Transforming Regulatory Reporting

The contents of this blog are for general information purposes only and do not constitute legal advice. Association of Foreign Banks disclaims liability for actions taken based on the materials. Readers should consult their legal advisers.

Regulatory reporting in the banking and financial services sector continues to evolve at pace. Traditionally, regulatory interactions have involved template submissions, spreadsheet-based forms, and manual reconciliation. Today, data-centric rather than form-based approaches are shaping the future—driven by new requirements, innovative technologies, and the need for greater supervision and efficiency. Among the principals that underpin this evolution, three of the most critical are common data standards, granular data reporting, and machine-to-machine submission.

In this article I explore why these themes matter, review ongoing regulatory and market initiatives, and explain how banks and regulatory technology providers can adapt to embrace and benefit from the evolving landscape.

The Importance of Data Standards

Defining Data Standards in Regulation

When we talk about “Data Standards” in Regulatory Reporting. we need to think beyond merely agreeing on the formatting and presentation of data. Data Standards encapsulate a shared language for defining, validating, exchanging, and interpreting financial information—covering data models, taxonomies, semantic identifiers, and validation rules. In the UK, we see the Prudential Regulation Authority (PRA) and Bank of England promoting industry-wide standardisation, through projects such as the Future Banking Data (FBD) initiative.

Why Shared Standards Matter

Standardisation directly addresses long-standing industry pain points such as inconsistency, duplication, and high compliance costs and brings numerous business benefits:

• Consistent data definitions facilitate maximised automation and reduce manual intervention and associated risk
• Shared validation logic delivers cleaner, higher-quality reporting—avoiding errors that potentially trigger regulatory interventions
• Standardised taxonomies help connect data sources, boosting inter-operability and
  re-usability across different regulatory submissions

Regulatory Progress and Industry Challenges

Reporting reforms in both UK and mainland Europe have achieved early wins by streamlining templates and focusing on proportionality. For instance, recent proposals to delete redundant reporting templates are estimated to cut UK bank reporting costs by £26 million annually with no detrimental impact on supervisory needs. Yet, aligning standards across jurisdictions remain challenging even for multinational firms, leaving smaller institutions with an even greater struggle to find the resources to adopt new models.

Strategic Benefits

With shared standards, banks become better partners for regulators, enabling more responsive supervision, cross-jurisdiction risk assessments, and reduced compliance burden – leaving more room and resources for genuine innovation. RegTech vendors, in turn, can offer more reusable and interoperable solutions—conferring industry-wide benefit.

The Rise of Granular Data Reporting

Thinking outside the Template: What is Granular Data?

Granular data reporting marks a major evolution away from aggregated, template-driven submissions. Rather than asking banks to produce totals or summaries, regulators across the globe increasingly require transaction-level or attribute-level detail—such as each individual loan, deposit account, or contract.  

AnaCredit (ECB), IReF (EBA), and initiatives by the Bank of England typify this global trend. Under Granular Data frameworks, firms supply detailed, raw data sets once, enabling regulators to generate any required summary, report or analytical output themselves.

Benefits for Regulators and Banks

Granular submissions give supervisors richer insights. Instead of scrutinising high level aggregated data, regulators can:

• Detect patterns and anomalies at a more detailed and meaningful level (e.g., systemic exposures, emerging risks).
• Cross-check data across multiple reporting streams (e.g., statistical, prudential, risk) for consistency.
• Enable real-time or “on-demand” analysis, reducing response times in supervisory processes.

For banks, embracing granular reporting brings both challenges and opportunities. It demands:

• Robust data governance and systems able to capture, validate, and ensure high quality data. Current approaches using aggregated data allow for ‘wriggle room’ where a granular, transactional approach allows no latitude for this. Even greater importance is therefore placed on the ability for detailed and real time data lineage capability.

• Enhanced controls over data lineage, privacy, and organisational accountability.
• Greater internal visibility—potentially opening up data previously siloed in product or business-unit systems. The necessity for increased data quality results in a repository of regulatory and risk data with use and application far beyond the reporting team.
• Additionally, these challenges yield other long-term benefits: reduced duplication of reporting, elimination of data redundancy and easier adaptation to new regulations.

Practical Adoption

Granular data is already a reality in Asia-Pacific, with EMEA rapidly making ground solution providers now offer cloud-based, AI-enabled tools that streamline data collection and validation, position banks for future regulatory demands, and support scalability for new rules.

As a consequence, the notion of a regulatory reporting solution itself will change as data-centric platforms find greatly expanded business cases in previously unconsidered of areas of firms.

