{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:36:12Z","timestamp":1753882572617,"version":"3.41.2"},"reference-count":29,"publisher":"World Scientific Pub Co Pte Ltd","issue":"09","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:p> This paper focuses on building an innovative industrial financial risk rapid discovery model based on large language model and knowledge graph inference. Firstly, this work deeply analyzes the diversity, concealment and rapid propagation of industrial financial risks. Secondly, it points out the limitations of traditional risk assessment methods when dealing with massive and heterogeneous financial data. Then, it designs an integrated strategy to integrate the powerful natural language processing and understanding ability of large language models. Combined with the advantages of knowledge graph in structured information representation, this model analyzes financial text data through a large language model. Hence, key information can be automatically extracted and a preliminary risk knowledge base can be built. Finally, an industrial financial network can be established to realize correlation mining and dynamic evolution analysis of risk factors. The experimental results show that the model can effectively capture risk signals in the industrial chain, and detect potential financial risks in advance. This study not only enriches the theoretical framework of industrial financial risk management, but also provides strong technical support for risk prevention and control in practice. <\/jats:p>","DOI":"10.1142\/s0218126625502068","type":"journal-article","created":{"date-parts":[[2025,1,18]],"date-time":"2025-01-18T05:16:29Z","timestamp":1737177389000},"source":"Crossref","is-referenced-by-count":0,"title":["A Fast Discovery Model for Production Financial Risk Using Large Language Model and Knowledge Graph"],"prefix":"10.1142","volume":"34","author":[{"given":"Pangli","family":"He","sequence":"first","affiliation":[{"name":"School of Economics and Management, Shangqiu Institute of Technology, Shangqiu 476000, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huani","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Shangqiu Institute of Technology, Shangqiu 476000, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"key":"S0218126625502068BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2021.3130311"},{"key":"S0218126625502068BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3077916"},{"key":"S0218126625502068BIB003","first-page":"262","volume":"11884","author":"Gao R.","year":"2021","journal-title":"Proc. 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