The regulatory focus for transaction reporting has recently shifted. Regulators are no longer only interested in the timeliness of submissions, but are now increasingly concerned with the accuracy and integrity of the data submitted. Poor quality data now constitutes a critical compliance failure, as reflected in recent regulatory updates such as the European Market Infrastructure Regulation (EMIR) Refit.
The Regulatory Shift from Submission to Scrutiny
Recent regulatory developments like the EMIR Refit signal a noticeable shift toward data quality and integrity as a core pillar of compliance. The European Securities and Markets Authority (ESMA) and the Financial Conduct Authority (FCA) now rely on high-quality data for effective market surveillance and to prevent systemic risk. This reliance has made data quality a top priority, particularly within the context of market abuse monitoring; therefore, data integrity failures are considered to be severe. The vast datasets collected from transaction reporting under the Markets in Financial Instruments Regime (MiFIR) are a primary tool used by regulators to detect potential market abuse. Incomplete or inaccurate reports can corrupt these datasets, which could undermine the regulator’s ability to prevent systemic risks. These patterns, as defined by the Market Abuse Regulation (MAR), mean that data quality and integrity are now a matter of critical importance for firms.
The Importance of Reconciliation in Data Quality
As the regulatory demands on data integrity increase, the focus shifts to data reconciliation as a key function for ensuring accuracy. Data reconciliation is the primary way firms can verify that their data matches the data received from counterparties and processed by trade repositories (TRs). An effective reconciliation framework will also involve internal reconciliation before submission. These end-to-end checks will confirm that a report is valid and also that it is fully accurate.
What is the Impact of Poor Data Quality?
The consequences of poor data quality can extend beyond the internal risks of the firm, contributing to broader market uncertainty. Inaccurate information directly affects regulatory oversight and market surveillance, which could impact the identification of key risks and potential market abuses. From a compliance perspective, firms that routinely fall short of data quality expectations face both financial and non-financial penalties, which can add to the compliance burden, increase operational costs and cause significant reputational damage to the firm. Poor data quality can also impact the future of the firm if long-term business decisions are made based on inaccurate risk models. Data quality in transaction reporting should be treated as a critical risk management function and not simply a compliance obligation. Firms must focus on several key areas to ensure data integrity, including establishing robust data governance with clear ownership, investing in RegTech for automated reconciliations and implementing KPIs for continuous monitoring and reporting of data accuracy. Firms that prioritise these investments and embed data integrity into their operations will be best placed to convert a regulatory obligation into a strategic advantage. Poor data quality exposes firms to compliance failures and reputational risk.
Speak to our experts today to strengthen your governance, automate reconciliations, and protect your reporting integrity.
