The rise of machine-readable and AI-enabled regulatory reporting

Theme 1

The principle of "Collect Once, Use Many"

European authorities are signaling a direct, systematic approach to reducing data fragmentation. The industry-wide challenge of duplicative reporting and downstream reconciliation is being addressed at an architectural level.

Rather than treating reporting as a collection of separate, template-driven compliance exercises, several initiatives increasingly support the development of more consistent data foundations designed to serve multiple regulatory, supervisory, and analytical purposes.

EBA Simplification

The European Banking Authority’s simplification proposals represent a major shift. The proposals integrate and streamline requirements across a broad range of areas, including FINREP (linked to IFRS 18), operational risk, liquidity risk, ESG, supervisory benchmarking, stress-testing information, and selected Pillar 3 disclosures. Together, these measures contribute to a more stable, reusable data foundation.

The EBA package therefore extends beyond a pure data-point reduction exercise. It seeks to improve consistency across reporting areas; limit separate or overlapping data collections and make greater use of existing reporting structures. At the same time, the broader evolution of the EBA reporting framework is not limited to simplification. The introduction of harmonized requirements for areas such as third-country branches may initially increase the overall reporting scope.

ESMA’s "Report Once" proposal

ESMA provides one of the clearest examples of a structurally integrated reporting approach. It has identified up to €1 billion in potential annual savings by proposing a structural simplification of transaction reporting across MiFIR, EMIR, and SFTR. The importance of this initiative goes beyond reducing individual fields or templates; it directly addresses the architecture of regulatory reporting through shared standards, common data structures, and more efficient data exchange.

EIOPA Solvency II reductions

EIOPA’s approach is an example of reducing reporting burden within an existing regulatory framework. The proposed changes include reduced reporting frequencies, the removal of selected annual templates, stronger proportionality, and technical simplifications. For solo undertakings, EIOPA estimates a 22% reduction in data points, alongside reductions of 26% in quarterly templates and 30% in annual templates.

Theme 2

High-quality, granular, and readily accessible data

At the same time that processes are being integrated, supervisors and banking regulators are increasing expectations regarding the granularity and quality of the underlying data. Supervisors are complementing aggregated reporting with more granular, harmonized, and reusable datasets.

The ECB’s Integrated Reporting Framework (IReF)

IReF is the flagship initiative in this space. The framework moves away from country-specific statistical templates (replacing AnaCredit, BSI, MIR, and SHS) to establish a single, harmonized euro-area reporting framework built on granular data points. The ECB's multi-year roadmap, leading to a pilot phase in 2030 and official reporting in 2031, gives banks a concrete path to modernize their data architecture.

SRB valuation and liquidity capabilities

The SRB provides another example of how crisis preparedness increasingly depends on standardized, high-quality, and rapidly accessible data.

The SRB’s Expectations on Valuation Capabilities (EoVC) require banks to establish structured valuation data repositories, a Valuation Data Index, an enhanced Valuation Data Set, and supporting valuation playbooks. The framework is being implemented gradually through 2029 and is intended to ensure that sufficiently granular and reliable data can be made available quickly in a resolution scenario.

The guidance on liquidity and funding in resolution complements this by focusing on the operational capability to estimate liquidity needs, report liquidity positions, and identify and mobilize collateral under demanding crisis conditions.

Theme 3

The common language and the emergence of AI-ready ecosystems

The shift towards more integrated reporting and greater use of granular data creates an additional challenge: information must be defined consistently and made interpretable across different systems, authorities, and regulatory domains.

This is where the Data Point Model (DPM) standard plays an increasingly important role.

The DPM provides a common semantic and technical foundation for a growing range of European supervisory, resolution, and statistical reporting requirements. It connects reporting templates with clearly defined business concepts, dimensions, and validation logic, making regulatory requirements more machine-readable and easier to process systematically.

DPM is increasingly governed within a joint institutional framework. Following the publication of the DPM 2.0 standard in 2023, the EBA, ECB, and EIOPA established the DPM Alliance in 2024. The Alliance provides a joint governance framework for the evolution of this standard, supporting greater harmonization of definitions across banking, insurance, and statistical reporting.

This standardized data structure is crucial because it acts as the essential fuel for both SupTech (Supervisory Technology) and RegTech (Regulatory Technology) solutions:

For Supervisors (SupTech)

With a structured metamodel in place, European supervisors may benefit from more dynamic, automated and data-driven oversight. A rich metadata framework provides the semantic lineage required to facilitate interoperability and data exchange between national and supranational institutions. This shared digital foundation may facilitate capabilities such as agentic AI-driven Smart Data Validation (using ML anomaly detection), Automated Text Extraction (parsing structured disclosures via NLP), and Predictive Risk Modeling that adapts to regulatory updates over time.

For Financial Institutions (RegTech)

These developments pave the way for the next generation of automation: Agentic AI. Once data is less dependent on siloed templates and structured within a governed, semantic model, advanced tools can support regulatory impact analysis, consistency checks, data mapping, and regulatory change detection. In the longer term, these foundations may also enable more agent-based workflows, subject to appropriate governance, auditability, and human oversight.

Looking ahead: The architectural imperative

All eyes are now on the EBA's upcoming Data Point Model update, DPM 2.1. Far from a dry technical specification, this update may provide an important next layer for a more consistent, machine-readable, and interoperable regulatory data architecture.

The strategic direction is clear: the focus now turns to architectural implementation. Success goes beyond simply updating old templates; it requires a targeted investment in building a robust, well-governed, and reusable data foundation designed for a smarter, more integrated future.