Bank crisis management and resolution after <scp>SVB</scp> and Credit Suisse: Perspectives from India and the European Union
Bibliographic record
Abstract
Abstract The March 2023 bank failures of Silicon Valley Bank, Signature, and Credit Suisse, which caused turmoil in financial markets and led to regulatory and central bank intervention, revived the debate about the effectiveness of the bank crisis management, resolution, and deposit insurance legal framework established after the Global Financial Crisis. Although the March 2023 events did not escalate into a full‐blown financial crisis, they drew attention to certain areas of the current framework, where improvements may be needed. These areas include the need for financial regulation and supervision to focus more on small‐ and medium‐sized banks as potential sources of systemic market events; to review the adequacy of the current deposit insurance regime and the treatment of uninsured deposits; and to provide more clarity about the order of creditor claims in case of bank resolution/insolvency. This article reviews the events of March 2023 and the key lessons from these events and discusses how these lessons could shape the frameworks for bank crisis management and resolution in India and the European Union. The two jurisdictions are in the process of updating their laws in this area, and the March 2023 events could influence the relevant decisions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".