The Ties that Bind: The Decision to Co‐Offend in Fraud
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
It is frequently observed that fraud has a greater economic impact on society than any other category of crime. Arguing that both research and practitioner frameworks in auditing and forensic accounting have tended to adopt an individualizing perspective predicated primarily on solo offending, this article adopts an inductive approach to consider why individuals co‐offend in fraud. It reports the results of a set of interviews with 37 individuals convicted of a range of frauds including financial statement fraud, insider trading, credit card fraud, money laundering, and asset misappropriation. In each instance, the fraud was perpetrated by a group of two or more co‐offenders. Based on inductive, exploratory case coding, we find that reasons for co‐offending vary according to the type of bond that exists between co‐offenders. Two dimensions of fraudulent co‐offending are identified—the primary beneficiary of the fraud and the nature of group attachment—to derive three distinct archetypes of bonds between co‐offenders: (1) individual‐serving functional bonds, (2) organization‐serving functional bonds, and (3) affective bonds. Key elements of each archetype as well as their impact on the decision to co‐offend are examined. Our findings suggest that the social nature of fraud is not merely an incidental feature of the crime but is instead a potential key to understanding its etiology and some of its distinctive features. They also support the need for diagnostic tools to move beyond individualistic analyses of fraud toward a broader, group‐sensitive assessment of fraud risk.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it