Bibliographic record
Abstract
240-260Criminals are using artificial intelligence to scale money laundering in ways that were not possible only a few years ago. Synthetic identities built from fragments of real documents, deepfake onboarding videos that pass liveness checks and automated networks of mule accounts allow bad actors to move funds faster and hide their tracks more effectively. Traditional compliance systems, based on static rules and manual review, struggle to keep up. This presentation looks at how the AML landscape is shifting and why financial institutions need more adaptive technology. The industry is moving from simple red-flag detection toward richer behavioural analysis that understands how legitimate customers behave so anomalies become clearer. Graph intelligence is helping compliance teams uncover hidden relationships between accounts, wallets and devices that would never appear suspicious on their own. AI can reduce false positives and highlight the real threats that matter. The future of AML is not about replacing compliance teams. It is about equipping them with smarter tools that learn from new fraud patterns, integrate global sanctions intelligence and provide confidence at scale. With Singapores ambition to lead regional fintech innovation, the region is well positioned to adopt these capabilities early and set a benchmark for safer digital finance in Asia. Drawing on real-world implementation experience in high-risk sectors, this talk explores what is working today, how the technology must evolve and what forward-leaning financial institutions can do to stay ahead as criminals increasingly think and operate like algorithms.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".