Regulators now request more data more often than ever before. The goal is to ensure financial institutions are compliant with regulations and are doing their due diligence, but regulators are in danger of becoming overwhelmed by the data they ask for. As the volume of data increases, a regulator’s capacity to process, analyse and understand can come under pressure.

As Ryan Flood, our CTO, highlighted in a recent Central Banking panel discussion, the sustainability of regulatory reporting is dependent on the role of the regulator evolving beyond that of being a data steward. He noted that typically today, a portion of a supervisor's time is spent on activities that are not pure supervision, including wrangling data volume or data complexity.

The portion of time spent on those activities is on the rise, which means the time they are spending on real supervisory activities is on the decrease. Regulators need to look for solutions to that problem and technology can help address it effectively.

They also need to invest in developing regulatory data management skills within their teams. Lack of this approach can expose regulators to organisation risks such as:

  • Loss of skills as key personnel move or become unavailable.
  • Siloed departments are incapable of responding to business needs.
  • Inefficient throughput of changes and excessive internal costs.
  • Inability to scale and service multiple departments in parallel.
  • Confusion for filers which in turn creates reputational risk.

Data modelling and regulatory data management

When data is managed manually, the regulator creates a new requirement and it's up to each institution to interpret it and figure out what it means for them. They then find out what IT or human resources might be needed to meet it.

For larger retail firms, this costs about $450,000 annually. The process is bloated with institutions overwhelmed by the scale of requirements, and regulators unable to effectively analyse the data. To help address this challenge, regulators are turning to data modelling - a representation of all the data and the relationship between different datasets.

Effective data modelling enables regulators to analyse their data and supervise effectively while encouraging compliance and input from the industry. However, there are some common trends across the financial services industry, where poor data modelling can lead to:

  • Duplication of data collected, increasing the frustration of financial institutions (FIs).
  • Numerous, large (‘bloated’) and complex data collections.
  • Consumers struggling to understand the data they are required to report.
  • Regulators being unable to analyse the data effectively/easily.
  • Lack of stakeholder buy-in (financial institutions, RegTech and downstream stakeholders, e.g., Business Intelligence team).
  • Difficulty to fix. Once a data collection is published it is difficult to “change direction” if it is modelled poorly.

To keep up with challenges, regulators need to create a new operating model for data management. We have outlined six core steps that can help in our recent regulatory data management insights post. They include vision and mandate; processes, standards, and best practices; community; technology; resources and skills; and governance. These steps work in parallel with new methods of data collection to create a new operating model capable of working at higher levels.

Key principles of regulatory data management 

We’ve taken our 20 years of domain knowledge and experience and developed an approach to assist regulators model and manage their data, based on a defined set of principles to describe best practice data modelling. These principles include: 

  • A common language: By creating a data dictionary, which has one definition for each data point, there is no ambiguity. This leads to zero duplication of data collected. 
     
  • Coherent data collections: Optimised data collections, that are easily understood, consumed and consistent are more efficient and easier to manage. Users continue using the tools they are familiar with, mainly Excel, but find it easier to get real value from the data.
  • FIs and RegTech enablement: Producing good data models and publishing them in well-defined, machine-readable specifications, helps reporting entities and industry to adapt faster and accelerate the adoption of regulatory technology.
  • Format agnostic: Data models need to be format agnostic to allow flexibility to add or replace existing formats, but also to design consistently independent of them. 
This approach to regulatory data management optimises every aspect of the data collection life cycle from collection to analysis. 

Benefits of strong regulatory data management

There are significant benefits of strong regulatory data management, which - working in tandem - can have a transformational impact on your supervisory activities.  

  • Optimal industry adoption of data collections 
  • Reduction of industry burden of regulatory reporting 
  • Quicker and easier to get real value from the data 
  • Easier to manage and analyse data collections 
  • Enabled next-generation supervision (advanced analytics, granular data, machine-to-machine reporting). 

By investing in technology and upskilling your team that can readily adapt to new requirements, your organisation can understand and analyse data without the burden of additional staff or cumbersome manual processes.

The next steps in becoming a more efficient regulator – are you ready? 

As the volume of data regulators collects and analyse continues to grow rapidly, data modelling will become a core pillar of regulatory data management.  

Our tried and tested regulatory data management process is established and proven over 20 years of practice specific to the regulatory industry. We have developed processes, tools and training to build a productised service that is available to our customers, Vizor Regulatory Data Management (VRDM).  

This service empowers regulators with self-sufficiency, reduces the production time of data collection from the first conception to production, whilst at the same time ensures the data collections, they produce are best practice and standardised, bringing all the benefits of good data modelling and data management. 

If you want to find out more, book a demo with our expert team today.  

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