In late 2023, the Financial Services Commission and the Financial Supervisory Service announced amendments to K-IFRS to enhance the transparency and accountability of virtual asset issuers, exchanges, and holders. These amendments strengthen the disclosure requirements for virtual assets in financial statements and provide standardized guidelines for accounting treatment in the virtual asset industry. This measure aims to improve the reliability of the virtual asset market and strengthen investor protection. This study explores various methods of integrating generative AI, specifically large language models (LLMs), into virtual asset accounting. As a practical example, it demonstrates how LLM prompts can automatically identify and extract key transaction items from virtual asset transaction statements and output them in the disclosure format required by the Financial Supervisory Service. The method for extracting and converting the necessary items for report preparation utilizes the Chain-of-Thought (CoT) reasoning function, automating the process of converting and reporting virtual asset transactions such as purchase amounts and revenue recognition. Effective use of LLM prompts is expected to facilitate the automation of accounting tasks and address existing challenges faced by the accounting profession by emphasizing the role of artificial intelligence (AI) in strategic decision-making processes. This case study proposes a technical approach applicable to the management and supervision of virtual asset transactions, aiming to enhance the efficiency, accuracy, and strategic value of accounting practices. It highlights the role of LLM prompt utilization technology and aims to contribute to innovative changes in the automation of accounting disclosure tasks. Our code is available at https://github.com/JSJeong-me/GPT-Disclosures.
