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Research Services - Benchmarking the Value of Xbrl Tender - 144795484

The THE FINANCIAL REPORTING COUNCIL LIMITED has issued a Tender notice for the procurement of a Research Services - Benchmarking the Value of Xbrl and Structured Data in an Ai-Driven Reporting Environment in the United Kingdom. This Tender notice was published on 09 Jul 2026 and is scheduled to close on 31 Jul 2026, with an estimated Tender value of Refer Document. Interested bidders can access detailed Tender information, eligibility criteria, and complete bidding documents by referencing TOT Ref No. 144795484, while the tender notice number is FRC2026-063 and Registering on the platform.

Expired Tender

Procurement Summary

Country: United Kingdom

Summary: Research Services - Benchmarking the Value of Xbrl and Structured Data in an Ai-Driven Reporting Environment

Deadline: 31 Jul 2026

Posting Date: 09 Jul 2026

Other Information

Notice Type: Tender

TOT Ref.No.: 144795484

Document Ref. No.: FRC2026-063

Competition: ICB

Financier: Self Financed

Purchaser Ownership: Public

Tender Value: Refer Document

CPV Classification

73000000 - Research and development services and related consultancy services

Purchaser's Detail

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Tender Details

The Financial Reporting Council (FRC) regulates auditors, accountants and actuaries and sets the UK's Corporate Governance and Stewardship Codes. As the Competent Authority for audit in the UK, we set auditing and ethical standards and monitor and enforce audit quality. The FRC is seeking to strengthen its understanding of how structured data performs in an AI-driven reporting environment. This aligns with the FRC's wider technology and digital reporting agenda, including work to understand the opportunities and risks arising from AI and to support high-quality digital reporting. The FRC is seeking to commission research services to benchmark the value of XBRL in an AI-driven reporting environment. The study will assess how structured data, including XBRL, performs in comparison with equivalent disclosures presented in PDF and HTML formats. The research should use clearly evidenced measures, including accuracy, completeness, consistency and explainability, to compare how AI models interpret, extract and reason from disclosures in different formats. The findings will support the FRC's work on digital reporting, structured data quality and the usability of corporate reporting information by preparers, users, regulators and technology providers. This evidence gap is central to the proposed study. The FRC requires robust comparative evidence on AI performance across equivalent disclosures presented in XBRL, PDF and HTML formats. The FRC requires the appointment of a suitably qua...

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