How to deal with multiple regulators in multiple jurisdictions
Bibliographic record
Abstract
Financial institutions are facing mounting compliance obligations as regulators place increased responsibility on them to combat financial crime. For financial institutions operating internationally, this has created the challenge of handling multiple regulators across multiple jurisdictions on matters of non-compliance. Non-US regulators have acquired, and continue to acquire, enhanced enforcement powers to investigate, levy fines and require remedial action of financial institutions on matters of non-compliance. Consequently, financial institutions must increasingly consider their compliance obligations across all their presence countries. The paper draws upon published sources, considering US and UK requirements predominantly, but also developments in Hong Kong, Singapore, France, Canada and Australia. It was reviewed, with input, by colleagues working in these locations. It argues that the increasingly complex global regulatory landscape combined with enhanced enforcement powers requires financial institutions to consider their regulatory obligations across jurisdictions in addition to meeting any US regulatory requirements. The paper provides practical suggestions for how to address an investigation for non-compliance whether internally or by one regulator or multiple cross-jurisdictional regulators (separately or as part of a global settlement). It is the intention of the paper to provide senior management in financial institutions an overview of what to consider during investigation, settlement and remediation of non-compliance with financial crime matters.
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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 | 0.075 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.026 | 0.024 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".