Bank resolution in South Africa: Recent developments
Bibliographic record
Abstract
Abstract In this contribution, the authors explore recent developments in South Africa's approach to bank resolution, assessing compliance of its new regime with the Financial Stability Board's Key Attributes of Effective Resolution Regimes for Financial Institutions (KAs). Emphasizing the imperative for orderly resolutions to avert financial crises, the authors scrutinize the post‐2008 Global Financial Crisis regulatory landscape. Until June 2023, South Africa's approach to bank failure was limited to curatorship and liquidation under the Banks Act 94 of 1990. Addressing gaps identified by international bodies such as the Financial Stability Board, International Monetary Fund, and the World Bank, and taking lessons from the failure of African Bank in 2014, South Africa has transitioned to a Twin Peaks regulatory model and also introduced a comprehensive resolution framework effective June 1, 2023, captured in the Financial Sector Regulation Act 9 of 2017. This framework currently applies to banks only. The South African Reserve Bank now holds an explicit financial stability mandate and is designated as the resolution authority. The article discusses the design features for an effective resolution regime as recommended in the KAs to benchmark the new South African resolution regime, noting overall compliance. However, it acknowledges the need for further development in certain respects to enhance alignment.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".