Triggering Human Review When an LLM Output Contains Financial Figures

Define what counts as a financial figure

A trigger is only as consistent as its definition. Start with a written taxonomy that risk, compliance and the business own together, rather than leaving the definition to a prompt instruction or an engineer's regular expression.

A workable taxonomy covers balances, interest and other rates, returns, premiums, projections and totals. It should also say how each type is expressed: currency amounts, percentages, basis points, ranges, and derived values such as sums or year-over-year changes. A projection and a historical balance carry different risk, so the taxonomy should record the type and not only the presence of a number.

Financial services teams that already apply review rules to marketing or client communications will recognize the pattern. The difference with LLM output is volume and variability, which is why the definition needs to be machine-evaluable and versioned. FINRA's guidance describes building a layer of human review of model outputs so that results align with internal policies, procedures and risk appetite. The taxonomy is where that policy becomes concrete.

Detection approaches and where each one stops

Pattern matching is the usual floor. It is deterministic, cheap to run and easy to explain to an examiner. Its limits follow from its design: a rule written for digits and currency symbols will not match a figure written in words, and a rule applied to flat text may not see values inside tables or structured fields unless the output is parsed first. Test those cases explicitly and treat each one as a requirement, not an edge case.

Context-aware classification adds judgment. It can separate a policy limit quoted from a document from a projection the model produced, and it can assign a figure type. It also introduces a probabilistic component, so its confidence output needs a defined threshold and a defined behavior below that threshold. A common arrangement is a deterministic check as the automated floor and a classifier layered on top, with human review reserved for cases automation cannot resolve.

The provenance of a figure matters as much as its presence. A figure returned by a tool or retrieved from a system of record can be compared against that source. A figure the model generated has no such anchor. Capture which path produced each figure, because source grounding is one of the strongest signals for deciding whether a human needs to look.

Whichever method you choose, evaluate trigger conditions in a deterministic post-processing step that reads the model's structured output, not in the prompt. Prompt instructions can be ignored by the model. A post-processor cannot.

Design the trigger conditions

Detection answers whether a figure is present. The trigger decides whether a human must see the output. Predefine these conditions so the runtime gate can enforce them, and version them so a change is itself a recorded event. No source sets universal materiality or confidence cutoffs for LLM financial content, so your institution has to set and justify its own. FINRA's guidance offers an analogy in amount or threshold limits on trade orders.

• Figure type: Route projections, returns and rates to review more readily than a restated total that is grounded in a source record.
• Materiality threshold: Set amount or percentage limits above which any matching figure escalates, and document the rationale for each limit.
• Audience: Customer-facing output should face a stricter rule than internal drafts. Define the audience from the application context, not from the model's own claim.
• Source grounding: Escalate figures with no tool or document source. Allow grounded figures that match their source exactly, subject to the other conditions.
• Confidence: Where a classifier is used, define the confidence level below which the output is held, and what happens if the classifier fails to respond.

Evaluate whether a runtime control fits

Evaluate any approach against a short set of questions. Does it assess each interaction at runtime and hold the output before release? Are trigger conditions defined, versioned and changeable under change control? What happens on reviewer timeout? Can decision records be exported for examiners and internal audit? What testing evidence exists for figures in words, figures in tables, and tool-sourced versus model-generated figures?

Trussed is one way to meet these requirements. It enforces policy in the execution path and can allow, redact, block or escalate an individual interaction for human approval. Enforcement is at runtime, not reviewed after the fact, and it generates continuous audit-ready evidence mapped to frameworks including NAIC, NYDFS and NIST. It can be deployed self-managed in the customer's cloud, with configurable retention and data residency.

Trussed does not claim financial-services customers. Insurance, a regulated financial sector, is where it is deepest, and it has customers across four regulated verticals: insurance, pharma, healthcare and education. Its approved production proof includes 18+ months of continuous production in a regulated enterprise, 99.95% uptime on live production traffic, and 100+ models under one policy plane. Treat those as evidence of operating maturity, not as validation of your specific figure-detection logic. You should test that logic against your own content and taxonomy whichever control you choose.

As a practical next step, draft the taxonomy and a first set of trigger conditions, then run a sample of real outputs through them to see what would have been held and what the reviewer decision record would have contained.