Continuous Auditing's Effectiveness as a Fraud Deterrent
Bibliographic record
Abstract
SUMMARY Continuous auditing increases the coverage and frequency of analysis of a firm's activities, and has been touted as a powerful fraud deterrence and detection technique, but we identify and examine a potential unintended consequence. When continuous auditing is accompanied by more timely notifications to auditees of exceptions to control rules, information is revealed about the system's capability to flag exceptions to control rules. Therefore, if a system has weak fraud-detection capability, early notification that the system did not detect a fraudulent transaction could actually increase an auditee's propensity to commit fraud. We examine whether the benefit of early notification depends on the fraud-detection capability of the organization's monitoring system (i.e., whether it is a strong or weak monitoring system). We use an experimental economics approach to address our research question. Consistent with expectations, we find that early and frequent notification of audit results is not always beneficial in deterring fraud, and that its benefit depends on whether the fraud-detection capability of the monitoring system is strong or weak. We do not find evidence of the predicted benefit of continuous notification reducing the incidence of fraud when the system is strong, but we do find an increase in participants' inclination to commit fraud when the system is weak. We discuss the implications of these findings for research and practice.
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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 | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".