This is the consolidated view of findings. Click the Citation IDs or 'see details →' on any item for the full details for each finding.
When a Risk team at a Statutory Boards & Agencies firm uses AI to source CPMI fast payment system landscape data for a market briefing or regulatory mapping exercise, the AI produces two compounding errors: it presents a monitoring-survey respondent count as the global operational universe figure, and it falsely asserts that the central bank / private operator split is absent from accessible public CPMI sources. A briefing or executive summary carrying these errors misrepresents both the scale and the governance structure of the global FPS ecosystem to internal decision-makers or external counterparts.
In a sector where Risk functions routinely produce analysis shared with or submitted to regulatory bodies — including the very bodies that publish the source data — a demonstrably wrong CPMI figure traceable to AI research undermines the firm's credibility and may require formal correction, with the reputational and relationship costs that follow in a closely supervised environment.