AI-powered information and Big Data: current regulations and ways forward in IFRS reporting

Susanne Leitner-Hanetseder, Othmar M. Lehner*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)


Purpose: With the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining economic benefits. AI-powered information and Big Data (simply data henceforth) have quickly become some of the most important strategic resources in the global economy. However, their value is not (yet) formally recognized in financial statements, which leads to a growing gap between book and market values and thus limited decision usefulness of the underlying financial statements. The objective of this paper is to identify ways in which the value of data can be reported to improve decision usefulness. Design/methodology/approach: Based on the authors' experience as both long-term practitioners and theoretical accounting scholars, the authors conceptualize and draw up a potential data value chain and show the transformation from raw Big Data to business-relevant AI-powered information during its process. Findings: Analyzing current International Financial Reporting Standards (IFRS) regulations and their applicability, the authors show that current regulations are insufficient to provide useful information on the value of data. Following this, the authors propose a Framework for AI-powered Information and Big Data (FAIIBD) Reporting. This framework also provides insights on the (good) governance of data with the purpose of increasing decision usefulness and connecting to existing frameworks even further. In the conclusion, the authors raise questions concerning this framework that may be worthy of discussion in the scholarly community.
Research limitations/implications: Scholars and practitioners alike are invited to follow up on the conceptual framework from many perspectives.
Practical implications: The framework can serve as a guide towards a better understanding of how to recognize and report AI-powered information and by that (a) limit the valuation gap between book and market value and (b) enhance decision usefulness of financial reporting.
Originality/value: This article proposes a conceptual framework in IFRS to regulators to better deal with the value of AI-powered information and improve the good governance of (Big)data.

Original languageEnglish
Peer-reviewed scientific journalJournal of Applied Accounting Research
Publication statusPublished - 15.07.2022
MoE publication typeA1 Journal article - refereed


  • 512 Business and Management
  • IFRS
  • AI
  • big data
  • intangible assets

Areas of Strength and Areas of High Potential (AoS and AoHP)

  • AoS: Financial management, accounting, and governance


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