AI Hallucination
An AI-generated output that is fluent and plausible but factually wrong or unsupported by the source material.
AI hallucination is the phenomenon where a language model produces an output that reads as authoritative but is either factually incorrect or invented entirely. It happens because language models are optimised for plausibility, not truth — they will compose a confident answer to almost any question, including questions where the model lacks grounding.
In consumer applications, a hallucination is an inconvenience. In regulated financial services, a hallucinated DDQ answer or a fabricated SoA citation is a compliance breach with discoverable consequences.
Hallucination is the single biggest blocker to AI adoption in regulated workflows. It is also the single most-solvable problem if the AI architecture is right: retrieval-augmented generation with mandatory citation to source means the AI cannot generate content not present in the source corpus, and every claim is traceable for the compliance officer.