{"title":"AI Chatbot as IFRS Advisory Tool: GPT-4 Experimental Design","authors":"Todor Tocev, Atanasko Atanasovski","doi":"10.1002/isaf.70031","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>The complexity of International Financial Reporting Standards (IFRS) challenges accounting professionals to navigate intricate judgment calls and estimations. This paper tackles a pressing question: Can OpenAI's ChatGPT (Version GPT-4) serve as a reliable artificial intelligence (AI) advisory tool to interpret and apply IFRS standards in real-world scenarios? The importance of this inquiry lies in the potential of generative AI to revolutionize financial reporting by enhancing accuracy, efficiency, and decision-making speed, which are critical demands in today's globalized financial environment. Through an experimental design employing practical case studies, this research evaluates GPT-4's performance under three prompting strategies: zero shot (ZS), few shot (FS), and chain of thought (CoT). This research examines the ability of AI to address judgment-driven, complex IFRS problems, expanding the scope of prior studies that primarily relied on theoretical exams or professional certification tests. Our findings reveal that GPT-4 can consistently identify the correct IFRS standard and produce professionally usable guidance, exhibiting strong potential. ZS proved fastest and most practical for a first advisory pass, FS delivered more structured and accounting-like answers but required greater preparation, and CoT generated the richest explanations at the expense of efficiency. Across all strategies, expert review remained necessary in areas involving item and measurement choices, contract integration, or business-model interpretation. This study efforts to advance the dialogue on AI's role in accounting and lays a foundation for future research exploring its broader implications in accounting decision-making. With insights into GPT-4's strengths and constraints, this study emphasizes its role as a transformative, yet supplementary, tool in advancing IFRS compliance and reporting standards.</p>\n </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Systems in Accounting, Finance and Management","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/isaf.70031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Economics, Econometrics and Finance","Score":null,"Total":0}
引用次数: 0
Abstract
The complexity of International Financial Reporting Standards (IFRS) challenges accounting professionals to navigate intricate judgment calls and estimations. This paper tackles a pressing question: Can OpenAI's ChatGPT (Version GPT-4) serve as a reliable artificial intelligence (AI) advisory tool to interpret and apply IFRS standards in real-world scenarios? The importance of this inquiry lies in the potential of generative AI to revolutionize financial reporting by enhancing accuracy, efficiency, and decision-making speed, which are critical demands in today's globalized financial environment. Through an experimental design employing practical case studies, this research evaluates GPT-4's performance under three prompting strategies: zero shot (ZS), few shot (FS), and chain of thought (CoT). This research examines the ability of AI to address judgment-driven, complex IFRS problems, expanding the scope of prior studies that primarily relied on theoretical exams or professional certification tests. Our findings reveal that GPT-4 can consistently identify the correct IFRS standard and produce professionally usable guidance, exhibiting strong potential. ZS proved fastest and most practical for a first advisory pass, FS delivered more structured and accounting-like answers but required greater preparation, and CoT generated the richest explanations at the expense of efficiency. Across all strategies, expert review remained necessary in areas involving item and measurement choices, contract integration, or business-model interpretation. This study efforts to advance the dialogue on AI's role in accounting and lays a foundation for future research exploring its broader implications in accounting decision-making. With insights into GPT-4's strengths and constraints, this study emphasizes its role as a transformative, yet supplementary, tool in advancing IFRS compliance and reporting standards.
期刊介绍:
Intelligent Systems in Accounting, Finance and Management is a quarterly international journal which publishes original, high quality material dealing with all aspects of intelligent systems as they relate to the fields of accounting, economics, finance, marketing and management. In addition, the journal also is concerned with related emerging technologies, including big data, business intelligence, social media and other technologies. It encourages the development of novel technologies, and the embedding of new and existing technologies into applications of real, practical value. Therefore, implementation issues are of as much concern as development issues. The journal is designed to appeal to academics in the intelligent systems, emerging technologies and business fields, as well as to advanced practitioners who wish to improve the effectiveness, efficiency, or economy of their working practices. A special feature of the journal is the use of two groups of reviewers, those who specialize in intelligent systems work, and also those who specialize in applications areas. Reviewers are asked to address issues of originality and actual or potential impact on research, teaching, or practice in the accounting, finance, or management fields. Authors working on conceptual developments or on laboratory-based explorations of data sets therefore need to address the issue of potential impact at some level in submissions to the journal.