How to Detect Hallucinations in Finance LLM Output
In finance a hallucination is rarely a wild fabrication; it is a plausible wrong number, a citation that does not say what the model claims, or a confidently stated figure pulled from the wrong line of a table. These slip past a human reader precisely because they look right. Detecting them requires mechanical checks that run on every output, not spot review. The checks that catch the failure modes that matter in finance are described below.
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Before You Start
Set up the inputs that make the next steps easier
Guide Steps
Move through it in order
Each step focuses on one decision so you can keep momentum without losing the thread.
- 1
Verify every numeric claim against the source
Extract each number the model states and check it against the source text it came from. The most common finance hallucination is a transcription error: the right concept with the wrong digits, a units mix-up, or the wrong row of a table. These pass human review because the surrounding prose is correct. A per-number check against the source catches them mechanically, which is the only reliable way given how confident and fluent the wrong number looks.
Pay special attention to figures from tables. Tables are where row, column, and units errors cluster, and where a human reviewer is least likely to catch a transposed value.
Use The ToolPlaygroundsHallucination Detector
Paste a source document + an LLM's extraction. Every numeric claim in the output is checked against the source. Client-side. Catches silent fabrication.
ToolOpen -> - 2
Check citation faithfulness
For every claim the model attributes to a source, confirm the cited passage actually supports it. Models occasionally cite a real passage that does not contain the claim, which is more insidious than a missing citation because it looks rigorous. A faithfulness check compares the claim to its cited evidence and rejects answers where the evidence does not back the statement. Grounding the model in retrieval is not enough; the citation has to be verified.
An unfaithful citation is worse than no citation, because it manufactures false confidence. Treat a citation that does not support its claim as a hard failure.
- 3
Recompute derived figures deterministically
Do not trust ratios, totals, growth rates, or projections the model computed itself, since multi-step arithmetic errors compound. Recompute every derived figure with a deterministic engine from the verified inputs, and compare it to the model's stated value. If they disagree beyond a tiny tolerance, surface the mismatch. The model should present checked numbers, not produce them, which is the role it is actually reliable in.
Set a tight numerical tolerance and surface disagreements rather than silently overriding. A silent override can hide a real problem in the inputs you would otherwise catch.
- 4
Validate the output structure
When the output is structured, validate it against a schema before anything reads it: required fields present, types correct, values within sane ranges, units as expected. A figure outside a plausible range or a missing field is a cheap, mechanical signal that something went wrong. Structural validation will not catch a plausible wrong number, but it catches the malformed and the absurd at near-zero cost, before the more expensive checks run.
Add range sanity checks, not just type checks. A margin of 400 percent or a negative share count passes a type check but fails a sanity check, and that is exactly the kind of error you want to stop.
Use The ToolPlaygroundsStructured Schema Validator for Finance
Paste LLM JSON output and validate against four pre-built finance schemas — research output, trade decision, risk snapshot, peer comparison — with sanity.
ToolOpen -> - 5
Flag unsupported claims for human review
Any claim that fails a check, lacks a verifiable citation, or disagrees with the deterministic recomputation should be flagged and routed to a human rather than passed through. The goal is not zero hallucinations, which is unachievable, but zero unreviewed hallucinations reaching a decision. A pipeline that surfaces its own uncertain outputs and gates the rest is trustworthy; one that lets everything through and hopes is not.
Track the flag rate over time. A rising rate is an early warning that the model version, the prompt, or the source data changed.
Common Mistakes
The misses that undo good inputs
Relying on human review to catch number errors
A plausible wrong number embedded in correct prose is exactly what human reviewers miss. The fluent, confident presentation defeats spot-checking, which is why numeric verification has to be mechanical and run on every output.
Accepting a citation without checking it supports the claim
Models can cite a real passage that does not contain the stated claim. An unverified citation manufactures false confidence and is more dangerous than no citation at all.
Letting the model compute the numbers that matter
Multi-step arithmetic errors compound and are stated with full confidence. Any figure that feeds a decision must be recomputed deterministically and compared, not trusted because the model produced it.
Try These Tools
Run the numbers next
Price-Blind Research Auditor
Paste a research prompt or agent context bundle. The auditor flags price numbers, directional words, and outcome-leaking phrases that cause LLMs.
Prompt Regression Tester
Run the same prompt against multiple models (Claude 4.5/4.6/4.7, GPT-5, Gemini 2.5) with your own keys. Diff outputs, score drift, catch regressions.
FAQ
Questions people ask next
The short answers readers usually want after the first pass.
Sources & References
- Survey of Hallucination in Natural Language Generation — Ziwei Ji et al., ACM Computing Surveys (2023)
- Evaluating Verifiability in Generative Search Engines — Liu, Zhang, Liang, EMNLP (2023)
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