From templates to underlying data
A template answers a question someone decided on years ago. When a new risk appears, supervisors design a new template or an ad hoc request, firms build it, and the answer arrives months later. With granular data, the records are already there. A new question becomes a new query.
Where it is happening
Euro area authorities have gone furthest, and others are following.
What changes for supervisors
Data volumes rise by orders of magnitude, so validation has to work at scale and as data arrives. Analysts move from reading templates to querying data, which needs different tools and skills. Aggregates must reproduce exactly, every time, or trust breaks. And the data model becomes a core supervisory asset that needs owners, versioning and governance.
A practical path for smaller authorities
You do not need a programme the size of IReF to benefit. Start with one dataset where templates answer badly, run it alongside the existing return, and prove that the aggregates reconcile before retiring anything. Each step is an iteration, not a replatform.
Where AI fits, and where people decide
Granular data is where AI earns its keep: scanning millions of records for concentrations, unusual patterns and data quality problems, and drafting the first cut of an analysis. The aggregation that produces official figures stays deterministic and reproducible. Supervisors decide what a pattern means and what to do about it.
- 1Pick one question your templates answer badly, such as concentration in a loan book.
- 2Define the smallest set of record-level fields that answers it.
- 3Run the granular collection alongside the existing template, not instead of it.
- 4Prove the aggregate reconciles to the template figure, every cycle.
- 5Retire the template only once the reconciliation has held for a full year.
Where Line4 fits
Line4 is a SupTech platform for financial regulators. It collects returns in XBRL, Excel and CSV, validates them against your taxonomies and rules at the point of submission, and puts AI to work reading them, with supervisors making every decision. If you work with a standard we have not yet run in production, we will say so plainly and show you how we would approach it.
Questions
What is granular data reporting?
Granular data reporting is the collection of record-level data, such as individual loans or securities holdings, from regulated firms, so supervisors can derive the aggregates and analyses they need instead of collecting pre-aggregated templates.
What is AnaCredit?
AnaCredit (analytical credit datasets) is the ECB’s loan-by-loan dataset on credit to companies and other legal entities in the euro area, collected under Regulation (EU) 2016/867 since September 2018.
What is IReF and when does it go live?
IReF is the Eurosystem’s Integrated Reporting Framework, which integrates banks’ statistical reporting into a single framework. In June 2026 the ECB set a pilot from Q2 2030 and go-live in Q2 2031.
What is BIRD?
The Banks’ Integrated Reporting Dictionary is a cooperation between the European System of Central Banks and commercial banks that defines how reports can be derived from banks’ internal data.
Does granular reporting replace XBRL?
Not necessarily. XBRL, particularly xBRL-CSV, can carry large structured datasets. The bigger change is in the data model and in how supervisors use the data once it arrives.