How are regulation and technology driving banks towards becoming more data driven?

Three external factors are compelling banks to transform their business and operating processes into more data-driven models

One of the consequences of the 2008 financial crisis is that banks have to handle an ever-increasing reporting burden. While supervisors acknowledge these impositions and aim to reduce them, new reporting obligations loom on the horizon as supervisory focus shifts towards tackling climate change risks and associated sustainability reporting. Alongside these pan-European initiatives, local regulators such as the Dutch Authority for Financial Markets intend to increase their data requests to participants in the financial markets to upgrade their ‘data-driven supervision’ capabilities to protect consumers. Simultaneously, the quest for a competitive advantage and cutting-edge technological trends exerts increasing pressure on traditional banks to rethink their business models in order to become future-proof, data-driven organizations.

Before we address the issue of what banks could do to confront these challenges, it is useful at this point to take a closer look at three key drivers that clearly illustrate the need for banks to transition towards a truly data-driven business model.

  1. Compliance and reporting

Since the financial crisis, banks have been subjected to a multitude of new reporting requirements to provide regulatory authorities with the information needed to carry out their supervisory tasks. This reporting includes statistical data that is becoming increasingly granular and prudential and resolution data. Supervisory authorities apply the principle of proportionality to these requirements, leading to large institutions reporting over 10 times the number of datapoints compared to more modest peers. Nevertheless, these smaller institutions report that EBA supervisory reporting alone already accounts for almost 40% of their total compliance costs. According to a recent Cost of Compliance study, this percentage translates into an exorbitant 20.4 billion euro in compliance costs across all EU institutions . The main drivers for these costs are:

• Complexity of the requirements;
• The amount of data reported;
• Internal data extraction and calculations;
• Stability and uniformity of the EBA supervisory framework and definitions.

Approximately 78% of financial institutions regard the rapid change in reporting requirements and ad-hoc, non-standardized reporting requests as significantly impacting compliance costs. Moreover, supervisory authorities increasingly focus on banks’ internal IT and data infrastructure and governance in order to enhance data control and improve data quality. A good example of this is the Basel Committee on Banking Supervision Principles for effective risk data aggregation (BCBS 239).

These regulatory pressures and compliance costs are swaying supervisors towards increasing standardization and harmonization of reporting requirements and data submissions. This could potentially reduce costs by as much as 24% , providing institutions with an unparalleled opportunity to reassess their overall reporting and data strategy, including their current IT and data infrastructure.

  2. Technological developments

According to a recent EY CFO survey , technological breakthroughs resulting from the big data revolution are not being sufficiently leveraged by the finance functions of many reporting institutions. Specifically, 43% of respondents indicate that the current finance function does not have access to and ownership of the appropriate technologies and data analytics, while 57% believe two of the predominant hurdles to reshaping the finance function are legacy IT systems and inconsistent data. While larger institutions struggle with the integration of new technologies within their large and complex legacy data architectures, smaller institutions are often sceptical about the use of RegTech and FinTech within their reporting processes. These are deemed to be prohibitively expensive, lacking the critical mass for widespread implementation.

In addition to this, recent innovations in AI, advanced analytics and cloud computing are further challenging institutions to rethink their data architectures. Serverless platforms, modular design and containerization provide unique opportunities to increase flexibility and speed, both of which are crucial in a constantly changing environment.