A Study on the Dual Impact of Generative Prediction (GPT)-Based AI on the Quality of Corporate Financial Disclosure

Abstract

Against the backdrop of accelerating digital transformation, GPT-based generative AI technologies are gradually penetrating the entire corporate financial disclosure process, exerting a significant dual impact on disclosure quality. Drawing on information asymmetry theory and principal-agent theory, combined with KPMG's global research data and case studies such as Amazon and AllHere, this paper systematically analyzes the positive impact and potential risks of generative AI on the quality of financial disclosure. The study finds that generative AI can reduce disclosure redundancy through automated processing, compressing MD&A report summaries to 25% of the original while retaining core information, while also improving forecast accuracy and compliance efficiency. However, this also presents risks such as "AI whitewashing," data fabrication, and algorithmic black box manipulation. For example, the US AI startup AllHere overstated its revenue by nearly 700 times by fabricating AI-related financial data. The study further suggests the need to establish a coordinated mechanism across three dimensions: optimizing corporate governance, upgrading regulatory technology, and managing model security. The conclusions indicate that the impact of generative AI on disclosure quality is not one-way; its ultimate effect depends on the alignment between technical application specifications and risk prevention and control systems. This finding provides empirical evidence for companies to rationally utilize AI technology and for regulators to improve governance rules.