Identifying collateral minimization opportunities for a Canadian bank in the large value transfer system using process control techniques
Bibliographic record
Abstract
The Large Value Transfer System (LVTS) is one of the most important payment systems in Canada with which fifteen members (including the Bank of Canada and the major Canadian bank being studied) send money to each other. The Canadian Bank studied currently processes outgoing payments on a First-In First-Out basis and only employs manual controls to manage liquidity needs for large "Jumbo" transactions (those exceeding 100 million Canadian dollars). This time consuming manual process does not optimize liquidity usage which results in excess collateral being tied up in the LVTS system at a significant cost to the Bank. This work developed a real time controller, similar to those in use in continuous process manufacturing systems, involving an engineering software package, CadSim. The Controller manages the Canadian Bank's payments process automatically to optimize its liquidity and collateral needs and to provide an overview of its role in the LVTS. The Controller's efficiency was tested with the Bank's historical data and found that collateral requirements can be reduced by 20% with minor payment delays. The reduction in liquidity needs will free up valuable collateral for the Bank and hence, reduce the opportunity cost of using the LVTS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".