Study of Generative AI in Automated Financial Report Generation

Abstract

This paper provides a comprehensive review of the rapidly evolving field of Generative AI in automated financial report generation. Traditionally, financial report generation has been a labor-intensive process relying on manual data aggregation, analysis, and narrative composition. However, recent advancements in Generative AI, particularly Large Language Models (LLMs), have demonstrated the capacity to automate and significantly enhance this process. This paper describes the current state-of-the-art, tracing the historical development of AI techniques applied to financial reporting. The paper examines core themes such as the application of LLMs for narrative generation from structured financial data, and the use of Generative Adversarial Networks (GANs) for synthetic data generation and fraud detection. A critical comparison of different Generative AI models is presented, highlighting their strengths and weaknesses in the context of financial reporting, alongside a discussion of the inherent challenges, including data bias, regulatory compliance, and the need for explainable AI. Finally, the paper explores future research directions, such as the integration of multi-modal data, the development of more robust and transparent AI models, and the ethical considerations surrounding the widespread adoption of Generative AI in finance. This review aims to provide researchers, practitioners, and regulators with a thorough understanding of the opportunities and challenges presented by Generative AI in transforming the landscape of financial report generation.