COVID-19 triggers great nonfinancial risk crisis: Nonfinancial risk management best practices in Canada
Bibliographic record
Abstract
The spread of viral disease COVID-19 is the most transformative nonfinancial risk (NFR) of this decade triggering the Great Nonfinancial Risk Crisis. The uniting of strategy and risk management has never been more crucial for financial institutions. The interrelationship between the pandemic and the increases in ageing, chronic diseases, interstate conflicts, nationalism, cyber attacks, cyber dependency, asset bubble, and sovereign debt is transforming our reality in previously unimaginable ways. NFR management best practices Canadian Financial Institutions (FIs) prioritised NFR and adopted a NFR framework that enabled them to identify the spread of viral disease (COVID-19). Then they reprioritised COVID-19 risk into their existing enterprise risk management framework to reduce the exposure and impact of the pandemic and re-evaluated their strategic assumptions to reset their business strategy in light of the reprioritised risk matrix. In this paper, the author reviews best practices in managing NFRs and trends from the practitioner’s point of view. Thirteen Canadian FIs are reviewed along with their positioning of NFR pre- and post-COVID-19, and their recent enhancements to their NFR-management process. The author illustrates how the adoption of the Global Risks and Trends Framework by several Canadian FIs has influenced their preparation and resilience in this pandemic. Finally, the author discusses best practice examples, as well as challenges that still exist, how organisations have adjusted their strategy linking risk to their recent experience, and what lessons other FIs can learn about managing these NFRs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".