Applying AI to Canada's Financial Intelligence System: Promises and Perils in Combatting Money Laundering and Terrorism Financing
Bibliographic record
Abstract
Money laundering (ML) and terrorist financing (TF) are pernicious global challenges. Estimates suggest that ML represents 2 to 4 percent of global GDP, disrupting financial systems, hindering anti-corruption efforts, fuelling terrorism, and destabilizing governments and security institutions globally. Emerging technologies like artificial intelligence (AI) complicate detection and prosecution efforts, enabling anonymity in the movement of money. This article explores AI's role in anti-money laundering (AML) efforts and in countering the financing of terrorism (CFT), focusing on data analysis for financial intelligence units (FIUs) and private sector reporting entities. It addresses AI's uses, benefits, and risks in Canadian and international AML/CFT, explores opportunities and challenges of AI adoption, and proposes next steps for research and practical application. By examining AI's promises and perils in financial intelligence, this article aims to contribute to academic and policy-oriented efforts to understand and leverage AI for effective ML/TF prevention.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".