When the risk score exceeds a threshold value, risk gradient information is safely shared among the group headquarters, the molecular companies and the tax-bank institutions, and linkage processing actions such as invoice freezing, red character notification or credit adjustment are triggered. According to a manual reexamination result, the risk model is finely adjusted through a knowledge self-updating technology, and the rule base is hot-updated.
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