Data Quality Is the Foundation of Post-Trade Automation

The next stage of post-trade automation will depend as much on data quality as on advances in technology. Artificial intelligence (AI) can help firms identify anomalies, resolve exceptions and streamline workflows, but these capabilities rely on accurate, consistent and well-governed data.

That theme emerged during our panel discussion at SS&C Deliver on the future of post-trade operations, including AI, automation and the path toward zero-defect operations. The discussion brought together perspectives from middle office delivery, fund services technology, a client using post-trade services and a data provider. Four live audience polls reinforced a common conclusion. Before firms can realize the full potential of automation and AI, they need to address the data issues that continue to drive operational exceptions.

What the Polls Reveal About Operational Priorities

The audience responses offered a clear picture of where firms see the greatest challenges and opportunities.

When asked about the biggest obstacle to zero-defect operations, 39% identified data quality, ahead of legacy platforms at 30% and process complexity at 24%. Data quality and governance also led when participants were asked where they would direct technology investment, receiving 57% of the vote. Process redesign followed at 27%, while platform modernization received 11% and AI and analytics received 5%.

The results suggest that firms see foundational improvements as a priority, even as interest in AI continues to grow. The poll on AI's potential value reinforced that view, with 63% of respondents selecting reconciliation and controls as the area where AI could deliver the greatest benefit.

The opening poll provided further context. Fifty-eight percent of respondents said their firms had automated processes but still dealt with many exceptions. Another 29% reported using AI in selected workflows, while 13% said their operations remained largely manual. No respondents said their firms were approaching self-resolving operations.

Together, the findings point to a gap between automating individual processes and creating operations that can consistently prevent, identify and resolve exceptions. Closing that gap requires attention to the quality and management of data across the post-trade lifecycle.

Data Quality Shapes the Entire Post-Trade Lifecycle

The panel discussion illustrated how relatively small data issues can create problems across multiple processes and organizations. A missing identifier, an incorrectly mapped product code or an inconsistent field convention can lead to reconciliation breaks, settlement delays, client inquiries and amended regulatory reports.

These problems often extend beyond the point where they originate. When counterparties maintain inconsistent data or information arrives at different times, exceptions can move through the workflow before their underlying causes are identified. Automating an individual step may improve processing speed without eliminating the data issues that create downstream work.

For clients, the consequences include inconsistent reporting, delayed responses and additional operational friction. These experiences also influence how clients evaluate service providers. Investment in data quality and governance helps providers deliver more consistent service and gives clients greater confidence in the information they receive.

Data quality also involves more than accuracy. Information must be available in the appropriate format and at the right time, with sufficient lineage to establish its origin and support confidence in its use. Managing these requirements across the data lifecycle is essential to making information useful for operational decisions.

Applying AI Where It Can Deliver Practical Value

The audience's selection of reconciliation and controls as the leading area for AI investment reflected the practical challenges facing operations teams. Reconciliation is labor-intensive, data quality issues become visible through breaks and mismatches, and errors can have consequences across the trade lifecycle.

AI can support these workflows through capabilities such as extracting structured information from unstructured trade instructions, identifying anomalies against historical patterns and grouping related exceptions for investigation. These applications can help teams identify problems earlier and focus their attention on issues that require human judgment.

The panel also emphasized the distinction between deploying AI in a specific workflow and using it to support operations at scale. A successful proof of concept does not necessarily translate into a production process that resolves exceptions consistently. Firms must consider how AI capabilities fit into existing workflows, how their outputs are validated and whether the underlying data is reliable enough to support the intended use.

Predictive capabilities may become more valuable as these foundations improve. However, firms need to establish reliable data management and effective controls before relying on AI to anticipate and resolve increasingly complex operational issues.

Sequencing Investment for Better Outcomes

The poll results highlighted a distinction between investing in new capabilities and establishing the conditions required to use them effectively. Although AI and analytics received just 5% of the investment vote, the result does not necessarily indicate a lack of interest in the technology. It suggests that respondents see data quality and governance as more immediate priorities.

Building on an unreliable data foundation can undermine the value of automation and AI. Inconsistent information can produce false alerts, obscure genuine exceptions and reduce confidence in automated decisions. Improving data quality, by contrast, can support existing processes while creating a stronger basis for future technology investment.

This requires coordination across data management, operational processes and technology. Firms need to understand where data issues originate, establish consistent governance practices and address problems earlier in the lifecycle. They can then apply automation and AI to specific workflows, measure the results and expand their use where the evidence supports doing so.

The objective is not to delay AI investment until every data issue has been eliminated. It is to prioritize the improvements that make AI more reliable and its operational benefits easier to demonstrate.

Moving Toward Self-Resolving Operations

Looking ahead to 2030, the panel considered which capabilities might become commonplace despite seeming groundbreaking today. One possibility was an exception queue that had largely disappeared from daily operations, not because exceptions no longer occurred, but because routine issues were resolved automatically and only unfamiliar or complex cases required human intervention.

Achieving that outcome will require more than faster processing. Systems must be able to identify problems, interpret relevant information, apply appropriate controls and determine when an issue needs further investigation. Reliable data is essential at each stage.

As automation advances, the role of operations professionals may shift away from manually working through routine exceptions toward investigating complex problems and advising clients. Realizing that potential will depend on how effectively firms address the underlying causes of operational breaks, rather than simply automating the steps involved in resolving them.

The discussion at SS&C Deliver underscored a practical priority for firms planning their next phase of post-trade transformation. AI and automation can expand what operations teams are able to accomplish, but the quality of their results depends on the information supporting them. Firms that combine sound data governance with targeted technology investment will be better positioned to reduce exceptions and move toward more resilient, increasingly automated operations.

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