Design the schema around what the next system must consume: define the model’s periods, financial concepts, units, assumptions and provenance, then make required fields and allowed values explicit. Use provider-supported structured output to constrain generation, validate the parsed JSON in your application, and add financial checks separately. A schema can make an answer structurally valid; it cannot prove the forecast is true or its calculations are correct.
Decide what the model’s consumer needs
Start with the application that will read the output, not with a long inventory of JSON Schema keywords. A model used to populate a dashboard may need period-by-period facts. A workflow that reviews forecasts may also need assumptions and source notes. Choose a representation that makes those tasks easy and defines what each number means.
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Give each financial fact its context
A bare value such as 1250000 is ambiguous. A usable fact should identify its concept, period, value, unit or currency, and whether it is an actual, forecast or assumption. If your model handles more than one reporting currency, record the currency with the fact rather than relying on a global default. Define scale and unit conventions too: for example, whether a value is in whole dollars or thousands of dollars, and whether a margin is stored as a fraction or a percentage.
For example, a revenue fact might contain the concept revenue, period FY2027, value 1250000, currency USD and basis forecast. This is one possible design, not a universal financial-model standard. You could instead nest line items under periods or use statements with periods as columns; prefer the structure whose meanings and validation rules are clearest to your consumer.
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Define periods and missing-data behavior
Document exactly what period labels mean. If you use start and end dates, specify whether the end date is inclusive; if you use labels such as FY2027, define the fiscal-year convention elsewhere in the contract. State whether actual and forecast periods can coexist, how many periods are expected, and what counts as a missing fact. Do not make zero, null and omission interchangeable: zero is a value, while null or an absent field should have a defined meaning in your application.
Make the contract explicit
Use stable field names, clear types, and required fields for information the consumer cannot safely infer. Use enumerations when the allowed choices are genuinely closed, such as actual, forecast and assumption. Add bounds or formats only when they match the data and are supported by the generation provider you selected. Descriptions help clarify important fields; OpenAI’s Structured Outputs guidance also recommends clear keys, descriptions for important fields and evaluations to find a structure that works for the use case.
This abbreviated schema illustrates a fact-level contract. A production contract would normally also define the surrounding model metadata, period list, assumptions and provenance fields required by its consumer.
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{
"type": "object",
"properties": {
"concept": { "type": "string", "description": "Stable financial line-item identifier, such as revenue." },
"period": { "type": "string", "description": "Period identifier defined by the model contract, such as FY2027." },
"value": { "type": "number", "description": "Numeric value in the declared unit and scale." },
"currency": { "type": "string", "description": "Currency code when the fact is monetary." },
"unit": { "type": "string", "description": "Unit or scale convention, such as USD or percent." },
"basis": { "type": "string", "enum": ["actual", "forecast", "assumption"] },
"source_note": { "type": "string", "description": "Source or explanation needed to trace the value." }
},
"required": ["concept", "period", "value", "unit", "basis", "source_note"],
"additionalProperties": false
}The example requires a source note and unit for every fact, while currency is not required because some units, such as percentages, are not monetary. Adapt those choices to your data: if every fact is monetary, require currency; if a note is not necessary for every line item, define a consistent alternative rather than letting the model guess. Consider adding a schema version and a model identifier to the full output envelope so saved results can be interpreted against the contract that produced them.
Separate generation constraints, structural checks and financial rules
| Layer | What it checks | What it does not establish |
|---|---|---|
| Provider-side structured generation | Whether the model is guided to produce output conforming to a supported schema. | Whether its inputs, forecasts or calculations are financially correct. |
| Application-side schema validation | Whether parsed data has the expected fields, types and permitted values. | Whether periods, currencies or arithmetic make sense together. |
| Financial-domain validation | Whether application-defined business rules hold, such as subtotal arithmetic or period continuity. | Whether an assumption is economically justified or a source is trustworthy. |
Check provider compatibility before relying on a keyword
Structured-output features are implementation-specific. OpenAI documents a supported subset of JSON Schema rather than the entire specification, so check the current documentation for the provider, mode and keywords you plan to use. Do not assume a schema accepted by a general-purpose validator will also be accepted by a provider’s constrained-generation feature.
Validate the parsed response in your application
Treat provider formatting as one constraint, not your only safeguard. Parse the response and validate it against the versioned contract before passing it to downstream code. Handle refusals, incomplete output, truncation, transport failures and validation errors as explicit failure states; an incomplete response must not be treated as a complete model. OpenAI’s Structured Outputs guidance describes refusals and incomplete output as cases callers need to account for.
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Add checks that express the model’s financial logic
Ordinary schema conformance does not establish relationships between values. Implement the rules your use case requires, such as:
- Periods are in the expected order, with no duplicate or missing periods.
- Every required line item appears once per applicable period.
- Currency, scale and unit are consistent wherever values are compared or combined.
- Sign conventions are respected, such as whether expenses are positive or negative.
- Subtotals and formula-derived lines reconcile within a defined tolerance.
- Actual, forecast and assumption values are labeled consistently and meet any transition rules.
These are application rules, not consequences of having valid JSON. They also cannot establish whether a forecast assumption is reasonable; source controls and appropriate human review may still be needed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Version and evaluate the contract
Define and version the output contract before production use. Record required and optional fields, nullable behavior, accepted units and period conventions. When a schema changes, treat the change as an interface change: test downstream consumers and decide how older saved outputs will be handled.
Evaluate both representative inputs and cases designed to expose ambiguity. Useful cases include missing assumptions, contradictory units, negative values, unusual periods, duplicated line items and incomplete responses. OpenAI’s guidance recommends evaluations when choosing an output structure. Keep enough version and provenance information to trace which contract and assumptions produced each model.
Know when JSON Schema is not enough
For an internal application contract, JSON Schema can describe the shape and basic constraints of the data. If the output must become a formal financial or regulatory report, investigate the applicable reporting requirements and XBRL taxonomy rather than treating a generic schema as a substitute.
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XBRL taxonomies define reporting concepts and metadata, including dimensions, and requirements can range from flexible GAAP-based reporting to prescribed regulatory tables. XBRL International describes layered validation in its reporting context: “Data quality can be greatly enhanced through multiple layers of validation.” Its xBRL-JSON format is a standardized JSON-based representation of an XBRL report, defined through mappings from the Open Information Model. It is relevant when the reporting workflow calls for XBRL semantics, not a universal schema recipe for AI-generated financial models.
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