An analysis of the victims of occupational fraud: a Canadian perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper aims to describe and explain characteristics of organizations that are victims of occupational fraud. Design/methodology/approach This study is based on a 2006 occupational fraud web survey conducted in Canada by the Association of Certified Fraud Examiners (ACFE). Findings The analysis shows that occupational fraud losses are quite large, accounting for a median loss of C$187,500 and a mean loss of C$1,142,494. These losses represent, respectively, 0.3 percent (median) and 9 percent (mean) of the victim organization's annual sales. Private companies, not‐for‐profit organizations and small businesses are particularly vulnerable to relatively larger fraud losses. It is also shown that the smaller the organization the more likely fraud losses will be relatively larger. Research limitations/implications This study contributes to academia by measuring the statistical significance of the cost of occupational fraud per various organizational characteristics. Practical implications This study is useful to regulatory agencies and anti‐fraud professionals because it provides information about what types of organizations are more vulnerable to fraud, thus indicating where prevention and detection efforts should be directed. Originality/value This paper is based on proprietary data owned by the ACFE and is the first to analyze the statistical significance of the consequences (cost) of occupational fraud 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it