AI makes shared reporting standards more valuable
Artificial intelligence increases the need for consistent data definitions, De Nederlandsche Bank (DNB) said in an October 27, 2026 speech in The Hague. It urged continued investment in shared European reporting infrastructure.
One taxonomy across the chain
DNB said effective supervision requires information to retain the same meaning as it passes between reporting institutions, national supervisors and European authorities.
XBRL supports this through shared definitions, validation rules and visualisation of reported information.
Automated collection and processing reduce manual work, while validation identifies data quality problems early.
The XBRL taxonomy supplies the understanding needed to interpret reported information without external material.
XBRL International certifies software against its specifications, allowing institutions and supervisors to use different packages under uniform standards.
Common specifications also benefit software vendors and reduce ambiguity across the reporting network.
Granularity raises the stakes
European supervisors are using larger, more granular and interconnected datasets, with the European Central Bank's Integrated Reporting Framework (IReF) part of that development.
DNB said greater granularity can improve risk assessment and integrated information can strengthen cooperation.
AI works best with structured information, clear descriptions and meaningful context, rather than compensating for weak data structures.
Future XBRL development must preserve semantics, metadata and context, not merely make facts and values easier to exchange.
Meaning before volume
DNB's message is that faster data exchange alone does not deliver better supervision.
It places shared meaning, interoperability and trust at the center of Europe's reporting network.
Its case for AI therefore rests on stronger standards, not on replacing them with more capable analytical tools.
Source: Keeping European supervision on Track
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