Bibliographic record
Abstract
Purpose International bodies, such as the Financial Action Task Force , have mandated the use of financial intelligence units (FIU) to address organized crime and money laundering. The purpose of this paper is to examine Canada’s FIU, the Financial Transactions and Reports Analysis Centre of Canada (FINTRAC), and explore its current effectiveness and future challenges. Design/methodology/approach This paper examines FIUs in general and then looks more specifically at Canada’s FIU, its policy and legislative basis as well as future challenges for the FIU. Findings The challenge money laundering poses to society is a mirror of the challenge that organized crime poses: a test of the values and the importance of rule of law. The FIU is an important mechanism to address this challenge generally, and there are important changes in the environment that must be addressed if the future policy objectives of the FIU are to be met. Research limitations/implications Some of the policy nostrums that are baked into the anti-money laundering system, such as placement, layering and integration, need to be revisited and researched to incorporate changes in the licit and illicit marketplaces. Practical implications Financial institutions and other intermediaries must comply with domestic anti-money laundering laws. Compliance is always contextual, and this paper will outline the role of the regulator and the environmental challenges that need to be met. Social implications Effectively addressing money laundering and organized crime is critical to the maintenance of rule of law and the protection of the financial system. Originality/value This is a brief but very fulsome review of Canada’s FIU, FINTRAC, which captures broader challenges in addressing money laundering, economic crime and regulatory systems designed to protect rule of law and the integrity of the financial system. The paper not only examines the current state of the FIU but also explores challenges on the horizon.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.113 | 0.014 |
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".