The opinions expressed here are those of the authors. They do not necessarily reflect the views or positions of UK Finance or its members.

As the Bank of England's Prudential Regulation Authority (PRA) Future Banking Data initiative signals, regulators are moving toward more dynamic, granular data collection. The question isn't whether reporting teams will need to evolve - it's whether they'll lead that evolution or be dragged along by it.

From rulebook interpreters to data scientists

Traditionally, regulatory reporting professionals built careers on deep knowledge of the Capital Requirements Regulation (CRR), understanding COREP and FINREP template logic, and navigating PRA supervisory guidance. That expertise remains valuable, but it's no longer sufficient.

Today's regulatory reporting teams increasingly require data science capabilities: Python, R for automation, SQL for complex data lineage analysis, and statistical methods for variance detection. The shift reflects a fundamental reality - the majority of regulatory reporting challenges are data challenges, exacerbated by regulatory interpretation uncertainties.

Successful teams are building hybrid capabilities: regulatory subject matter experts who can code/data scientist, and data engineers who understand prudential concepts like risk-weighted assets (RWAs), liquidity coverage ratio (LCR), and large exposures reporting.

Integration, not isolation: Breaking down silos

Perhaps the most significant change is how regulatory reporting teams interact with the rest of the business. The traditional model - reporting sits in Finance, receives month-end files, and submits returns to the Bank of England - is breaking down.

Leading UK banks are embedding reporting expertise earlier in the value chain. When product teams design new offerings, reporting specialists sit alongside Risk, Treasury,and Finance to ensure data capture happens at origination. When pricing models are developed, reporting teams contribute intelligence on how data quality affects capital costs - turning compliance into commercial insight.

This integration addresses a challenge repeatedly highlighted in UK Finance industry discussions: the gap between regulatory templates and how firms actually manage risk.

The automation imperative: Meeting PRA expectations

Manual reconciliations and spreadsheet-based calculations are giving way to automated pipelines, rule-based validations, and exception-driven workflows. Artificial intelligence and machine learning are entering the picture as tools for workflow automation, quality monitoring, pattern recognition, and anomaly detection.

The PRA's emphasis on data governance, Basel Committee Principle 239 (BCBS 239) compliance, and near-real-time aggregation capability makes automation essential. Banks maintaining legacy manual processes and tools cannot meet accelerating PRA supervisory expectations while managing the complexity of Basel 3.1, SDDT, COREP, FINREP, and emerging granular data requirements. Critically, automation enables senior reporting professionals to focus on higher-value activities; ensuring submissions are credible, internally consistent, and decision‑useful; anticipating and responding to supervisory queries and thematic reviews; and advising the business on the strategic implications of reported outcomes. This represents a move from data processing to data intelligence. data processing to data intelligence.

The business case for RegTech investment

CFOs and boards increasingly recognise that regulatory reporting infrastructure isn't just a cost centre - it's foundational to capital efficiency and sound governance. Poor data quality doesn't just risk PRA supervisory fines; it results in suboptimal pricing, excess capital buffers, and missed commercial opportunities.

Importantly, when the same data, controls, and metrics used for regulatory reporting are embedded in internal MI and decision-making, bank’s directly support the PRA’s objectives on governance, controls, and senior executive oversight. Demonstrating end-to-end data lineage, golden source alignment, and timely aggregation of exposures enables faster, better-informed decisions, more accurate risk assessment, and ultimately, competitive advantage in the UK banking market. 

This consistency between external reporting and internal MI responds directly to the PRA’s repeated focus on data credibility, management accountability, and the expectation that firms can demonstrate how key metrics are used in running the business, not merely reported to supervisors.

Building the foundation

The regulatory reporting transformation demands modern RegTech infrastructure bridging legacy systems and emerging supervisory expectations. The most successful implementations share common characteristics: unified data models creating a single source of truth across prudential, statistical, financial, risk models, and ad-hoc reporting; automated data ingestion eliminating manual reconciliations; and intelligent validation frameworks catching errors before they reach PRA supervisors.

Modern RegTech platforms enable end-to-end lineage tracking, near-real-time aggregation across legal entities, and the data governance transparency supervisors expect. Banks building these foundations today position regulatory reporting as strategic infrastructure serving risk managers making real-time decisions, finance teams optimising capital allocation, and PRA supervisors monitoring systemic risk. The technology exists. The question is: which banks will deploy it strategically?