Generative AI in Financial Reporting: Early Evidence from the Field

Last registered on November 15, 2024

Pre-Trial

Trial Information

General Information

Title
Generative AI in Financial Reporting: Early Evidence from the Field
RCT ID
AEARCTR-0014740
Initial registration date
October 31, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 15, 2024, 1:06 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Affiliation
MIT Sloan School

Other Primary Investigator(s)

PI Affiliation
Stanford Graduate School of Business

Additional Trial Information

Status
In development
Start date
2024-11-01
End date
2025-06-30
Secondary IDs
J01
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The question of how artificial intelligence (AI) will reshape accounting is critical to the economy and has far-reaching implications for practitioners, regulators, and academics. The advent of generative AI stands to revolutionize accounting processes and their resultant outcomes. According to a recent report by Gartner (2024), the adoption rate of generative AI in accounting was 5% in 2023, but is expected to increase rapidly in the coming years. The introduction of Generative AI to accounting has two main potential areas of significant consequence. First, Generative AI can significantly transform financial reporting quality by automating routine tasks, accelerating complex data analysis, and enhancing reporting accuracy. Second, as a result, Generative AI can significantly transform accounting careers both on the intensive margin (e.g. a shift in type of tasks) and on the extensive margin (e.g. a shift in labor demand and supply). The goal of our research project is to evaluate these two intertwined topics in tandem through a randomized controlled trial. The specific research questions in this project include: 1) Whether and to what extent does Generative AI affect financial reporting quality, 2) What is the quantifiable efficiency gain in accounting processes assisted by Generative AI, 3) What is the impact of Generative AI on the development and career satisfaction of accountants, and 4) Whether Generative AI produces accounting information that enhances firm operations.
External Link(s)

Registration Citation

Citation
Choi, Jungho and Chloe Xie. 2024. "Generative AI in Financial Reporting: Early Evidence from the Field." AEA RCT Registry. November 15. https://doi.org/10.1257/rct.14740-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2024-11-01
Intervention End Date
2025-03-31

Primary Outcomes

Primary Outcomes (end points)
1) Whether and to what extent does Generative AI affect financial reporting quality (e.g. Accrual quality, timeliness)
2) What is the quantifiable efficiency gain in accounting processes assisted by Generative AI (e.g. Number of clients, work hours allocation)
3) What is the impact of Generative AI on the development and career satisfaction of accountants (e.g. Satisfaction survey, retention)
4) Whether Generative AI produces accounting information that enhances firm operations (e.g. Future cash flow predictability)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Staggered introduction of AI usage
Randomized treatment across accountants from various accounting firms
Weekly surveys over three months
Archival data collection from both treatment and control group
Experimental Design Details
Not available
Randomization Method
Coin flip
Randomization Unit
Accountant (individual)
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
15 accounting teams
Sample size: planned number of observations
200
Sample size (or number of clusters) by treatment arms
50 accountants using AI, 50 accountants control, 200 accountants responding to survey
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Stanford University Research Compliance Office (RCO) Human Subjects (IRB) Panel on Non-Medical Human Subjects
IRB Approval Date
2024-10-07
IRB Approval Number
76715