A financial risk prediction method and device, a storage medium and an electronic device

By deeply integrating the rule engine with heterogeneous graph neural networks, the problem of balancing structured and heterogeneous data in financial risk control is solved, enabling accurate prediction and efficient review of financial risks, and improving the system's adaptability and identification accuracy.

CN122415245APending Publication Date: 2026-07-17CHINA TOWER CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA TOWER CO LTD
Filing Date
2026-04-20
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing financial risk control technologies struggle to simultaneously achieve efficient review of structured data and in-depth risk mining of heterogeneous data, making it impossible to accurately predict potential financial risks.

Method used

By deeply integrating a rule engine with a heterogeneous graph neural network, it acquires various corporate financial data and preset audit rules to perform automated verification and risk analysis. It also combines the heterogeneous graph neural network to conduct risk assessment, generate risk levels, and process them accordingly.

Benefits of technology

It enables accurate prediction of potential financial risks, improves the accuracy of risk identification and audit efficiency, and enhances the system's adaptability and practicality.

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Abstract

The application discloses a financial risk prediction method and device, a storage medium and an electronic equipment, and belongs to the technical field of financial risk control. The method comprises the following steps: acquiring various enterprise financial data, and acquiring preset financial audit rules; based on the preset financial audit rules, automatically checking the various enterprise financial data to identify various irregular items, and obtaining suspected risk data, heterogeneous graph data and preliminary risk labels; based on a heterogeneous graph neural network, performing risk analysis and evaluation on the suspected risk data and the heterogeneous graph data to generate a risk evaluation result; based on the preliminary risk labels and the risk evaluation result, performing risk prediction on the risk levels of the various enterprise financial data to obtain a risk prediction result. Through the prediction method, the potential financial risk can be accurately predicted.
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