Combining Knowledge Graph and Artificial Intelligence to Conduct Financial Report Quality Detection Research

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 29; no. 4; pp. 787 - 795
• Main Author: Luo, Lan
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Co. Ltd 20.07.2025
• Subjects: Accuracy; Algorithms; Artificial intelligence; Deep learning; Efficiency; Fraud; Graphs; Inspection; Internet stocks; Knowledge representation; Money laundering; Optimization techniques; Stock prices; Structured data; Unstructured data
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2025.p0787

Since financial reports usually contain a large amount of data and complex information, traditional methods for quality inspection are not only slow but also difficult, which greatly affects the efficiency of quality inspection. This paper adopts knowledge graph and artificial intelligence methods to convert unstructured data in financial reports into structured data that can be quickly processed, thereby improving the efficiency and performance of financial report quality inspection. Therefore, this paper proposes an ALBERT-BiGRU-CRF model algorithm to perform named entity recognition on financial reports, which can effectively identify complex entities in financial reports; in addition, a RoBERTa-BiGRU model algorithm is proposed to extract the relationship between entities and finally construct the relevant knowledge graph. By analyzing the knowledge graph, relevant data inside the financial report can be obtained. The F1 score of the ALBERT-BiGRU-CRF model proposed in this paper is 6.1% higher than that of the BERT-BiGRU-CRF model, and the F1 score of the RoBERTa-BiGRU model proposed in this paper is 4.1% higher than that of BiGRU. The model proposed in this paper is of great significance for the knowledge graph modeling and quality inspection of financial reports.