Non-bank financial intermediation: Canada’s submission to the 2024 global monitoring report
Bibliographic record
Abstract
The global non-bank financial intermediation (NBFI) sector has grown significantly since the 2008–09 global financial crisis. Because of this growth, the Financial Stability Board (FSB) established in 2016 the Non-bank Monitoring Experts Group, which collects data annually from 29 jurisdictions and produces the Global Monitoring Report on Non-Bank Financial Intermediation (GMR) (Financial Stability Board 2024). The GMR summarizes growth in the NBFI sector and key subsectors in each jurisdiction. The Bank of Canada works closely with Statistics Canada, the Ontario Securities Commission and the Office of the Superintendent of Financial Institutions to compile Canadian data for the GMR. We share insights from data from 2002 to 2023 that the Bank has collected and submitted to the FSB for inclusion in the GMR.1 Although we provide context for recent developments, we do not present the Bank’s overall assessment of vulnerabilities related to either Canadian NBFI entities or more general activity in core financial markets. The Bank’s Financial Stability Report—2025 contains the most recent assessment of vulnerabilities associated with the NBFI sector (Bank of Canada 2025a).
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".