Cashless Gambling: Potential Money Laundering and Responsible Gambling Initiatives
Bibliographic record
Abstract
Recent Australian government inquiries into casino, club, and hotel activities identified significant money laundering, and/or junket and links to organised crimes, and governance failings. The findings of two Royal Commissions determined that casinos in Sydney and Melbourne were not suitable entities to hold a gaming licence. Regulators gave these casinos two years to address concerns raised during these inquiries. One recommendation, supported by media reports and public health advocates, suggested the implementation of cashless gambling, that is, the use of non-cash forms of gambling (e.g., digital wallets, QR codes, or gambling debit cards). Others have expressed concerns about counterproductive or unintended consequences of tokenization of money and difficulties in monitoring expenditure. Although potentially useful as an anti-money laundering initiative, the effective use of cashless gambling as a harm minimisation/responsible gambling initiative requires careful consideration of its architecture, that is, the structure, processes and oversight of its implementation and operation. In this paper, we describe the complexities of cashless gambling and highlight relevant issues that need to be addressed. The findings of the various inquiries also raise serious questions regarding the proportion of funds commonly ascribed to individuals with gambling disorders. We conclude that key stakeholders (e.g., government, industry, financial and academic) need to collaborate to develop an optimal cashless gambling structure that achieves its intended objectives for responsible gambling over and above anti-money laundering.
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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 | 0.002 | 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 | 0.001 | 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".