Research on the Construction and Application of Enterprise Intelligent Financial Analysis System Driven by Big Data—A Case Study of the E-Commerce “Clothing Industry”

在企业财务管理领域，大数据技术的深度应用为智能财务分析体系的构建带来了全新机遇。本研究聚焦电商企业的“服装行业”，通过为期18个月的实地调研、62次管理层深度访谈及37家同行业企业数据对比分析，揭示企业在业财融合背景下如何通过数据中台搭建、算法模型优化等策略提升财务分析效能。研究发现，当企业将应收账款周转天数异常波动与生产设备利用率数据关联分析后，应收账款回收周期较传统分析方式缩短22%，坏账损失率下降17%。研究进一步构建了“数据采集–智能处理–决策支持”三维应用模型，为大数据驱动下的企业财务分析实践提供可操作的行动框架。In the field of enterprise financial management, the in-depth application of big data technology has brought brand-new opportunities for the construction of an intelligent financial analysis system. This study focuses on the “clothing industry” of e-commerce enterprises. Through an 18-month field investigation, 62 in-depth interviews with management teams, and a comparative analysis of data from 37 enterprises in the same industry, it reveals how enterprises can enhance the efficiency of financial analysis through strategies such as building a data middle platform and optimizing algorithm models under the background of business and finance integration. The research found that when enterprises correlated and analyzed the abnormal fluctuations of accounts receivable turnover days with the data of production equipment utilization rate, the accounts receivable recovery cycle was shortened by 22% compared with the traditional analysis method, and the bad debt loss rate decreased by 17%. The research further constructed a three-dimensional application model of “data collection-intelligent processing-decision support”, providing an operational action framework for the practice of enterprise financial analysis driven by big data.