Published by Jessica Weisman-Pitts
Posted on December 21, 2021
5 min readLast updated: January 28, 2026
Banks must improve data management to meet regulatory demands, focusing on data quality and integration to reduce compliance costs.
By Martijn Groot, VP Strategy, Alveo
Responding to a report from the European Banking Authority (EBA) analysing the current RegTech landscape in the EU, Juniper Research’s lead analyst Nick Maynard, commented: “Ultimately, the EBA report highlights that regulatory frameworks can be harmonised to stimulate greater [regtech] use, but it identified problems such as data quality, integrating with legacy systems and awareness as the biggest issues.”
Banks and financial institutions generally still have much to do to ensure they are ready for the ongoing wave of regulatory activity impacting the industry. Post-trade regulatory reporting requirements have increased following a range of regulations including Dodd-Franck, EMIR, MiFID II and SFTR. This poses new reporting challenges but also creates complications in trade enablement with new demands on instrument and counterparty or client onboarding.
Confronted by multiple challenges
There are a raft of challenges that banks and financial services companies need to address here. Regulatory requirements stipulate that banks need to be able to identify and standardise the source and provenance for all data.
Demonstrating this data lineage can be highly complex – a lack of controls and transparency to data flows can mean that firms are unable to explain the origin of specific data points. This also obscures the actual business usage and value of data which leads to ineffective demand/ usage management and control of the overall cost base, content licensing, legal and regulatory perspective. There is also increased regulatory scrutiny on the data that goes into models with new data lineage requirements from the ECB’s TRIM initiative, for example.[1]
More generally, the number of data points to be reported on transactions has steadily increased. Apart from the increased granularity, especially since MiFID II regulators have become more prescriptive on what identifiers to use for instruments, legal entities and execution venues.
Data quality is critically important here also. Firms need complete visibility of their data, including how it has been processed and its lineage, so they can see immediately where it has come from and are able to assess whether it is fit for purpose. They must be able to drill down into the data in each process and see where quality is insufficient and remedial action is required. Poor data quality will undermine efforts to meet the growing range of financial services regulations in the market today.
When auditors or regulatory bodies ask questions or make significant inquiries as part of their supervisory role, firms need to provide answers that are not just accurate but are credible and convincing.
That’s especially the case given that regulators are increasingly scrutinising data quality, and in particular the quality of data that feeds into models. Financial services businesses will often need to explain the results, using not only the mathematics and economic rationale of the model itself but also the data that went into it, what the quality issues were, what the sources were and who touched it on the way.
Finding a solution
First and foremost, banks should look at implementing best practices in the collection, cross-referencing and integration of data, before moving on to look at data quality workflows, such as controlling and tracking proxies. Joined-up data may be the most significant shared aim of regulatory regimes – but it is yet to be adequately addressed.
A standard business domain model can help keep track of the market and reference data requirements firms face. Data integration needs to include all the main data providers and identification standards including key service providers.
No matter the specific regulation, organisations will need to source from different channels, but they will also need to document how they came to the information they have. That may also include estimating missing data fields through specific proxies or sectoral benchmarks.
Data standardisation includes mapping to a common format, interpolate and convert reporting bases and units of measurement to a standard. Tracking data lineage is required for complete transparency, distinguishing between data sources and also between company reported facts, third-party opinions or internal estimates.
A data management function includes integration with common data sets and reporting systems as well as a business-user friendly process to onboard, inspect and complete data sets. Data quality metrics act as a feedback loop to optimise sourcing and improve overall data quality.
Looking to the future, complying with ever-evolving regulations means that firms must increasingly respond to real-time and adhoc data requests. As a result, financial services companies will need to get required data to business users and consuming systems and to quickly refresh when needed. At the same time, they need to reduce costs and prevent the unnecessary acquisition of data or duplicated data. The cost of change, whether this is simply adding an instrument, refreshing fields of a security or changing a source should be substantially lower. The required solution should be capable to handle large volumes of requests yet precision-source new information.
Ultimately though, ensuring compliance with modern financial services regulation depends on applying sound principles of financial data management on an ongoing basis. It cannot be treated as a box ticking exercise but if firms source and integrate data well, document how they do that and maintain high levels of data quality and data lineage, they will be well placed to comply the regulatory reporting requirements both of the present and be agile enough to adapt quickly as tomorrow’s needs change too.
The article discusses how banks can use data management to lower costs associated with regulatory changes.
Banks face challenges with data quality, integration, and meeting regulatory requirements.
By implementing best practices in data collection, integration, and quality management.
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