Operationalizing Data Quality: The next frontier in regulatory reporting
Regulatory reporting continues to evolve. Firms have invested heavily in EMIR Refit, CFTC/CSA Rewrite, JFSA/HKMA/ASIC Rewrite and other global reporting regimes. Now in Europe, ESMA’s comprehensive approach to simplifying financial transaction reporting signals that further change is coming, reinforcing the need for firms to modernize their reporting infrastructure rather than simply react to new rules.
Too often, data quality is treated as an after-the-fact exercise. Validation rules identify errors, operations teams investigate exceptions, and corrections are submitted to regulators. While necessary, this reactive approach does little to address the root cause.
The industry’s focus now needs to shift towards operationalizing data quality.
That starts with reviewing the underlying data model. As reporting requirements converge and evolve, firms should ensure their source data, ownership, lineage and mapping logic remain fit for purpose. Investing in a scalable data model today will enable organizations to accommodate future regulatory changes at lower cost, with minimal disruption and without compromising data quality.
Technology has a critical role to play. A robust control framework should combine preventative controls before submission with detective controls after reporting, enabling firms to identify anomalies early, monitor data quality trends and demonstrate effective governance. Automated, technology-backed controls deliver consistent oversight while reducing operational risk and manual effort.
Artificial intelligence also has an important place but only when applied responsibly. Justifiable and traceable AI can help identify patterns, prioritize exceptions and accelerate root-cause analysis, provided its outputs are transparent, explainable and subject to appropriate oversight. AI should strengthen expert judgement, not replace it.
The benefits extend well beyond regulatory compliance. High-quality data improves straight-through processing, reduces reconciliation breaks, lowers operational costs and provides greater confidence in risk and business decisions. This is without mentioning lowering cost-of-change.
As supervisory expectations continue to mature, firms that operationalize data quality through resilient data models, embedded controls and responsible AI will not only improve reporting outcomes but also establish a future-ready reporting operating model capable of adapting efficiently to the next wave of regulatory change.
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