Regulation: Hurdle or Driver for Data Analytics in Financial Services

In the aftermath of the 2008 financial crash, service providers have been subject to increasing rules & requirements. To what extent has this climate held back advances in data analytics?

Simon Axon
Simon Axon
9 février 2023 3 min de lecture
Regulation and Data analytics in financial services

Financial services is certainly one of the most highly regulated sectors. In the aftermath of the 2008 financial crash in particular, service providers have been subject to ever increasing rules and requirements designed to better protect customers and reduce risk. Keeping up to date with these and delivering timely reporting to regulators are significant aspects of day-to-day operations across the sector. 

But to what extent has this climate of regulation held back advances in the use of data analytics? Our conversations with customers as well as our recent research undertaken with Qorus, suggest that different organisations take different routes and come to different conclusions about the impact of regulation. For some it has been a barrier, but for others a driver. For all, robust consistent data models provide solid foundations.

In our survey of chief data officers in leading banks around the world, results were evenly split between those who saw regulation as a limiter to the use of data analytics and those who saw it as a driver. Forty percent reported that prioritization of investment in data analytics were regulation driven. In one-to-one interviews many acknowledged the dual impact of regulation. This balanced opinion is typified in the response of one CDO:

“Financial Services is highly regulated, and our use of data and AI has to have very robust governance and operational controls embedded within. This element may inhibit [the sector] in some of the most advanced, cutting-edge use cases that other industries might explore. However, when it comes to implementing robust and fair AI processes, finance will be a leader as we have so much historical strength in compliance processes.” 

So, regulation does put hurdles in the path of those looking to develop and implement data analytics at the heart of their businesses, but clearing those hurdles can lead to better, stronger analytics. One of the principal areas of friction reported by many of our customers is the need to keep on top of a myriad of regulations that seek to achieve broadly consistent outcomes but with specific requirements that differ slightly jurisdiction by jurisdiction and are subject to ongoing change. 

As a digital transformation and data manager at a European bank commented, there are: “Continuously increasing scope and changing priorities…Small deviations between regulations, not only between countries but also between domains such as Finance and Risk.”  Even minor changes to what and how different data must me reported can have repercussions that lead to increased complexity, time and resource requirements. 

The growing market in ‘regtech’ tools offers a range of fast and efficient methods to deploy data analytics to assist regulatory reporting and other aspects of the regulatory process. However, these tools are only as good as the data that feeds them. Siloed approaches to building data analytic models will not only create significant overheads but run the risk of building ‘pipeline jungles’ that in themselves become regulatory and operational risks.

Leading financial services firms have avoided these problems by creating consistent data models that support repeatable and auditable processes. They map required data from across internal sources and integrating it into reusable features that support multiple reports. In turn, this enables automation that can significantly reduce the regular overhead and free high-value staff to work on other tasks. 

Across industry sectors many customers faced with regulatory reporting requirements are leveraging their investment in Teradata as their core transactional system of record as the perfect platform to support these regtech tools. Rather than creating new, bespoke data silos and pipelines to support every regulatory demand, they run automated reports on live data within their Teradata systems. They can build, test and store common models to reduce duplication, data movements and the potential for errors. Standardised and regular tasks can be automated creating further efficiencies whilst ensuing full auditability and provenance of data used to deliver reports. 

Our conversations with CDOs across the industry illustrate the many ways in which they are working to create common data platforms to increase accuracy, efficiency and speed of regulatory reporting. They understand the advantages and potential of replacing overwhelmingly manual processes with repeatable and automatable models that call upon a common, shared data platform and see their Teradata investments as the natural foundation for regtech. Increasingly they are leveraging the cloud capabilities of Teradata to bring speed, scalability and cost efficiency to this critical area of the business. 

To learn more about the concerns, ambitions and insights of CDOs in the sector please read our complete report “Accelerating data analytics: how financial services can learn from other sectors.” Or get in touch with the EMEA Financial Services consulting team. 


À propos de Simon Axon

Simon Axon leads the Financial Services Industry Consulting practice in EMEA. His role is to help our customers drive more commercial value from their data by understanding the impact of integrated data and advanced analytics. Prior to his current role, Simon led the Data Science, Business Analysis, and Industry Consultancy practices in the UK and Ireland, applying his diverse experience across multiple industries to understand customers' needs and identify opportunities to leverage data and analytics to achieve high-impact business outcomes. Before joining Teradata in 2015, Simon worked for Sainsbury's and CACI Limited.

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