Transmission of Cyber Risk Through the Canadian Wholesale Payments System
Bibliographic record
Abstract
In response to growing concerns about cyber and other operational risks—such as those related to climate change—international organizations, central banks and private sector entities have taken collaborative actions to increase the operational and data resilience of financial institutions and financial market infrastructures, including payment systems. In Canada, the Bank of Canada has established and leads the Canadian Financial Sector Resiliency Group and the Resilience of Wholesale Payments Systems (RWPS) initiative, which both offer a forum for coordinating a national sectoral response to systemic operational incidents, such as cyber attacks (see Dinis 2021 for details). As part of the RWPS initiative, this paper studies how the impact of a cyber attack that paralyzes one or multiple banks' ability to send payments would transmit to other banks through the Canadian wholesale payment system. Based on historical payment data, we simulate a wide range of scenarios and evaluate the total payment disruption in the system. We find that depending on the type and number of banks under attack, the time of the attack and the design of the payment system, a cyber attack can quickly become systemic and result in a significant loss of liquidity throughout the system. We also demonstrate that the system-wide impact of an attack can be significantly reduced by having contingency plans that enable attacked banks to continue to send high-value payments. Given the interconnectedness of banks, we conclude that the cyber resilience of a wholesale payment system depends strongly on the cyber resilience of its participants, and we underline the importance of strong sectoral collaboration and coordination.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".