Hallucination Risks in AI-Generated Financial Analysis
Grounding AI in cited sources beats catching fabrications after the fact.
Grounding AI in cited sources beats catching fabrications after the fact.
Financial agents need the right number for the right period from authoritative, verifiable sources.
Agentic pipelines deliver audit trails, not just answers, by grounding claims in live sources.
Verifiable sources, not plausible numbers, must anchor AI financial claims.
Preparing, detecting, and synthesizing disclosures in sequence preserves evidence and timing.
Architecture that embeds citations throughout reasoning, not just at output.
Match cache TTLs to how fast your search data actually changes.
Structuring queries by type—not just volume—retrieves the evidence agents need to answer completely.
Pick freshness over speed: query-time retrieval wins for changing facts.
Uniting ingestion, retrieval, and inference into one fast system keeps AI decisions actionable.
Deliberate curation at each pipeline stage determines what knowledge models can actually learn.
Separate source change, indexing, and retrieval timestamps to measure true freshness.