Granular Data in SupTech: Developing a digital ‘lingua franca’

Miles or kilometres, celsius or Fahrenheit, inches or centimetres - we come across different ways of communicating the same data every day. When people use different methods of measuring the same thing, it can create confusion.

When it happens in a sector like financial services where tens of thousands of transactions are processed each minute of each day, it can cost both regulators and the regulated significant amounts of time and money. Inconsistencies result in slow, inefficient processes. Different approaches to data used by different geographies or sectors in dealing with regulators cause delays, errors, and duplication of work.

A digital “lingua franca” - a common language to process data in the world of financial regulation - is sorely needed. Granular Data in SupTech can be part of the solution.

The exponential rise in data volumes

The volume of data financial services organisations and regulators must process is increasing exponentially. This surge in data within the datasphere since 2010 and projected to 2025 is illustrated below, broken down by region.

Financial regulators are facing the same challenge, and a failure to adapt harms all stakeholders – the regulator, the regulated, and the public.

• The increase in data volumes in financial regulation is evident. Huw van Steenis, chairing a ‘Future of Finance’ review for the United Kingdom’s in 2019, noted an explosion of data meant supervisory teams were “receiving twice the entire works of Shakespeare in reading each week”.
• Van Steenis further observed that the potential backlog of reading material to interpret is not going to reduce “…the amount of data HSBC stores on its servers doubles every two to three years.”

The pollsters consider this number conservative. The poll found that even where common standards exist, interpretations differ across borders. This burden disproportionately impacts smaller institutions.

The Bank of England acknowledges this pain point in financial regulation: “The Bank often requires data to be aggregated in ways that make reports hard to repurpose. This leads to more requests for new reports or breakdowns of existing reports than would otherwise be the case. It also leads to redundancy in the reporting process, as firms need to re-assemble the same underlying building blocks in different ways for different reports.”

What is Granular Data in SupTech?

Granular Data in SupTech is one part of the solution for handling the exponential increase in data. It enables this digital lingua franca for financial regulation. Granular Data means data that is broken down into the finest, most detailed level that it is practical to use. Breaking the data down to its smallest components is called disaggregation.

For the financial services organisation, this means:

• It is easier to understand the nature of the request since the language is broken down into its simplest components.
• It is less burdensome for the financial services organisation as the scope for misinterpretation is minimised.
• Reporting is more accurate which reduces the need to make resubmissions.

For the regulator, this means:

• The burden of data reporting is reduced and results come faster.
• Improved accuracy and insights as the financial services organisation will not misinterpret the data request.
• Greater flexibility to re-use acquired data to produce different insights.

Case study: Bank of England implements Solvency II

Solvency II is a directive in EU law that codifies and harmonises the EU insurance regulation. Under the law, EU insurance companies must hold a certain amount of capital to reduce the risk of insolvency.

The Solvency II collection asks insurers to provide 30 data points on each asset they hold – including their nature, issuer, economic sector, value, and acquisition price. The bank uses the Vizor SupTech platform, called the Bank of England Electronic Data Submission (BEEDS) Portal, to collect this data.

The bank noted several benefits:

• Supports the bank’s risk reviews of insurers.
• Allows analysis of common counterparty exposure risk and country-specific concentration risk.
• Mitigates ad hoc data requests by the bank when conducting thematic reviews of asset class risk.

Key factors in enabling a digital lingua franca

Granular Data in SupTech is only one part of the solution. Regulators should not consider them in isolation. Supervisory authorities must adopt a range of strategies. This should include technology, processes, and tools, including:

• Ongoing collaboration with all industry stakeholders.
• A well maintained, descriptive, and machine-readable data dictionary like ECB’s Bank’s Integrated Reporting Dictionary (BIRD).
• Use of machine-readable data collection.
• Publication of these specifications so that they are available and consumable by both humans and machines.
• Format agnostic specifications allowing consumers choice regarding their preferred method of consumption.
• New or improved approaches to drafting reporting instructions from regulation – examples include standardised natural language and instructions.

Regulators and financial institutions can implement a variety of other solutions in combination with Granular Data in SupTech to address growing volumes of data include:

• Using APIs to enable automated machine to machine reporting. New architectural approaches to data acquisition in large volumes are necessary to reduce human effort and errors and improve timeliness.
• Machine learning can facilitate the automated generation of insights leading to prompt decision making by regulators.
• Big Data: Big Data is a broader concept than Granular Data, associated with large amounts of both unstructured data and structured data.

Could Granular Data in SupTech change how you do business?

The need for a common language based on Granular Data in SupTech is clear, but each regulator needs to plan and find their roadmap to get there.

A phased approach to introducing granular data allows for time to attain and promote industry buy-in, as well as meet your most pressing supervisory needs in a prioritised manner.