ESMA’s 2025 Report on the Quality and Use of Data: A Proactive Approach to Reporting Quality

Key Takeaways

• ESMA’s 2025 report shows regulatory reporting data is now a core supervisory asset, used for oversight, surveillance and policymaking.
• The report points to better data quality tools and shared platforms, but uneven firm readiness and rising enforcement remain key concerns.
• DTCC’s Trade Reporting Analytics can help firms identify reporting issues earlier, improve data quality and prioritize remediation as regulatory scrutiny increases..

The 2025 edition of ESMA’s Report on the Quality and Use of Data was recently released, confirming that regulatory reporting data is now central to supervision, risk monitoring, market oversight and policymaking across Europe. The report points toward deeper data use, shared infrastructure, stricter quality expectations and an effort to achieve these goals with streamlined reporting requirements.

ESMA’s report demonstrates that they are using reported data more actively, reusing it more widely, and applying more advanced analytical tools – including AI – to improve oversight.

For reporting firms, this places an increased emphasis on complete, accurate, timely and consistent data that can withstand regulatory analysis, not just pass submission validation. Read below for five themes from the report that may shape firms’ regulatory data quality strategies:

1. Regulatory data is being reused as a supervisory asset.
   • Key Findings:
     ESMA confirms that regulatory data is embedded in supervision, stress monitoring, compliance checks and policymaking. The report also highlights simplification, burden reduction and “report once” concepts, signaling the potential for greater reuse of existing datasets across supervisory purposes.
   • Why This Matters:
     Firms should assume reported data is actively analyzed, reused and compared. As regulators like ESMA rely more on existing submissions, inconsistencies across regimes, jurisdictions and lifecycle events become more visible and harder to defend.
2. Valuation, collateral and timeliness issues are increasingly visible.
   • Key Findings: ESMA highlights EMIR data as a core input for oversight, stress monitoring, compliance checks and crisis response. This makes timely valuation, collateral and lifecycle reporting critical to how authorities assess risk, exposures and market conditions. Poor-quality or stale data can weaken supervisory analysis and reduce confidence in reported positions.
   • Why This Matters: Firms should actively monitor missing, stale or late valuations, incomplete collateral details, late margin updates and delayed lifecycle events. These issues may not always prevent submission, but they can distort risk views and become visible through supervisory dashboards, data quality indicators or NCA engagement.
3. Pairing, matching and lifecycle reconciliation remain key quality measures.
   • Key Findings: ESMA notes that SFTR data quality improved in 2025, particularly in lifecycle reconciliation, but matching challenges persist. Pairing and matching are essential because authorities rely on reconciled data to monitor secured funding markets, interconnectedness, stress events and potential systemic risks.
   • Why This Matters: Firms should monitor unpaired and unmatched records, investigate lifecycle reconciliation breaks and identify systemic counterparty patterns. Persistent breaks reduce confidence in reported data, limit supervisory usability and may indicate deeper issues in booking, counterparty communication, lifecycle processing or reporting controls.
4. Reference data and identifier consistency are becoming harder to ignore.
   • Key Findings: ESMA continues to emphasize accuracy, completeness, consistency and timeliness across regulatory datasets. As data is reused across supervisory purposes, reference data quality becomes more important, including product classification, identifiers, counterparty attributes and consistency of reported values across firms and regimes.
   • Why This Matters: Firms should monitor for inconsistent treatment of CFI, UPI, LEI, UTI, counterparty nature and corporate sector values. Reference data issues can affect pairing, matching, classification and regulatory interpretation, while inconsistencies that appear minor internally may become more visible when supervisors compare data across firms, datasets and jurisdictions.
5. AI and advanced analytics are raising the bar for data quality detection.
   • Key Findings: ESMA highlights growing use of automation, shared supervisory tools, SupTech collaboration and generative AI, including operational use cases supporting supervisory analysis and market abuse detection. This signals a shift toward faster, more scalable detection of reporting anomalies and data quality patterns.
   • Why This Matters: As supervisory analytics become more advanced, firms need stronger capabilities to interpret regulatory findings, identify anomalies and prioritize remediation. AI and machine learning can help summarize complex regulatory signals, detect abnormal reporting patterns and support earlier intervention before issues escalate.

How DTCC Can Help

DTCC’s Trade Reporting Analytics helps firms respond to evolving expectations by turning regulatory reporting data into actionable insight. As ESMA and other NCAs make greater use of reported data for supervision, monitoring and analysis, firms need to identify data quality issues before they become visible through regulatory review. Trade Reporting Analytics helps firms monitor for issues such as missing or stale valuations, late margin updates, unpaired or unmatched trades, duplicate identifiers, inconsistent reference data, abnormal values and cross-jurisdiction reporting differences.

As regulators increasingly reuse transaction data and apply more advanced analytics, firms also need a clearer view of whether their reported data is complete, consistent and fit for supervisory use. Trade Reporting Analytics supports that objective by helping firms surface potential issues earlier and benchmark their reporting quality against their peers to help prioritize remediation across regimes and jurisdictions.

Looking ahead, Trade Reporting Analytics’ analytics-led capabilities, including reconciliation insights, anomaly detection and peer-relative reports can further help firms interpret supervisory priorities, identify emerging reporting patterns and focus remediation where it matters most.