中小企业财务分析中AIGC的应用前景与边界研究

Research on the Application Prospects and Boundaries of AIGC in Financial Analysis of Small and Medium-Sized Enterprises

摘要: 在新质生产力推动产业数字化转型的背景下，中小企业财务分析面临数据整合低效、预测能力不足、专业人才短缺等突出困境。本文以AIGC技术与中小企业财务分析为核心研究对象，综合运用文献研究法与对比分析法，系统探究AIGC在中小企业财务分析中的应用前景与边界。研究发现：AIGC在多源数据自动化处理、智能预测分析、财务风险实时预警等方面具备技术优势，能够匹配中小企业降本增效的现实诉求；但算法可解释性(XAI)不足、基础数据治理水平薄弱、企业资源禀赋约束、AI应用责任归属制度尚不健全等因素，制约AIGC价值释放。据此，本文依托TOE框架的三维逻辑，从技术轻量化迭代、企业业财融合内部治理、外部制度保障体系三个维度构建突破路径。本文将XAI、数据治理纳入中小企业智能财务分析研究，拓展TOE框架在技术采纳后场景的应用，丰富中小企业财会数字化相关研究，可为中小企业运用AIGC优化财务分析质量、推进数字化转型提供理论参考与实践指引。

Abstract: Against the backdrop of industrial digital transformation driven by new-quality productive forces, small and medium‑sized enterprises (SMEs) are plagued by multiple prominent dilemmas in their daily financial analysis work, among which inefficient cross-source data integration, inadequate forecasting capacity for business and financial trends, and the severe shortage of compound financial professionals stand out as major bottlenecks restricting the improvement of financial management effectiveness. Taking AIGC technology and the financial analysis practice of small and medium-sized enterprises as its core research object, this paper adopts both the literature research method and comparative analysis method. On the basis of sorting out domestic and overseas relevant literature, it systematically explores the practical application prospects as well as inherent application boundaries of AIGC technology when deployed for SME‑oriented financial analysis scenarios. The research findings demonstrate that AIGC possesses remarkable technical strengths in automatic multi‑source data processing, intelligent predictive analysis and real‑time financial risk early‑warning. These technical capabilities can effectively respond to the realistic demands of SMEs for cost reduction and efficiency improvement in financial work. However, the expected value of AIGC cannot be fully realized without corresponding supporting conditions. Its practical performance is constrained by a set of realistic factors, including insufficient interpretability of Explainable Artificial Intelligence (XAI) algorithms, weak basic data governance within enterprises, inherent resource endowment constraints faced by most SMEs, and the imperfect institutional systems concerning liability attribution for losses caused by AI application. Accordingly, this paper draws on the three‑dimensional analytical logic of the Technology‑Organization‑Environment (TOE) framework and constructs corresponding breakthrough paths from three dimensions: lightweight technological iteration for AIGC tools, optimized internal governance focusing on business‑finance integration within enterprises, and the establishment of sound external institutional guarantee systems. Different from most existing studies that merely employ the TOE framework to analyze pre‑adoption influencing factors of digital technologies, this paper brings XAI theory and data governance thinking into the analytical framework of intelligent financial analysis for SMEs. It further expands the application scope of the TOE framework to post‑technology‑adoption scenarios, and enriches theoretical accumulations for research on digitalized finance and accounting of small and medium‑sized enterprises. The research conclusions of this paper can deliver solid theoretical references and operable practical guidelines for SMEs to improve the overall quality of financial analysis and steadily push forward digital transformation by making reasonable use of AIGC‑enabled analytical tools.