IReF and The Future of Regulatory Reporting – How IReF, RDARR/BCBS 239 and AI are reshaping banking data architectures
Regulatory reporting in Europe is entering a new phase. Increasing data granularity, higher reporting frequency, and stronger expectations regarding governance and trace-ability are reshaping how institutions design and operate their reporting frameworks. What was once treated as a compliance layer is increasingly becoming a structural com-ponent of banks’ data and operating models.
The Integrated Reporting Framework (IReF) is one of the clearest manifestations of this broader shift. While formally a statistical reporting initiative, its move from template-based submissions to harmonised granular data alters how data must be structured, controlled, and aggregated across the organisation.
For institutions operating legacy, template-driven reporting chains, this shift has implications beyond reporting formats. It affects the design of data models, the scalability of aggregation processes, and the operational embedding of governance responsibilities.
IReF is often framed as a reporting reform. In practice, it shifts the conversation to architecture: sustainable delivery of granular, higher-frequency reporting depends on governance, lineage, and aggregation being embedded in the operating model.”
– Stefan Weiß, Managing Director, Advisense Germany
In our work with financial institutions, the discussion around IReF quickly expands from reporting templates to broader topics such as end-to-end data governance, aggregation capabilities, and the sustainability of existing reporting architectures.
In parallel, the principles for Risk Data Aggregation and Risk Reporting (RDARR) established by the Basel Committee on Banking Supervision under standard number 239 (BCBS 239) establish supervisory expectations around risk data aggregation and reporting. Together, these developments show a structural evolution of regulatory reporting, from periodic data submissions toward more integrated, technology-driven transparency frameworks supported by scalable data architectures and automation.
Regulatory Fragmentation and the Rationale for IReF
Statistical reporting in the euro area has evolved through national frameworks, resulting in fragmented rules and standards. IReF seeks to introduce a harmonised and standardised approach to data collection.
IReF reflects a broader regulatory trend: authorities increasingly expect granular, harmonised data that can be reused across analytical and supervisory contexts.
National legal bases, including certain statistical publications by the Deutsche Bundesbank, are intended to be repealed as part of the implementation. According to the Bundesbank’s IReF FAQs, the extent to which additional national statistical surveys can be eliminated is under review, depending on the final substantive and methodological design of IReF and its ability to cover national data needs.
While IReF aims to reduce redundancies over time, the shift to granular reporting increases data volumes and demands more robust data architectures and governance.
From Templates to Granular Data
IReF replaces predefined reporting templates with a harmonised granular input data model. Supervisory authorities will analyse detailed data directly rather than relying solely on aggregated outputs.
This shift aligns with a wider transformation in regulatory reporting: quality, consistency, and traceability must be ensured at the source level rather than corrected downstream.
IReF is currently planned to apply to:
- Monthly Balance Sheet Statistics (BSI)
- MFI Interest Rate Statistics (MIR/MITR)
- Securities Holdings Statistics (SHS)
- Credit Data Statistics (including AnaCredit)
Building on initiatives such as AnaCredit, this approach enhances consistency and comparability while raising expectations for data control and scalability.
RDARR as Foundational Enabler
The RDARR principles under BCBS 239 continue to define supervisory expectations regarding risk data aggregation and reporting. Clear governance, data quality, lineage, and scalable aggregation capabilities are not new concepts, but in a granular reporting environment, they become structural preconditions.
The combination of RDARR expectations and granular reporting frameworks such as IReF reinforces a broader shift: regulatory reporting is no longer limited to format compliance but depends on the robustness of underlying data ecosystems.
Corrections and quality improvements are expected to occur primarily at the input level, increasing the importance of disciplined governance and consistent data flows across the reporting chain.
AI, Automation, and Scalable Architectures
As reporting frequency and data volumes grow across regulatory domains, scalable automation is essential to sustain large-scale reporting.
Technology-enabled capabilities, including automated data quality controls, lineage transparency, and advanced analytics, help institutions manage growing complexity while maintaining traceability and control.
In this context, AI is not a regulatory requirement but a practical enabler of scalable, data-driven reporting architectures.
The Role of BIRD
The Banks’ Integrated Reporting Dictionary (BIRD) supports IReF implementation through a harmonised logical data model, standardised definitions, and transparent transformation rules.
More broadly, initiatives such as BIRD reflect the direction of travel in regulatory reporting: define once, report once, and enable consistent data use across frameworks.
While IReF defines the statistical scope (“what”), BIRD provides structured guidance on implementation (“how”). The frameworks are designed to be closely aligned, supporting integrated reporting without introducing new regulatory requirements.
Lessons from AnaCredit
AnaCredit demonstrated that large-scale regulatory data initiatives involve both technical and organisational challenges. Data quality, IT constraints, and evolving requirements require structured governance and scalable architectures.
These lessons illustrate a recurring pattern in regulatory transformation: granular data initiatives succeed when governance and operating models align with technological capabilities.
Timeline
The implementation plan for IReF is expected in 2026. A one-year pilot phase will precede regular reporting, planned to begin in the fourth quarter of 2029.
The timeline underscores that regulatory reporting transformation is a multi-year journey requiring sustained architectural and organisational alignment.
Management Implications
IReF does not introduce new principles but raises operational expectations for existing governance and aggregation frameworks.
More broadly, the evolution of regulatory reporting in Europe signals a transition from static submissions toward integrated, data-driven transparency frameworks.
For senior management, the key question is not whether to comply with individual initiatives, but whether the institution’s data architecture and operating model are sufficiently scalable, traceable, and resilient to support granular and increasingly frequent reporting across regulatory domains.
Sustainable compliance requires governance and architecture that are fully embedded throughout the reporting chain.
