{"id":8673,"date":"2026-06-27T14:34:57","date_gmt":"2026-06-27T06:34:57","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=8673"},"modified":"2026-07-10T13:56:18","modified_gmt":"2026-07-10T05:56:18","slug":"jase-202610-33-021","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-021","title":{"rendered":"The Quality of Financial Reports and Decision Support Driven by Big Data Analysis"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Binbin Hu<\/i><\/a><\/p>\n\n\n\n

Big Data Accounting Institute, Henan Industry and Trade Vocational College, Zhengzhou 450000, China<\/p>\n<\/div>\n\n\n\n

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Received: March 22, 2026
Accepted:\u00a0June 23, 2026
Publication Date:\u00a0June 27, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Performance Comparison of Model Ablation Experiment. <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.021<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Against the backdrop of global digital transformation of corporate finance, the mismatch between traditional financial reporting systems and complex dynamic business environments has become increasingly prominent, with problems such as financial information distortion, weak risk early warning capabilities, and the disconnection between financial information quality and decision value creation becoming key bottlenecks restricting high-quality development of enterprises. With the deep development of the digital economy, the digital transformation of enterprise financial activities is accelerating. How to rely on big data analysis technology to solve the industry pain points of distorted financial report information and insufficient decision support capabilities has become a core issue in the field of enterprise financial management and corporate governance. This study is based on the theory of information asymmetry and the financial quality evaluation system, and constructs a four-dimensional quantitative model for financial report quality, depicting the evolutionary relationship between the authenticity, compliance, and value relevance of financial information; Subsequently, an improved adaptive gradient boosting decision tree (AGBDT) algorithm was introduced to construct a big data analysis driven financial report quality optimization and decision support framework. The model integrates historical financial information weighting mechanism, multi-objective decision return function, and adaptive learning rate optimization strategy, achieving joint optimization of financial report quality improvement, decision risk control, and enterprise value growth. The empirical results show that the proposed model achieved the lowest average absolute error of 0.21 in financial quality identification and the highest accuracy of 96.3% in financial fraud identification on the Shanghai and Shenzhen A-share listed company datasets and the New Third Board enterprise datasets. The effectiveness of decision support was improved by 32.7% compared to traditional models, and the overall performance was significantly better than the four comparative models. In extreme market volatility and industry heterogeneity scenarios, this can still control the deviation of financial report distortion warning within 8.5%, while achieving optimization of business decision-making under low risk. This study provides an efficient and scalable technological path for the construction of digital financial management systems in enterprises, which is applicable to various scenarios such as information disclosure management of listed companies, financial control of group enterprises, and value analysis of investment institutions.<\/p>\n\n\n\n

Keywords: Big data analysis; Financial report quality; Financial information quality; Decision support; Adaptive gradient boosting decision tree; Financial risk early warning<\/em><\/p>\n\n\n\n

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