Evolution of risk management from risk compliance to strategic risk management Part II: The changing paradigm for the risk executive and Boards of the Canadian banking and insurance sectors
Bibliographic record
Abstract
Part I of this paper examined the risk function’s evolution in response to (i) financial disclosures becoming increasingly risk-based, (ii) an increasing need to optimise capital management and business mix to enhance Return on Equity (ROE). The optimisation frameworks to determine the optimal ‘risk strategies’ need to be established by the risk function, which is now at the core of financial disclosure, technology and strategy. Part II examines the necessary competencies for the risk executives, in particular Chief Risk Officers (CROs), to be effective and lead the evolution. These are analytical, digital and strategic competencies. For the leadership roles in well-established finance, accounting, actuarial functions, and in engineering, it is recognised that professional qualifications, advanced content knowledge and experience are required for the leaders to be effective. We observe that this is often not the case for bank risk executives. It is not uncommon to see a leader without specific risk expertise and experience holding senior risk executive, even CRO, roles. CRO roles also have limited upwards mobility and can be the last stop, bridging the executive to retirement. We examine the potential causes, including the historical reasons, insiders’ bias, cognitive biases, pigeon-holed career paths and misuse of power. We make suggestions for improvements and opening the path for the next generation of risk professionals to fill the executive and board roles and lead the necessary evolution.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".