Lessons from T+1 settlement: Risk mitigation and future implications
Bibliographic record
Abstract
The transition to a T+1 settlement cycle marks a significant milestone in the evolution of global securities markets. This paper examines the key operational, technological and strategic challenges encountered during implementing T+1 and the lessons learned by early adopters, particularly in the US and Canada. The accelerated settlement cycle, designed to reduce counterparty risk and enhance market efficiency, demands a fundamental rethinking of post-trade workflows, requiring companies to adopt automation, streamline processes and foster cross-industry collaboration. The paper also highlights the implications of compressed timelines for cross-border transactions and the critical role of regulatory frameworks in ensuring a smooth transition. In addition, the analysis explores the readiness of other jurisdictions, including the UK and Europe, which are planning transitions to T+1 by late 2027. Drawing on real-world examples and industry insights, this paper provides actionable guidance for companies navigating T+1 while considering the broader implications for future settlement cycles such as T+0. Additionally, the paper assesses how the takeaways from T+1 can shape the transition to T+0, particularly in automation, liquidity management and risk mitigation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks. com/business/.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".