arbiter
JournalsAccount

Financial Executives’ Responses to the Use of Artificial Intelligence in Financial Reporting and Auditing

Paige Csere et al.

Current Issues in Auditing2025https://doi.org/10.2308/ciia-2025-014article
AJG 2ABDC B
Weight
0.50

Abstract

SUMMARY This article summarizes “How do financial executives respond to the use of artificial intelligence in financial reporting and auditing?” (Estep, Griffith, and MacKenzie 2024; hereafter EGM). EGM survey financial executives about their perceptions of AI in financial reporting and experimentally examines how they would incorporate AI-generated information when resolving proposed audit adjustments. EGM find that financial executives do not have negative perceptions of AI. Further, in a hypothetical scenario, they find that when a financial executive’s company uses AI to help prepare an accounting estimate, financial executives book larger audit adjustments if auditors use AI to audit the estimate than if auditors do not use AI. If a financial executive’s company does not use AI, auditors’ use of AI has minimal influence on financial executives’ decisions. This paper provides insights for practice based on EGM’s findings. JEL Classifications: M41; M42; O33.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.2308/ciia-2025-014

Or copy a formatted citation

@article{paige2025,
  title        = {{Financial Executives’ Responses to the Use of Artificial Intelligence in Financial Reporting and Auditing}},
  author       = {Paige Csere et al.},
  journal      = {Current Issues in Auditing},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.2308/ciia-2025-014},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Financial Executives’ Responses to the Use of Artificial Intelligence in Financial Reporting and Auditing

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.