Near-Miss Reporting

Near-miss reporting is the practice of documenting AI system incidents that almost caused harm, such as a model producing a flawed output that was caught by a human reviewer before reaching a customer or regulator. Organizations use these records to spot recurring weaknesses in their AI systems, controls, and oversight processes before they turn into full-blown failures. For compliance and risk teams, a well-run near-miss program also creates evidence that the organization is actively monitoring its AI and learning from problems, which regulators and auditors increasingly expect to see.

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EnforcementUK2026-10-20

FCA Says AI Near-Misses Must Be Reported Under Existing Rules

The UK Financial Conduct Authority has signaled that mandatory incident reporting rules already require firms to report significant AI failures and near-misses. FCA chief data officer Jessica Rusu cited agentic AI systems as a particular concern. She warned that consumer harm, cyber vulnerabilities, and operational failures can occur faster and at greater scale as AI autonomy increases. UK-regulated financial services firms must now treat AI near-miss capture as an active compliance obligation, not a future requirement.

Financial Conduct Authority incident reporting agentic AI financial services operational resilience near-miss reporting UK