2.2 The FDTA Context

Return to 2. Background and Motivation

The Financial Data Transparency Act (FDTA), enacted in 2022, establishes a statutory framework for improving the transparency, usability, interoperability, and consistency of financial regulatory information reported to participating United States federal agencies [R1]. The Act directs those agencies to establish coordinated data standards and to apply those standards through subsequent agency-specific rulemaking or other authorised actions. To achieve these objectives, the FDTA relies upon open standards and coordinated standard-setting while deliberately avoiding prescription of specific technologies, data models, products, or implementation approaches [R1, R2].

This approach reflects an important policy judgment. Meaningful transparency and comparability do not result solely from adopting particular tools, file formats, schemas, taxonomies, or platforms. They depend upon the consistent interpretation of reported information across organisations, reporting periods, jurisdictions, and independently developed systems [R10].

In practice, many agency implementations of the FDTA joint data standards employ structured, tagged reporting formats, such as XBRL, to support machine-readable reporting and automated information exchange [R8, R9]. These technologies play an essential role in standardising submission and enabling automated processing. However, structural conformance alone does not guarantee semantic consistency. Two reports may conform to the same schema yet convey information that is interpreted differently due to differences in definitions, contextual assumptions, classification rules, temporal scope, governing authority, or calculation logic [R8, R9].

This condition gives rise to the semantic interpretation problem. Reported information is rarely self-interpreting. Its meaning depends upon explicit and implicit semantics, including business concepts, regulatory definitions, contextual qualifiers, governing rules, and version context. When organisations embed these semantics informally within documentation, software, configuration, analytical workflows, or implementation conventions, comparability, traceability, auditability, and reproducibility degrade over time [R10]. The challenge intensifies as reporting requirements evolve and artificial intelligence-assisted extraction, classification, and interpretation techniques are integrated into reporting environments.

The FDTA emphasis on comparability, interoperability, and regulatory oversight therefore implies the need for more than standardised reporting formats or exchange mechanisms. It requires an architectural approach that makes semantic interpretation explicit, traceable, governed, and reproducible while remaining sufficiently flexible to support heterogeneous implementations, independently governed organisations, and the controlled evolution of standards over time [R10].

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