Artificial intelligence thrives on structure.

Agency-specific FDTA rules need to make sure the data they collect is AI-ready.

AI shines when given access to richly contextualized, standardized, structured data and identifiers. While Large Language Models (LLMs) can source any kind of data, structured or unstructured, for financial analysis and research that demands a high degree of accuracy, structure and standardization are crucial.

Here's why open data standards and identifiers drive better AI.

Context matters. Contextual information like definitions, data types, dimensional characteristics and understanding the relationships between facts, give AI the information needed to go beyond surface-level processing. It helps machines understand the intent and behavior of information.

Structure counts. Consistently defined information is easier for AI to understand and process. By contrast, unstructured information, by its nature, is more difficult for anyone to understand.

Standards communicate. The meaning of reported data is captured when it is expressed in a uniformly designed semantic data model like a taxonomy or ontology.

Together, the Legal Entity Identifier (LEI, ISO 17442) and eXtensible Business Reporting Language (XBRL) power improved results for regulatory disclosure programs.

As U.S. regulators develop digital asset market infrastructure regulation, the Global LEI Foundation (GLEIF) and XBRL US encourage agencies to incorporate statutory language on the use of identifiers and open data standards that express semantics. This approach is critical to promote efficiency and transparency, and to set agencies up to capitalize on the increasing capabilities of AI to boost efficiency and reduce costs.

Incorporating references to XBRL and to the LEI or the Financial Data Transparency Act's (FDTA) common data standard/identifier language into rulemaking will give regulators direction on the intersection between the legislation and the FDTA. Clearly naming the standards and identifiers required will also give digital market participants, from reporting entities to data consumers to software providers that support the ecosystem, clarity from the start on how to prepare and effectively comply.