Data Quality in Transaction Reporting: Why Under-Reporting, Misreporting & Over-Reporting Still Persist?
- Arjun Thirukonda
- May 19
- 4 min read
This is the fifth entry in our ongoing RegEdge blog series on trade and transaction reporting. In our previous posts, we explored:
Here, we focus on a persistent, foundational challenge across reporting regimes: data quality. We'll examine why it remains a regulatory pressure point, especially in light of ESMA’s latest 2024 Data Quality Report.
Why Data Quality Matters More Than Ever?
ESMA’s 2024 Report on the Quality and Use of Data reaffirms that data is not just a compliance output. It's central to regulatory supervision, systemic risk detection, policy calibration, and market transparency. ESMA’s use of transaction data to replace parallel reporting flows for volume cap and transparency calculations under MiFIR signals that high-quality, multi-use data is the future. Simultaneously, ESMA is deploying increasingly sophisticated Data Quality Engagement Frameworks (DQEFs), internal dashboards, and risk alerts to monitor the completeness, accuracy, and timeliness of reporting.
For firms, poor data quality is no longer a silent inefficiency. It now has direct consequences, including enforcement actions, fines, and reputational damage. The margin for error is shrinking as regulators increasingly rely on data-driven policy, surveillance, and supervision.
Why Do Data Quality Challenges Persist?
To understand these persistent issues, we've grouped the core challenges into four categories. This structure helps firms identify relevant themes and areas for targeted action.
A. Structural and Systemic Complexity
Complex, Multi-Layered Reporting Requirements: Transaction reporting obligations span hundreds of fields across multiple regulations, each with slight variations. Trade lifecycle events may require multiple reports, depending on execution methods, booking models, and asset class nuances. Regulatory divergence, such as that between ESMA and the FCA post-Brexit, adds interpretation risk.
Fragmented System Architecture: Firms often rely on fragmented connections of legacy platforms for applications and reference data, in-house tools, and vendor solutions. Inconsistent data mapping and a lack of system interoperability lead to:
Duplicates from multi-system booking
Missing timestamps or identifiers
The inability to trace the full trade lifecycle across systems
Siloed Regulatory Approaches: Many firms still manage each regulation (e.g., EMIR, MiFIR, SFTR) independently. This results in duplicated processes, inconsistent reporting logic, and missed opportunities to consolidate controls, undermining ESMA’s push for a unified, multi-use data model.
B. Governance and Interpretation Gaps
Shifting Regulatory Interpretations:
Regulatory Q&As, RTS/ITS updates, and enforcement actions can change expectations mid-cycle.
Fields may shift from optional to mandatory, or their usage may be redefined.
Firms that treat implementation as a one-time effort risk falling behind.
Poor Data Governance and Ownership: Reporting often lacks clear data owners across front-office, operations, and compliance. Common symptoms include:
Inconsistent population of key fields (e.g., trader ID, execution venue)
The inability to trace data lineage
The absence of a process to resolve systemic issues
C. Operational and Control Weaknesses
Manual, Tactical Workarounds: Data gaps often lead to Excel-based fixes, manual enrichment, or email follow-ups. These increase operational risk and reduce auditability. In high-volume environments, even small error rates result in thousands of faulty records.
Inadequate Exception Management & Reconciliation: Many firms lack real-time validation against regulatory schemas. Exceptions are tracked in silos, often only post-submission. Reconciliations between internal and reported data are infrequent or ad hoc.
Data Model Mismatch Between Trade & Reporting: Booking models used by the business may not align with regulatory requirements. Common issues include:
Netting in systems versus gross reporting obligations
Allocation logic not mapped to end-client-level data
The reporting of off-book or non-standard trades
D. Third-Party, Market, and Regulatory Pressures
Vendor & Outsourcing Risks: Third-party vendors and utilities may create abstraction from raw data. Firms that rely entirely on "black-box" vendors lose control over data quality. Misaligned SLAs or logic gaps between vendor outputs and firm expectations can go undetected.
Emerging Product and Market Complexity: Crypto, digital assets, and new derivatives challenge existing reporting logic. Traditional reporting systems struggle to accommodate these products without significant rework.
Regulatory Scrutiny and Enforcement Trends: Regulators are increasingly focused on completeness, accuracy, and timeliness (as seen in CAT). The 2024 ESMA report highlights the growing reliance on data for policy reform, stress testing, supervisory convergence, and MiFIR volume cap replacement. The cost of non-compliance is rising, with:
Direct fines
Skilled person reviews (e.g., FCA Section 166)
Reputational damage
What Should Firms Do Now?
To respond effectively to these challenges, firms should take action across four key areas:
Strengthen Governance and Accountability
Establish cross-functional data ownership (business, technology, and compliance).
Improve lineage tracking and documentation.
Break down regulatory silos and encourage cross-regime collaboration.
Improve Controls and Monitoring
Build end-to-end control frameworks with real-time validation.
Implement robust exception and reconciliation processes.
Conduct regular diagnostic reviews against source data.
Future-Proof Architecture and Integration
Move toward unified, normalized data models.
Reduce reliance on tactical workarounds and vendor black boxes.
Design for the flexibility to support crypto and evolving products.
Build People Capability and Responsiveness
Provide targeted training across the trade lifecycle.
Establish proactive change management for regulatory updates.
Involve control functions early in the product lifecycle.
How RegEdge Can Help?
We work with investment firms, broker dealers, and asset managers in enhancing their reporting ecosystems through:
Targeted gap assessments and remediation
Design and implementation of target-state reporting frameworks
Independent assurance reviews of reporting accuracy and controls
Exception management tooling and governance model design
Advisory and implementation on moving toward harmonized data architectures
Whether you’re navigating MiFID, EMIR, or SFTR, our team delivers practical, control-oriented solutions rooted in regulatory experience.
Data quality isn’t just a regulatory risk—it’s a reputation risk. Let’s get it right before the regulator finds the errors.
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