How can traditional operating model design cope with the move to T+1?

By James Maxfield, Head of Product and Solutions

In this article we’ll look at the traditional operating model design most capital markets firms use and the changes needed to meet the pressures of T+1.

Next time we’ll look and the problems with, and alternatives to, batch processing, then the benefits of switching to a data-driven operating model.

There are just a few months left until the move to T+1 slices a day off US trade settlement cycles. Most firms are still scoping out the requirements of the move, scheduled for 28 May 2024, ready to identify where they can make time savings.

The challenge of T+1 goes beyond just a lack of resources. According to Robert Cavallo, Director, DTCC Clearance and Settlement Product Management, “When the industry moved to T+2 in 2017, many firms needed to add resources to support the transition. However, the move to T+1 is fundamentally different — and technologically and behavioural changes must be the goal to ensure success.”

Firms need to think about how they operate and where they can find time savings. The traditional operating model is heavily focussed on straight-through processing (STP), with operations teams aligned around resolving the exceptions that come out of this process.

Let’s look at the technology and processes firms use to identify and resolve their exceptions, why this is a challenge in a T+1 environment, and how you can adapt to better meet the requirements of shorter settlement cycles.

Operating models are built around exception management 

Traditional operating model design makes financial firms reactive in nature. Teams pick up process fails and then look to fix them within market deadlines. In other words, post-trade is built upon the assumption you need to wait for issues to manifest and rely on manual intervention to address them.

For example, a trade breaks because the Standard Settlement Instructions (SSIs) don’t match. Someone then has to contact the counterparty to remediate this.

Naturally, firms aspire to having no exceptions. 100% straight-through-processing rates would allow them to have zero operations overheads. But the reality is that there will always be exceptions in the data and you’ll need the capacity to deal with that. So firms plan for a set amount of exceptions and resource for those.

This can become a problem where exception rates fluctuate due to processing issues, for example dropping from 95% to 85%, or when volumes increase for reasons such as market volatility or stress events. This adds to workloads, but the current time lag between trade date and settlement date gives post-trade teams some window to catch up.

That window of opportunity is disappearing, however.

Time is spent fixing breaks, not causes

The aim under a traditional operating model is to lower the number of exceptions by as much as possible. Lots of firms still run on legacy technology, though, which makes this goal difficult to achieve.

Exceptions are often caused by the inability of their systems and processes to adapt to changes in data or business needs – despite it being one of the top financial data management challenges.

The way in which capital markets firms merge their tech stacks when acquiring each other is another common cause of exceptions. Firms typically want to integrate acquired businesses quickly and get them operational, rather than rearchitecting to simplify their processes.

And there’s the organisational history to consider. The priorities of capital markets firms have changed over the years; processes and technology designed to deliver on the goals of a decade ago don’t necessarily align with current objectives.

Regardless of the source of exceptions, firms are often left tinkering, conducting little bits of re-engineering or process optimising around the edges. Their core processes – often reliant on hard-coded systems and schemas – remain untouchable. Teams don’t have the time or resources to find and fix the causes of data quality issues.

There are many problems with this approach, but if firms manage to keep their exception rates low enough, it isn’t disastrous (until a big change comes along or they need to explain their processes to auditors). However, firms running on legacy technology are often reliant on batch driven processing. This means it can take a day or more to create the exceptions in the first place.

T+1 removes that day. Firms will need to think differently around how they manage exceptions and resolve them, given the shortened window of time.

How firms can respond to T+1

If your operating model remains exception-driven then you need to respond to shrinking settlement timeframes by discovering exceptions much faster.

Lots of customers are telling us that this is the focus of their T+1 preparations. The quicker the exceptions are created, the sooner they can begin the resolution process. Having a clear view of exceptions more quickly helps them to manage trade and economic risk.

Ideally, you want to get from data to exception on T+0. Exceptions and risks compound if left to carry over to the following day, increasing the workload.

This is already a common scenario for post-trade teams: surging volumes create backlogs that can take the team days to clear. The typical response to known volume surges, like index rebalances and option expiries, is often to work late and temporarily reschedule workloads. This won’t work on an ongoing basis.

Traditional operating models will need to consider how they capture exceptions more quickly.

Accelerating exceptions discovery with T+0 reconciliation

Many capital markets firms rely on offshore teams to handle reconciliations. They are typically reliant on batch driven processes to create exceptions overnight, where a ‘follow the sun’ model provides the exceptions that middle and back office teams can start to work on when they arrive in the morning.

This won’t work for T+1. Risk of failure is significant if you haven’t affirmed your trades by settlement day. And that’s not including the fact you’ll likely also have carried over exceptions from the previous day.

Summary

The reality is that most firms have a traditional, exceptions-driven operating model. Making this work with shorter settlement cycles means changing the behaviours and processes around exception creation and management. This allows you to spot issues faster to better manage risk and prevent a backlog.

Finding exceptions faster means reconciling on trade date, or moving traditional post-trade processes to effectively become pre-trade. That will require big changes to the way that most financial firms set up and run their reconciliations and manage data integrity challenges. In particular, the industry needs to move away from a reliance on overnight batch processes. We’ll take a look at that in our next T+1 article.

Interested in evolving your ways of working to set your organisation up for T+1 and beyond? See what our CEO Christian Nentwich has to say on the role data plays in operating model change.