Bibliographic record
Abstract
Purpose – The purpose of this paper is to analyze the effect of various internal controls (i.e. hotlines, regular ethics (fraud) training, surprise audits, internal and external audits and background checks) on reducing occupational fraud losses by victim organizations. Design/methodology/approach – The paper, based on data from an occupational fraud report co-authored by the Association of Certified Fraud Examiners (ACFE) and Peltier-Rivest (2007), uses a multivariate regression analysis to analyze the effect of various internal controls on preventing fraud losses. Findings – The authors’ analyses demonstrate that hotlines, regular ethics (fraud) training, surprise audits and internal audits all decrease fraud losses when used separately. However, hotlines and surprise audits are the only statistically significant controls when controlling for the potential correlation among all internal controls. Hotlines are associated with a reduction of 54 per cent in median fraud losses, while surprise audits cut median losses by 69 per cent. Research limitations/implications – This study contributes to academia and the anti-fraud profession by assessing the statistical effect of six internal controls on preventing fraud losses, while controlling for the potential correlation among these controls. Practical implications – This study discusses the relative benefits (loss savings) of various internal controls to organizations, governments, managers and anti-fraud professionals. This information may help determine investment priorities in the context of scarce resources. Originality/value – This paper is based on proprietary data owned by the ACFE and is the first to analyze the statistical significance of various internal controls on the reduction of fraud losses in Canada.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".