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ESMA’s Data Quality Report Signals a Higher Bar for Regulatory Reporting Data

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By Michele Hillery, Managing Director, Head of Repository & Derivatives Services at The Depository Trust and Clearing Corporation (DTCC).

Regulators across jurisdictions are leveraging trade reporting data as a supervisory resource, using it to monitor risk, assess market activity and inform policy and oversight decisions. As this use becomes more sophisticated, firms face an even stronger imperative to ensure their data is accurate, consistent, reliable and supports supervisory analysis, rather than only meeting submission requirements

The 2025 edition of ESMA’s Report on the Quality and Use of Data makes that shift unmistakable. It shows that regulatory reporting data has become a core supervisory asset for market oversight, stress monitoring, compliance checks and policymaking, supported by shared tools, automation, and advanced analytics. Just one year earlier, ESMA’s 2024 report also underscored the growing use of transaction data for monitoring, supervision, enforcement and policymaking, while reinforcing the need for stronger governance and quality benchmarking. It showed that progress is possible, with EMIR rejection rates improving after REFIT and legacy trade upgrades showing notable improvement. Still, persistent gaps across indicators and regimes made clear that opportunities remain. The 2025 report points to stronger tools and shared platforms, but disparities across firm readiness and rising enforcement remain key concerns.

To advance in this area, participants need a tool that enables firms to continue to improve trade and transaction data before it is reported to regulators, with a key focus on accuracy, completeness, and compliance with regulatory mandates.

A Higher Bar for Reporting Data

ESMA’s 2025 report highlights simplification, burden reduction and “report once” concepts, signalling a push toward greater reuse of existing datasets across supervisory organizations. For reporting firms, submitted data should be viewed as something regulators actively analyse and compare, not something that disappears after filing. At the same time, automation, analytics, and AI-driven use cases are moving supervisory capabilities toward faster, more scalable detection of reporting anomalies and market abuse patterns.

As regulators rely more heavily on existing submissions, inconsistencies across regimes, jurisdictions and lifecycle events become easier to detect and harder to defend. The consequences are also becoming more material due to poor data quality triggering heightened scrutiny, escalation and potential financial or capital impacts. Firms must move beyond simply meeting reporting requirements and ensure the data they produce, and share can withstand regulatory scrutiny and support internal risk management.

Proactive Reporting Oversight

The 2025 report also highlights progress in data quality across several regimes, including improved EMIR reporting following REFIT and stronger lifecycle reconciliation in SFTR. Critical weaknesses also remains, particularly around valuations, collateral, timeliness, pairing, matching and reference data consistency. In SFTR, ESMA has continued to stress that there are still large margins for improvement, even as quality frameworks and indicators become more formalised. These are not minor technical inefficiencies; they directly affect how authorities assess exposures, market conditions and reporting reliability.

Firms should actively monitor missing or stale valuations, incomplete collateral details, late margin updates, delayed lifecycle events, unpaired or unmatched records, duplicate identifiers, and inconsistent reference data. These remain clear pain points because they can distort supervisory risk views even when submissions are accepted. As ESMA makes methodologies and indicators more transparent, internal benchmarking is becoming essential to identify outliers, compare performance across reporting streams, and show disciplined remediation.

Persistent discrepancies in pairing, matching, lifecycle reconciliation and reference data can also reduce confidence in reported positions and point to deeper issues in booking, counterparty communication, processing or reporting controls. As regulatory use of reported data expands, firms will need a more proactive approach across asset classes, regimes and jurisdictions.

Detection Is Accelerating

ESMA’s report highlights growing use of automation, common supervisory tools, supervisory technology collaboration, and generative AI. Together, these developments signal rising expectations for how quickly and effectively data quality issues can be detected, as well as whether the data is useful for regulatory monitoring and oversight.

A reporting data analytics platform can help firms turn regulatory reporting data into actionable insight. It can support monitoring for missing or stale valuations, late margin updates, unpaired or unmatched trades, duplicate identifiers, inconsistent reference data, abnormal values and cross-jurisdiction differences, all while providing benchmarking and near-real-time visibility into remediation priorities. As regulators benchmark firms against peers, firms need similar internal capabilities to identify outliers and assess whether their reporting is complete, consistent and fit for supervisory use.

Looking ahead, analytics-led capabilities such as reconciliation insights, anomaly detection and peer-relative reporting can help firms interpret supervisory priorities and identify emerging patterns. The implications will vary across the market. Sell-side institutions face scalability and cross-regime consistency challenges, while buy-side firms, particularly those that delegate reporting, must strengthen oversight because accountability for the data remains with them. While ESMA is a clear signal of where supervision is heading, the broader trend is global.

Firms should treat reporting quality as an ongoing governance priority rather than a point-in-time trade submission exercise. Only then will firms and regulators have access to data to inform decision making and identify key risks.

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