Optimization of Automated Financial Statement Information Disclosure System Based on AI Models

In the context of the digital transformation of the global economy and the rapid advancement of enterprise informatization, ensuring accurate and timely financial statement disclosure has become a critical priority for businesses and regulatory bodies. This study aims to address the inefficiencies, high error rates, and slow response times inherent in traditional financial information disclosure processes, which fail to meet the real-time data accuracy demands of modern enterprises. The study introduces an AI-driven optimization scheme for an automated processing network system for financial statement information disclosure. By leveraging advanced machine learning techniques and large language models, the proposed system enhances the accuracy, speed, and cost-effectiveness of disclosure processes. The system was tested and compared against traditional manual methods, focusing on processing time, accuracy rates, and operational cost savings. The optimized system significantly reduces the average processing time from three hours to 20 minutes, achieving a 90% efficiency improvement. Accuracy is enhanced from 92% to over 97%, while the response speed increases by 40%. Additionally, the system reduces operational costs by 15%, resulting in annual labor cost savings of approximately 12 million yuan. These findings demonstrate the transformative potential of AI technologies in addressing the limitations of traditional financial disclosure processes. This study highlights an innovative application of AI in the realm of intelligent finance, offering a scalable solution that aligns with the evolving demands for real-time, accurate financial information. The research contributes to the growing field of AI-driven automation by showcasing its practical implications and substantial benefits in financial statement disclosure.