Mapping the individual and structural theories of financial crimes
Bibliographic record
Abstract
Purpose The purpose of this paper is to argue for an integrated explanation of financial fraud. Greater emphasis must be placed on the structural and situational factors that are the elements of fraud risks and fraud. Design/methodology/approach The paper is based on a review of the literature on the explanation of financial fraud. Both micro- and macro-theoretical explanations of fraud were analysed to allow for a broader picture of the types of individuals that were involved in fraud, the rules governing their conduct and the types of law they broke. Findings The main reason why people commit fraud is that their crime propensity interacts with the elements present in criminogenic environments. Indeed, because most of the research on structural theories of fraud focuses on general criminality, not much has been done in the area of financial fraud. More research needs to be carried out to excavate the subterranean cluster of narrative on fraud risks and fraud. Research limitations/implications To address the future contingency of fraud risks, the paper adopted a similar position of prior accounting research on financial crimes. The structural explanation of fraudulent behaviour considers individuals’ actions to be less the result of individual deviance and more the cause of societal forces. Structural theories take into consideration the individual psychology of the offenders and position it to reflect the various realities – institutional, structural and cultural life – they are caught up in. Future research must endeavour to address these concerns. Originality/value The manuscript is among a new stream of literature that addresses the structural elements of financial fraud.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".