The impact of large language models on accounting and future application scenarios
本文通过系统文献综述，分析了大型语言模型如何提升会计效率、透明度和创新，涵盖财务报告、ESG披露、风险管理和智能知识库等应用，并讨论了数据质量、隐私等挑战及应对策略。
Purpose This paper examines the transformative impact of large language models (LLMs) on accounting practices and explores future application scenarios. Through a systematic literature review, it highlights the potential of LLMs to enhance efficiency, transparency and innovation across areas such as financial reporting, ESG disclosure, financial analysis and risk management. Additionally, it identifies key challenges, including data quality, privacy and the need for domain-specific adaptations, while proposing actionable strategies to address them. By forecasting advanced applications like intelligent knowledge bases and automated operations, this study provides a roadmap for integrating LLMs into accounting, driving progress and sustainability in the industry. Design/methodology/approach This study adopts a systematic literature review methodology to explore the impact and future applications of LLMs in accounting. It identifies key research areas by analyzing over 50 high-quality studies selected through extensive keyword searches, Boolean queries and backward and forward citation analyses of seminal works. The review is structured around eight thematic areas, including financial reporting, ESG disclosure and risk management. By synthesizing findings, the study develops a comprehensive framework for understanding the transformative potential of LLMs while addressing associated challenges, such as data security and specialization, to guide future research and practical applications in accounting. Findings The study reveals that LLMs significantly enhance efficiency, transparency and innovation in accounting by automating processes like financial reporting, ESG disclosure and risk management. They enable advanced applications such as intelligent knowledge bases, budget optimization and automated contract management. However, challenges remain, including the need for high-quality data, domain-specific model training, interdisciplinary talent development and robust data security measures. The findings underscore LLMs’ potential to transform accounting practices while emphasizing the importance of theoretical frameworks and strategic planning to address these challenges and fully realize their benefits in driving industry progress and sustainability. Practical implications The study highlights practical pathways for integrating LLMs into accounting, emphasizing their potential to automate processes, enhance decision-making and improve operational efficiency. Organizations can leverage LLMs for tasks such as financial reporting, ESG analysis and risk management, reducing manual effort and increasing accuracy. Practical implications include the need for targeted training of LLMs in accounting-specific contexts, robust data governance to ensure quality and security and developing interdisciplinary skills among accounting professionals. By addressing these areas, organizations can harness LLMs to drive innovation, streamline operations and achieve sustainable growth in a rapidly evolving business environment. Originality/value This study provides a comprehensive and systematic analysis of the transformative impact of LLMs on accounting, addressing gaps in fragmented research and limited practical insights. It uniquely integrates theoretical perspectives with practical applications, offering a structured framework for understanding LLMs’ role across multiple accounting domains. By identifying key challenges and proposing actionable strategies, the paper delivers original value to both researchers and practitioners, fostering innovation and guiding the integration of LLMs into accounting practices. Its forward-looking approach offers a valuable resource for advancing knowledge and shaping the future of accounting in the digital age.