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 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".