At the PostTrade 360° conference in Stockholm, discussions repeatedly reinforced one theme: T+1 is not simply a settlement compression exercise. It is a test of data quality, process automation, and operational resilience.
Contributed by Mireille Dyrberg, CEO, Xceptor
What became clear was the growing gap between institutions actively modernising their post-trade operations and those still relying on manual workarounds and disconnected data. These inefficiencies may be manageable under T+2. Under T+1, they become far more visible, increasing the likelihood of settlement failures.
The reassuring part: bridging that gap doesn’t require firms to rip out core infrastructure. It starts with trusting the data already flowing through it.
Industry readiness check
On a panel discussing allocations and confirmations, we asked the audience, “How well prepared is your firm for the December deadline?”
The results were eye-opening. 6% said they are fully prepared, 39% said they are partially ready, 33% said they have a lot of work to do, and 22% said they expect to miss the deadline and are aiming for October 2027 instead.
That’s nearly a quarter of the room already planning around a missed deadline. It mirrors what we are seeing across the market, with huge differences between market participants that are already fully compliant and those still trying to finalise their plans.
The next question was, “What is your biggest challenge in preparing for the allocations and confirmations deadline?”
Buy-side client readiness was the most cited challenge, followed by technology readiness, data quality, internal preparation, counterparty engagement, and operational processes.
As with many regulatory deadlines, we often see box-ticking and last-minute efforts to meet the minimum compliance requirements, without addressing the underlying inefficiencies that made the transition difficult in the first place. Many of the challenges cited above are a symptom of exactly that.
Operational challenges exposed by T+1
Many financial institutions operate across multiple systems, business lines, and asset classes. Data often exists in different formats and moves through fragmented workflows.
Gil Cross, Xceptor’s head of Product, Post-Trade, noted during a separate discussion, “Many firms are still relying on operational workarounds developed to accommodate product variations or client requirements, creating significant operational complexity over time.”
T+1 removes the buffer previously used to manage these challenges. Applying the same approach used yesterday, at twice the pace, is rarely an effective strategy.
A failed settlement is usually a symptom of something breaking earlier in the chain: inconsistent reference data, poorly integrated systems, incomplete allocations, or slow exception handling. With much less time between trade execution and settlement, identifying and resolving the issue becomes significantly harder.
Inconsistency is also an industry-wide problem. Counterparties, custodians, brokers, and buy-side clients can all operate differently, using different document formats, terminologies, and processes. That chain is only as efficient as the most manual step, and as the industry moves to near 24/7 operations, the complexity continues to grow.
Imposing standards for how confirmations and allocations are processed is not the right answer either. Forcing wholesale replacement of core infrastructure can be slow, costly, and operationally risky, and should not be necessary when more effective approaches exist.
Data matters even more under T+1
When processes break down and operational bottlenecks slow settlement cycles, it usually comes back to one point: data. Institutions must be able to trust the data flowing through their operations, and that is what makes automation – without increasing errors – possible in the first place.
Rather than replacing existing infrastructure, the better approach is to adopt an interpretative layer that sits across systems, transforming data regardless of where it comes from or how it arrives.
For example, Xceptor’s Data Automation Platform integrates with an institution’s existing internal systems, third parties such as data vendors and market infrastructure providers, and any communication channel. Using agentic AI combined with multi-step validation controls and human oversight, data is automatically extracted and standardised into the format firms need, regardless of its source or whether it arrives in a PDF, spreadsheet, email, or structured message. As a result, downstream processes can rely on trusted data without disrupting existing infrastructure.
Consistent data also creates greater visibility into operational performance. Firms can measure processing times, identify exceptions, understand common causes of settlement failures, and make more informed decisions about where to improve processes for greater efficiency and resilience.
As I noted during my panel discussion, “It all comes back to data, data, data.” Whether preparing for T+1, implementing AI, or tackling operational inefficiencies, success depends on the quality, availability, and usability of that data.
AI is essential, but so is control
As trading volumes grow and settlement cycles shrink, AI becomes a necessity. Firms cannot keep scaling operations by simply adding more people.
Almost everyone today is using large language models (LLMs) to help summarise information, draft content, search documents, or accelerate routine tasks. While this may provide some efficiency gains, the capital markets industry requires high levels of transparency, auditability, and confidence in outcomes. A general-purpose AI model operating without controls is unlikely to meet those standards.
The greatest value comes when AI is applied to real-world operational use cases, combined with business logic, governance frameworks, and human oversight.
Gil Cross explained, “Rather than allowing AI agents to execute entire processes independently, AI can oversee or enhance structured, rules-based workflows. Deterministic processes follow a predefined path and can be trusted and audited more easily, while AI helps manage variance and complexity around those processes. This provides the flexibility and intelligence of AI while keeping the control and predictability required in highly regulated environments.”
That’s where AI delivers the most value: processing large volumes of unstructured data from confirmations and allocations, triaging exceptions, and predicting failures before they reach settlement. With confidence scores that determine how and when AI acts, decision paths and full audit trails that provide full visibility, humans stay in control and gain more time to apply judgement.
Beyond compliance: post-trade operations that scale
Treating T+1 as a standalone compliance project misses the bigger picture.
Going back to that first stat: 22% of participants in the room already expect to miss the December 2026 deadline and are targeting October 2027 instead. Closing that gap will come from fixing the data and processes flowing through operations. Firms that invest in trusted data, intelligent automation, and controlled AI now will be better positioned for both T+1 and whatever comes next.
Xceptor Post-Trade Operations makes complex operations simple to run, and trades simple to settle. By bringing together data orchestration, workflow automation, AI, and governance, we help firms handle more trades, more counterparties, and more complexity while maintaining control, transparency, and operational resilience.
Every asset class. Every format. Every deadline. Handled.
