大语言模型在财务会计领域面临根本性应用障碍

有分析认为，大语言模型在财务会计工作中存在难以克服的局限性。1根本问题在于，大语言模型基于概率逻辑运作，而会计工作需要确定性逻辑和高精度操作。1即便准确率达到95%，在大规模账簿环境中仍会产生大量错误——以包含10万条分类账为例，95%的准确率意味着约1000条错误记录难以被发现。1相比之下，人类会计师的准确率通常低于90%，但他们的优势在于可被追究法律责任。1

法律和监管框架进一步强化了这一劣势。1《萨班斯-奥克斯利法案》要求公司必须配置具备人类身份的财务总监和控制人，这意味着AI无法满足相关法律要求。1法院也明确不允许AI律师出庭陈述案件，无论其表现如何优异。1

Large language models encounter inherent limitations when applied to financial accounting work that may prove insurmountable for practical deployment. The core issue stems from LLMs' probabilistic rather than deterministic approach to computation—a characteristic that translates poorly to domains requiring high precision 1. While LLMs have shown greater effectiveness in fields such as law, their performance in accounting falls substantially short of operational viability.

The accuracy challenge becomes apparent when scaled to real-world scenarios. Campfire, which has raised $65 million to build accounting modules from the ground up, has achieved only approximately 95% accuracy 1. This figure, though superficially close to perfect, masks a critical flaw: in a ledger containing 100,000 entries, such accuracy would still produce around 1,000 erroneous records 1. Identifying and correcting these errors poses a significant operational burden that undermines the claimed benefits of automation.

Beyond technical performance, regulatory and legal frameworks create structural disadvantages for AI systems in this domain. The Sarbanes-Oxley Act mandates that companies employ a human chief financial officer and internal controls personnel 1, requirements that AI cannot satisfy. Additionally, human accountants maintain a competitive advantage grounded in legal accountability: they can be held liable for their work, whereas AI systems currently lack such recourse 1.

Human accountants, despite achieving accuracy rates typically below 90%, retain this legal liability as their primary advantage over AI alternatives 1. Until LLMs solve both their inherent probabilistic limitations and navigate the regulatory requirement for human responsibility, building AI systems for core financial functions remains fundamentally impractical.