Using Synthetic Data to Measure the Impact of RTGS on Systemic Risk in the Australian Payments System
Bibliographic record
Abstract
This paper examines the possibility that financial contagion may be spread from one bank to another via the Australian payments system. The initial study of payments system risk was undertaken by Humphrey (1986) who found significant risk in the U.S. Fedwire system in the mid 1980s. Subsequent studies by Angelini, Maresca & Russo (1996), Kuussaari (1996), Northcott (2002) and Furfine (2003) have found, however, little evidence of systemic risk in the payments systems of Italy, Finland and Canada, and in the U.S. inter-bank market. Given that the implementation of real time gross settlement (RTGS) systems in many countries, including Australia, at significant cost, has been designed to reduce payments system risk, the finding that this risk is small is significant. While detailed payments system data for Australia is not available to researchers outside the Reserve Bank, this study constructs a synthetic data set based on available information and uses this data to simulate the failure of each financial institution operating in the Australian payments system. We find little evidence of systemic risk in the Australian payments system using this approach and conclude that the introduction of RTGS in the Australian system in 1996 had only a marginal effect on risk.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".