The Future of Supervisory Data: Implications of the Bank of England’s Discussion Paper

Jump to: A shift in RegTech and RiskTech markets | Implications for banks | Implications for vendors | The Chartis View

What you need to know

The Bank of England’s (BoE’s) discussion paper on the future of banking data, published on 4 February, 2026, represents more than a regulatory consultation. It signals a structural reset in how supervisory data is defined, governed and exchanged between banks and regulators.

Chartis believes that, for banks and technology vendors, this perspective marks an important transition from report-centric compliance to a data management platform that can manage regulatory data and the processes around it (including ingestion, aggregation, validation, governance and lineage).

In this article, Research Director Anish Shah outlines Chartis’ perspectives on the strategic themes emerging from the paper, and the implications for banks and technology vendors.

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A shift in RegTech and RiskTech markets

The supervisory direction outlined by the BoE reshapes competitive dynamics across the RegTech/RiskTech ecosystem. Performance and success are shifting away from template automation toward integrated data platforms (see Table 1).

In this context, Chartis has identified several key perspectives and dynamics:

• A move from burdensome reporting to data infrastructure. Historically, regulatory reporting has been template-driven: banks compile reports and regulators review submissions. Under a future supervisory model, banks will maintain standardized datasets and regulators will be able to access structured, validated data. This reframes regulatory reporting in terms of an overall supervisory infrastructure, rather than as a process of periodic disclosures. The emphasis on performance moves upstream, from robust form production to the integrity of data architectures.
• Increased standardization and harmonization. Common taxonomies and consistent definitions will reduce supervisory friction and improve cross-firm comparability. They will also reinforce the need for enterprise data dictionaries and ‘golden source’ architectures.
• A move toward real-time supervision. While full real-time supervision may be some way off, the immediate path is toward faster access to liquidity, capital and risk data, particularly during times of stress. Treasury and asset and liability management (ALM) functions will therefore become central to the transformation agenda.
• More machine-readable reporting. Structured data submissions and automated validations reduce manual adjustments and a dependence on spreadsheets. Regulatory logic (such as the data validation and modeling needed for calculations) will become embedded within platforms.
• Heightened expectations around data governance and lineage. Increasingly, supervisors will expect firms to demonstrate clear ownership of critical data elements and end-to-end data lineage transparency. Data governance will increasingly converge with risk governance, to ensure compliance with Basel Committee on Banking Supervision (BCBS) 239 principles.
• More use of artificial intelligence (AI) and automation. As regulators modernize their own analytical capabilities, the next technology frontier will extend beyond data submission to data interpretation. 

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Implications for banks

The transformation signaled by the BoE will force banks to rethink and redesign their regulatory risk and reporting architectures, rather than fixing silos with workarounds. Banks must evaluate whether their current systems can support:

• Cross-functional integration of risk, finance and treasury data.
• Intraday or near real-time aggregation.
• Scalable validation and rule engines.

Legacy environments built around siloed reporting solutions are unlikely to support future supervisory expectations without significant modernization. This creates both investment pressure and strategic opportunity.

A shift to a data infrastructure means that banks will have to address new considerations:

• Standardizing enterprise data.
• Reducing manual reconciliations.
• Aligning risk, finance and treasury data.
• Investing in lineage and traceability.

Overall, regulatory modernization can become a catalyst for enterprise-wide strategic platforms to manage data and leverage analytics and AI. Banks that treat this change solely as a compliance reform risk missing broader enterprise benefits, including:

• Faster stress-testing cycles.
• Improved balance-sheet optimization.
• Reduced operational risk.
• Enhanced strategic decision-making.

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Implications for vendors

Table 2 summarizes Chartis’ perspectives on the main implications of the coming transformation for tech vendors.

In an age of AI, the competitive dynamic will be less about filing returns and more about surfacing systemic risk signals. Vendors will differentiate themselves with:

• AI-driven inconsistency detection.
• Automated data-quality scoring.
• Predictive supervisory risk indicators.
• Dynamic regulatory rule mapping.

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The Chartis View

Over the next three to five years, Chartis believes that three market shifts are likely:

• Platform consolidation as banks rationalize their fragmented reporting stacks.
• Increased M&A activity, particularly among data governance and lineage specialists.
• Hybrid models, whereby top-tier banks combine their internal data lakes with vendors’ data-validation engines.

The winners will be vendors that:

• Embed data governance and lineage natively.
• Integrate across risk domains.
• Enable scalable, API-driven supervisory interaction.
• Support cloud-native deployment.

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