Anti-money laundering and moral intensity in suspicious activity reporting
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 examine the influence Jones’ Moral Intensity Model (1991) has on the decision-making process of anti-money laundering (AML) compliance officers charged with reporting suspicious money laundering transactions in Jersey. Design/methodology/approach Ten interviews were conducted to elicit participants’ views on the six dimensions of moral intensity and their influence on the compliance officers’ decision to submit a suspicious activity report (SAR) of potential money laundering. Findings The findings indicate that the officers’ moral intensity to submit a SAR seems to be heavily influenced by issue-specific contextual factors. Contexts (legal and legislative mandates) seem to have more of an effect on the moral intent and actions of the officers rather than directly affecting the decision to submit a report of a suspicious money laundering transaction. Research limitations/implications The paper lays the groundwork for further work in this area and calls on researchers to develop instruments that can enhance the measurements of the dimensions of moral intensity. Practical implications The setting (AML in the financial sector) is both timely and extremely interesting to keep studying, particularly in Jersey because of its dubious sensitive particularities. Originality/value The study is the first to examine Jersey AML sector through the lens of moral intensity. In this sense, the paper poses interesting questions, namely, to explore the dynamic complexities experienced by compliance officers in Jersey to detect and report suspicious money laundering activities and the decision-making criteria of actually submitting a SAR.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it