Operational Resilience:Industry Benchmarking
Bibliographic record
Abstract
In a series of conversations with financial executives across Canada, we discussed the current state of operational resilience planning and their organizations’ plans for the future. The primary challenges mentioned were a high dependency on third (and fourth) party providers, increased organizational complexity, getting appropriate buy-in and focus across the organization, and regional variations in regulatory requirements. To address these challenges, and heighten their resilience, organizations are finding and pursuing several opportunities, which include mechanisms for identifying and prioritizing their critical services, as well as leveraging a global workforce to provide distributed capabilities. Organizations also discussed approaches for dealing with differing regulations globally. In terms of resilience structure, organizations have looked at their governance frameworks and ensuring they are fit for purpose, as well as utilizing stress and scenario testing to assess their capabilities. An effective training program underpins a solid resilience plan, and organizations discussed their approaches here as well. In a mid- to post-pandemic world, an effective resilience strategy has been, and will continue to be, integral to the success of financial institutions. The current environment provides a compelling reason for firms to bolster their capabilities.
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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 | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".