Research on the Knowledge Association Reasoning of Financial Reports Based on a Graph Network

Abstract

With increasingly strict supervision, the complexity of enterprises’ annual reports has increased significantly, and the size of the text corpus has grown at an enormous rate. Information fusion for financial reporting has become a research hotspot. The difficulty of this problem is in filtering the massive amount of heterogeneous data and integrating related information distributed in different locations according to decision topics. This paper proposes a Graph NetWork (GNW) model that establishes the overall connection between decentralized information, as well as a graph network generation algorithm to filter large and complex data sets in financial reports and to mine key information to make it suitable for different decision situations. Finally, this paper uses the Planar Maximally Filtered Graph (PMFG) as a benchmark to show the effect of the generation algorithm.

From data to insights: the application and challenges of knowledge graphs in intelligent audit

“…In financial reporting, knowledge graphs integrate numerous internal and external data resources, such as financial information, market trends, regulatory standards, and historical records, enabling financial analysts to gain deeper insights into financial health and business performance [184]. Liang et al [185] developed a graph network model to establish holistic connections between decentralized information, filtering through large, complex datasets in financial reports to unearth key information applicable to diverse decision-making scenarios.…”