Exogenous Data Using Large Language Models Like ChatGPT
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© 2025 Irina Jie Bao and Benita Gullkvist, published by Emerald. This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com.
This chapter explores the role and potential of Large Language Models (LLMs) like ChatGPT to process exogenous data for various applications through illustrations with GPTs and several useful plug-ins, as well as a practical use case. Exogenous data refer to data external to organization’s traditional, usually internal, financial data collection processes and may include social media, online searches, networks, and news media. The aim of utilizing exogenous data in accounting and auditing is to complement traditional financial reporting and assurance methods by offering a more nuanced and comprehensive view of a company’s activities, as well as enhancing the quality of reporting and audit processes. Stakeholders can improve their decision-making processes by integrating publicly available exogenous data into their analyses. Although exogenous data can provide valuable insights beyond traditional financial statements, acquiring such data might be expensive and time-consuming. Therefore, there is a need for effective tools like ChatGPT to facilitate the process of extracting and analyzing data.
Emojulkaisu
Exogenous Data in Accounting and Auditing in the Rutgers Series in Accounting Information Systems
ISBN
978-1-83608-734-2
ISSN
Aihealue
Kausijulkaisu
Rutgers Series in Accounting Information Systems
OKM-julkaisutyyppi
A3 Kirjan tai muun kokoomateoksen osa (vertaisarvioitu)