Enterprise audit information extraction method based on artificial intelligence

By constructing a multimodal conversion processing and dynamic knowledge graph of enterprise audit information, the problem of poor correlation of multimodal data in enterprise audits is solved, high-precision audit clue discovery and legal terms adaptation are achieved, and audit efficiency and accuracy are improved.

CN120340042AActive Publication Date: 2025-07-18BEIJING ZHONGYOU JINSHEN TECH CO LTD
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Patent Information

Application Number
CN202510764404.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-10
Publication Date
2025-07-18
Estimated Expiration
2045-06-10

AI Technical Summary

Technical Problem

The existing technology has problems in enterprise audits with poor cross-modal correlation of multimodal data and misjudgment of audit clues caused by static legal clause mapping, especially in cross-modal correlation and semantic understanding of unstructured data.

Method used

Using an artificial intelligence-based method, a standard data set is generated through multi-modal transformation processing, an audit knowledge graph is constructed, cross-modal alignment is performed, and feature fusion is combined with graph neural network and attention mechanism, dynamically verify and update the knowledge base, and the final audit document is generated.

Benefits of technology

It significantly improves the accuracy of audit clue discovery and the adaptability of legal clauses, improves the efficiency of cross-modal clue discovery, reduces character breakage and speech transcription error rates, and realizes dynamic correlation analysis of financial abnormalities and business flow fluctuations.

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Abstract

The invention discloses an enterprise audit information extraction method based on artificial intelligence, and relates to the technical field of enterprise audit information processing, and the method comprises the steps: obtaining an audit original document from an enterprise, carrying out the multi-mode conversion processing, and generating a standard data set; performing deep semantic analysis on the standard data set, and constructing an audit knowledge graph; performing cross-modal alignment on the auditing knowledge graph, the image features, the table features and the time sequence features, and constructing a joint auditing analysis model; performing dynamic verification on an audit analysis result and a historical case library to form a dynamic evolution knowledge base; and generating a final audit document based on the updated knowledge base. Through cross-modal feature vector alignment and attention-driven clause applicability scoring, dynamic association analysis of financial anomaly and business pipeline fluctuation is realized, and the cross-modal clue discovery efficiency is improved.
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