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 Finance team at a Statutory Boards & Agencies firm asks AI tools to establish how many countries were paying IMF surcharges before the October 2024 reform, the AI returns 19 rather than the correct 20 — and cites a specific IMF press release to support the incorrect figure. Any internal analysis, MI pack, or sovereign-risk briefing built on that baseline will carry a wrong headline statistic that contradicts the IMF's own published documentation.
The practical exposure for the firm is a wrong-deliverable risk: credit assessments, country-risk tierings, or board briefings that reference the reform's scope will mis-describe the number of countries relieved from surcharge obligations (8, not 9 on the AI's version). In contexts where the firm's analysis is reviewed alongside IMF communications — by regulators, auditors, or sophisticated counterparties — the discrepancy signals inadequate primary-source verification in the Finance team's AI governance practices.
Every finding on this page compares an AI subject's account of the rule against the regulator's verbatim text from the regulator's own portal. Both are linked. Each delta, its root causes, and impact analysis are documented and published with immutable Citation IDs.