The Impact of Audit Technology on Audit Task Outcomes: Evidence for Technology‐Based Audit Techniques*
Bibliographic record
Abstract
ABSTRACT As audit technology becomes more widespread, practice and academia are raising concerns about the costs and benefits of these technologies. We examine how internal auditors use technology‐based audit techniques (TBATs) and how TBATs impact the efficiency and effectiveness of their audits. We use two surveys and interviews of individual auditors and chief audit executives (CAE) to examine their perceptions of TBATs. Auditors perceive TBATs as beneficial. Specifically, an increase in the use of TBATs is associated with completing more audits, finding more risk factors, providing more recommendations, and decreasing audit days. However, CAEs also perceive TBATs to be costly. An increase in the use of TBATs is associated with an increase in the size of the internal audit function. Finally, interviews with CAEs suggest that TBATs are not used more often because of difficulties in quantifying their benefits, observing their benefits in a timely manner, and hiring auditors with appropriate skills. Overall, TBATs stand to increase the efficiency and effectiveness of audit tasks, but auditors struggle to quantify their net cost‐benefit tradeoff. Our findings validate the issues raised by both proponents and opponents of audit technologies and help provide empirical data to inform their decision‐making process regarding the future of these tools. Additionally, our study prompts several avenues for future research that can help inform regulators, practitioners, and researchers on how these technologies are impacting the auditing profession.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.275 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".