Settlement fails are often seen as the cost of doing business; inevitably they will happen and be absorbed. The T+2 settlement window provides at least an entire working day to solve issues but as Europe moves to T+1, operation leaders face a sharper challenge. In a compressed settlement cycle, the firms best prepared will be those that can distinguish real settlement risk from operational noise and focus capacity where the financial or operational impact is greatest.
Contributed by Steve Cox, PostTrade Product Lead, Meritsoft, a Cognizant company
This requires a move away from reactive exception management towards more data-led, predictive fail prevention. Fails incur costs through penalties under the central securities depositories regulation (CSDR) and interest claims but can also have a knock-on effect for future trades that are dependent on that trade completing, which in turn may necessitate borrowing stock.
Under a shorter settlement cycle, the priority is no longer simply spotting every potential fail but identifying where the genuine risks are. Firms that can combine cleaner settlement data, historical counterparty behaviour, predictive risk scoring, and automation will be better placed to prevent fails, reduce penalties, and create a more efficient post-trade operating model.
The curse of fragmented data and legacy systems
Solving fails isn’t always easy because relevant data arrives from multiple sources at different times. Custodian banks, CSDs, counterparties, CSDR data, and SWIFT messaging all provide signals, but not always in a consistent or timely way. Matching becomes especially critical under T+1, where confirmation and affirmation need to happen on trade date. A trade is at risk of failing if the standing settlement instructions (SSIs) don’t match, or more commonly, if one side has yet to send them. But knowing when the risk is real will be key.
Legacy systems and processes make this harder. Non-auditable email-based investigation, fragmented status updates, and infrastructure that cannot easily consume new data fields all limit the speed and accuracy of exception management. The industry has created solutions such as Swift UTI for trade identification while partial settlement through a vendor platform can increase fills. Both, however, require firms to retool systems to recognise new data fields or accept half-completed orders.
A centralised data layer, supported by modern standards and architecture, gives firms the foundation to move from manual investigation to analytical and predictive settlement operations enabling them to fix trade breaks before they spiral.
Predictive prioritisation and false alarms
No two counterparties trade the same way. One counterparty may send settlement instructions hours ahead of the deadline, while another may routinely submit them much later. Without context, both trades may look equally concerning but not every missing or delayed instruction represents the same level of risk. Understanding counterparty behavioural patterns, operations teams can assess which exception is genuinely unusual and prioritise the one with a higher likelihood of failing.
This type of analysis can become predictive. By comparing live trade status with counterparty behaviour, previous matching performance and historical fail patterns, firms can separate everyday operational noise from trades that are genuinely at risk. The objective is not to flag more exceptions but to identify the highest risk ones earlier in the settlement process. Timely intervention, coupled with integrated communications that enable proactive counterparty engagement, enables rapid issue resolution and a reduction in fails.
The prediction doesn’t need to be a complex series of probabilities but can be simplified to a traffic-light system that allows firms to triage by risk and cost. With confirmation and affirmation moving to trade date under T+1, operation teams need to be able to tell at a glance where the real trouble spots are. For example, a late settlement instruction from a counterparty that normally submits early could be flagged as red, while a similar delay from a counterparty that routinely instructs later in the day may remain amber. This can further be combined with CSDR penalty estimates, allowing firms to focus on the high-risk trades that are the most likely to fail and the most expensive to leave unresolved.
Firms are not only able to prioritise the right trades, but these metrics can also inform front office decision making. If the operation teams find that one counterparty’s trades are at higher risk for fails, that information could be passed on to the execution desk or allow for a frank discussion with the counterparty.
Automation and front-office value
Better data enables more informed decisions but also makes automation easier. The information needed to compare settlement instructions is the same data needed to fill out fields in straight through processing (STP).
By identifying the high-risk trades, firms can more easily automate the low-risk ones. It keeps trades that are mundane off the radar, allowing operations team to confidently focus on the real risks.
Industry readiness remains uneven, with many firms still working to automate settlement instruction fully. For T+1, partial automation will not be enough. Automation and STP can only scale if settlement instructions are properly matched and supported by reliable status data.
That same data is increasingly being integrated into order management systems, allowing traders to make more informed decisions about execution, counterparty selection, and cash deployment. Take, for example, hold and release statuses from custodian banks. If front office traders can quickly see a security is unlikely to be released on time, they can redeploy that cash for other trades.
As firms explore more AI use cases in post trade, a normalised data layer will serve as a springboard when training and developing AI tools.
From reactive cost to strategic advantage
The US transition has shown that managing T+1 reactively can keep fail rates under control, but often at the cost of additional headcount. As Europe prepares for its own transition, settlement efficiency will depend less on how many exceptions firms can see and more on how accurately they can prioritise them. By bringing together cleaner data, predictive analytics, practical risk scoring, and rapid issue resolution, firms can reduce penalty exposure, ease pressure on operational headcount, and make better use of liquidity.
Ultimately, the firms that succeed in the T+1 transition and beyond will be those that can act with speed, precision, and confidence. By separating meaningful risk from false alarms, firms can prevent more fails before they happen, reduce the cost of exception management, and strengthen the link between post-trade efficiency and front-office performance.
