Preventing fraudulent financial reporting with reputational signals of strategic auditors
Bibliographic record
Abstract
Abstract Financial reporting fraud continues to cost companies millions of dollars annually and is a major source of concern for regulators, stakeholders, and auditors. While academic research has largely focused on external auditors' fraud detection efforts, we analyze whether auditors can help prevent occurrences of fraud through low‐cost reputational signals of higher “strategic reasoning”; strategic reasoning refers to strategies that individuals take in light of the anticipated actions of others (see van der Hoek et al., 2005, A logic for strategic reasoning, AAMAS '05, 157−164). Specifically, we consider the potential impact on manager behavior of signaling whether audit professionals use zero‐, first‐, and second‐order audit approaches. Zero‐order audit approaches involve making decisions based mostly on the auditor's incentives, first‐order approaches involve decisions based mostly on the client's incentives, and second‐ or higher‐order audit approaches involve decisions based on the client's incentives while recognizing that the client will respond to the auditor's decisions (see Wilks & Zimbelman, 2004, Accounting Horizons , 18 (3), 173–184). Using a context‐rich experiment in which manager participants have no history of interacting with the auditor, we find that the likelihood of fraud occurring is lower when it is signaled that audit partners and their teams use a first‐ or second‐order strategic audit approach compared to a zero‐order approach, due to an increase in the perceived likelihood of the auditor detecting fraud. We also consider whether signaling an auditor's level of strategic reasoning influences the level of effort used to conceal fraud and find an increase in the expected level of fraud effort for managers in the first‐ and second‐order audit conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".