Machine-to-Machine (M2M) Submission in Regulatory Reporting

Defining M2M: Automating the Reporting Flow

Machine-to-machine submission means direct, automated exchange of structured data between a bank’s systems and regulatory platforms—driven by APIs, standardised schemas, and robust validation logics. The era of manual processing and aggregated data templates is being replaced by intelligent, rule-based data pipelines.

The UK regulators, through initiatives like TechSprint and the Digital Regulatory Reporting (DRR) pilot, have demonstrated the viability of automated reporting, successfully prototyping machine-executable rules and machine-readable requirements – though this is still to bear tangible fruit industry wide.

Business and Supervisory Advantages

• M2M can reduce error rates and speed up regulatory cycles given near real-time feedback and validation capabilities
• Operational costs and risks decrease as teams spend less time manually preparing submissions and are able to use their specialised knowledge and experience on data analysis and insights.
• Regulators benefit from rapid, accurate data flows, improving their ability to identify risks and respond to market developments.

Model-driven, machine-readable and executable regulatory reporting also means that changes to rules can propagate instantly across reporting solutions—minimising delays and misinterpretation.

Implementation Challenges

Success of M2M platforms relies on focus on and investment in:

• Standardised data models and taxonomy alignment (linked to the concept of data standards).
• Secure, scalable APIs and infrastructure.
• Process redesigns that allow reporting teams to move from manual “form fillers” to expert analysts

Industry feedback to the FCA and BoE has called for continued and increased collaboration on technology standards, privacy, and transition periods to secure widespread M2M adoption.

Proliferation of granular data initiatives only increases the desirability and ultimate efficacy of M2M based solutions. In turn, M2M solutions will contribute to the realisation of the true benefits of granular data initiatives.

Building the Regulatory Data Infrastructure of the Future

How the Pillars Reinforce Each Other

The three pillars—standards, granularity, automation—represent a matrix of interlocking benefits. Common standards are prerequisites for granular reporting, ensuring detailed data can be reliably parsed and analysed. Granular data enables M2M automation since both regulator and bank can trust structure, lineage, and content. Finally, automated M2M platforms reduce operational expense and risk and allow for more frequent supervisory analysis.

Preparing for the Data-Driven Reporting Era

Firms should prioritise the following:

• Data Infrastructure: Invest in data hubs, lineage tracking, and scalable architectures capable of integrating with regulatory platforms.
• Governance: Build robust internal controls, ownership frameworks, and privacy protection for increasingly detailed data sets.
• Technology Partnerships: Choose platforms and vendors committed to interoperability, standards alignment, and cloud- or API-based innovation.

RegTech companies play a pivotal role—providing modular, future-ready solutions that help both established and challenger banks comply with new data paradigms at both an optimised cost and speed.

Conclusion

We are seeing a significant evolution in regulatory interactions in UK (and global) banking. Legacy, template-driven models are giving way to systematic standardisation, detailed data flows, and automated machine-to-machine reporting. Banks, regulators, and solution providers that embrace these changes will realise efficiencies, strengthen risk oversight, and build a more dynamic, data-driven financial ecosystem.

The coming years will define not just how compliance is met, but how the banking sector leverages data for strategic advantage. For solution providers, it is a significant opportunity to shape tools and practices that enable institutions to thrive in a rapidly evolving supervisory environment.

In my next blog, I’ll consider in more detail the advent of Granular Data Reporting – those factors that give it impetus, some current real-world examples and how AI might interact with and facilitate granular solutions.

Authored by

Andrew Kesbey

Andrew has been working in Banking and Software Solutions for the past 40 years in a variety of roles

Working in consultancy and pre-sales roles for solution providers in the areas of post-trade processing, SWIFT messaging, reconciliation, Andrew has focussed on the Regulatory Reporting domain for the past 11 years with Lombard Risk/VERMEG and Regnology. Andrew currently holds the position of Chief Growth Officer at Focusync.

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Supported by

Focusync is a provider of Regulatory Reporting and Risk solutions to the UK Banking community. We offer a full scope (Bank of England, PRA, FCA etc), end to end platform on cloud or on premise with a focus on quality of both product and projects (for which we have a 100% record for delivering on time and at a fixed price with 24/7 support).

Our reporting solution supports a ‘No Touch’ workflow minimising operational risk, combined with innovations such as AI driven predictive liquidity to provide the most reliable, forward looking solution possible.

Our ALM and Stress Testing solutions share the same single source of truth data platform ensuring efficiency, accuracy and precision, establishing a solid basis for compliance while pioneering forward-thinking approaches to anticipate the evolving needs of the financial landscape – giving our clients the confidence to navigate regulatory challenges and embrace a future where efficiency meets foresight.