Giles Elliott, head, business development, capital markets, TCS BaNCS, Tata Consultancy Services
There are layers and system features that resolve T+1 issues automatically and keep the shorter settlement process flowing.
T+1 has clearly arrived in the West, although the views on how this has impacted day-to-day processing will extend globally.
Whether industry firms have taken a tactical or strategic technological approach to covering this market change, the role of technology and the importance of having a highly modernized solution have never been greater.
So, how is technology playing a role? And what are the most important areas that we have seen come through in the transition to T+1 for market participants?
Firstly — the importance of real-time data has never been higher, and the technology and underlying features that drive this are critical.
At a headline level, firms require real-time processing systems that can run 24 by 7 across global markets.
With such narrow global processing windows, losing hours for batch cycles and end-of-day processing is unthinkable as an ongoing model.
However, a layer below this exists the system features that can resolve issues automatically and ensure that the data reflected is a real-time representation.
These features can cover the ability to configure workflow rules, standing instructions, configured tolerances, and data normalization/translation features to minimize exception queues.
There is also a priority for implementing real-time monitoring tools to track trade settlement statuses and detect any delays or discrepancies immediately after T+1, as well as automated reconciliation tools to compare trade details between counterparties, clearinghouses, and custodians to ensure consistency and accuracy in settlement instructions.
Sydney Mammen, solutions manager, TCS BaNCS, Tata Consultancy Services
Leading from this theme is a desire for extreme levels of end-to-end (E2E) automation — to reduce the challenges of operations capacity planning and daily spikes in capacity demands.
This addresses in part the points around the rules and configuration previously mentioned.
However, the biggest incremental step is likely to come from increased machine learning (ML) and the use of artificial intelligence (A.I.), both of which have a core requirement for a strong, real-time, and accurate baseline of data.
ML and A.I. also access historical data and have the capacity to store this, potentially through the cloud, without impacting day-to-day system processing capacity.
A.I. and Generative A.I. (GenA.I.) will evolve to play significant roles in automating matching processes for T+1 trades by enhancing efficiency, accuracy, and scalability, once there is a strong baseline foundation for real-time data.
Pattern Recognition tools can analyze historical trade data to identify common characteristics of successful matching.
A.I. tools can be programmed to make real-time decisions regarding matching based on predefined rules, risk parameters, and trade characteristics. In the same vein, predictive analytics techniques can play an important role in forecasting matching rates, processing times, and potential matching failures based on historical data and current market conditions.
Enhanced operations user tools have gained a new profile with the lead-up to T+1, and with workload prioritization a critical theme, the importance of risk-based dashboards and workflow prioritization tools has hit new levels due to the compression of processing windows.
The expectation is that these are highly configurable to address different scenarios and client preferences, and with dynamic analytical capabilities to assess a range of parameters that contribute to the assessment of risk.
Underlying this is the requirement to understand performance across the E2E processing model, understanding areas of delay and exceptional process resolutions.
Overlaying this is a louder industry voice calling for harmonization and adherence in market practice guidelines and firms are being asked to increasingly track whether the desired convergence is being seen.
The teams of operations performance analysts have reset the importance of these analytical engines and the demand for daily insights and this has extended the scope of technical solutions.
Quite clearly, we are seeing transformations with the financial market infrastructures (FMIs) and new automated solutions to deal with participant issues such as potential fails due to position shortages.
And, this is driving its own set of innovations, which, in turn, needs to be catered for within-participant systems.
Aligning to such development paths is raising the bar on market integrations and highlighting the benefits of working with partners that are highly committed to both sectors.
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