Financial business data risk early warning management method and system based on artificial intelligence

By constructing directed graphs and path scoring, and combining them with time series analysis of financial and business data, the problems of false alarms, missed alarms, and feedback loops in existing financial risk early warning systems have been solved. This has enabled intelligent identification and causal analysis, thereby improving the accuracy and management value of risk management.

CN120852077AActive Publication Date: 2025-10-28CHINA SHENHUA ENERGY CO LTD GUANGDONG BRANCH
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Patent Information

Application Number
CN202511009361.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Priority Date
2025-06-18
Filing Date
2025-07-22
Publication Date
2025-10-28
Estimated Expiration
2045-07-22

AI Technical Summary

Technical Problem

Existing financial risk early warning systems in large enterprises suffer from false alarms or omissions, lack causal analysis and feedback loops, and are unable to provide accurate risk assessment and management in complex business environments.

Method used

An AI-based financial business data risk early warning management method is adopted. By acquiring and decomposing the time series of actual execution amounts, a directed graph is constructed, path scores are calculated, and response suggestions are generated to achieve intelligent identification of anomalies, causal reasoning, and closed-loop feedback.

Benefits of technology

It enhances the interpretability and governance capabilities of the financial risk early warning system in complex business environments, enabling it to identify potential risks, explain the causes of anomalies, and achieve responsibility identification and closed-loop processing.

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Abstract

The invention belongs to the field of data security, and discloses a financial business data risk early warning management method and system based on artificial intelligence, and the method comprises the steps: 1, obtaining a time sequence of an actual execution amount, and decomposing the time sequence of the actual execution amount to obtain an abnormal score of a budget item; step 2, acquiring an abnormal confidence score of the budget item based on the abnormal score, and judging whether the budget item is an abnormal budget item based on the abnormal confidence score; step 3, constructing a directed graph based on the business event data and the abnormal budget item; step 4, obtaining all paths which take the nodes with the abnormal budget as the end points and meet the path constraint in the directed graph, and obtaining a path set; respectively calculating a path score of each path; and 5, calculating a priority score of each path based on the path scores, obtaining a main path based on the priority scores, and generating a response suggestion based on the main path. According to the invention, the interpretation capability and the treatment capability of the financial risk early warning system in a complex business environment are improved.
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