The gendering of fraud: an international investigation
Bibliographic record
Abstract
Purpose Changing workplace demographics reflect a rising number of women in the traditionally male-dominated field of business. The purpose of this study is to investigate how upwardly mobile women may impact the commission and type of white-collar crime, contributing to the scarce literature on gender distinctions in criminal behavior while comparing criminal trends globally. Women’s increased representation in positions of power in business provides them with increased fraud opportunities prompting the authors to ask: in their areas of opportunity, do women and men commit the same types of white-collar crime and at the same rates, and how does this phenomena vary globally? Design/methodology/approach Using a database from the Institute for Fraud Prevention, 5,441 fraud cases are examined from 93 nations for the annual periods from 2002 until 2011. Ordinal logistic regression methods are used to test for differences in gendered criminal behavior by fraud offense type controlling for age, position, education, compensation level and country context. Findings Internationally, results from the study indicate that female fraudsters are three times more likely than male fraudsters to commission crimes of asset misappropriation in the workplace. Upon further investigation, stratifying the data by geographical region, findings from the study demonstrate that female fraudsters are more likely than male fraudsters to commit asset misappropriation in the following geographical regions: Africa (three times as likely), Asia (twice as likely), Canada (three times as likely), China (five times as likely), Europe (twice as likely), the Middle East (four times as likely), Oceania (four times as likely), the United Kingdom (eight times as likely) and the United States of America (twice as likely). Originality/value Evidence from this study should be of importance to multinational enterprises, auditors and fraud examiners, as asset misappropriation constitutes 87 per cent of all fraud cases globally. Further, these findings prompt the need for researchers to develop this area of research.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".