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.