Although CSDR is not short of critics, the penalty regime does seem to be doing its job, with members from the Association for Financial Markets in Europe reporting that settlement fail rates in the EU have halved since 2022, and are now broadly comparable with the US.
Other markets could perhaps learn from CSDR, or maybe even replicate it, as this could have a materially positive impact on settlement discipline. Should this happen, it is vital, however, that regulatory arbitrage be avoided at all costs.
Settlement completion rates could be improved even further if the industry makes a few adaptations to its technology processes across the middle and back office, especially ahead of T+1.
This can be achieved by following a few simple steps.
Through the introduction of STP and automation, and by only permitting manual interventions exclusively under exceptional case management, settlement completion rates will increase.
Enhancements to internal data quality , the possible creation of a common industry database, and allowing the right counterparty, product and transaction identification through wider use of Legal Entity Identifiers (LEIs), Unique Product Identifiers (UPIs) and Unique Transaction Identifiers (UTIs), will also mean more settlement efficiencies can be realised , making the T+1 transition that much easier.
At a CSD-level, settlement practices could benefit from additional market-wide harmonisation efforts, along with the provision of hold and release mechanisms and partial settlements. Some have suggested securities lending programmes should be offered by CSDs, adding that legislation or fiscal treatment must not impede or hamper their use for solving end of day delivery fails.
Equally, the same methods to estimate and assess settlement efficiency ought to be standardised and made publicly available, as should granular information about settlement fails themselves, e.g. at an asset class level, type of transaction, etc.
Leveraging disruptive technologies could also help drive down fail rates.
A handful of forward-looking providers are already using sophisticated data analytics to drill down and identify settlement failure patterns in the market. Elsewhere, artificial intelligence (AI) is being deployed to predict possible future settlement failures, by analysing , say, the International Securities Identification Number (ISIN), counterparty, transaction type, its size, the level of interest rates, market conditions, etc, and mapping out any noticeable trade fail trends.