← Quantitative Proof QUANTITATIVE PROOF · FINANCIAL MODELLING

From financial evidence to scenario analysis without making language the calculator.

A FinanceGPT financial model can bind source financial information and explicit assumptions to deterministic model calculations, then add scenario and sensitivity intelligence while preserving the distinction between calculated outputs and language explanation.

Evidence typeGoverned workflow demonstration Action authoritySeparate

THE QUESTION

How can a finance team test downside, base and upside assumptions while keeping the model, evidence and explanation reviewable?

THE EVIDENCE PATH

01Financial evidence

02Model assumptions

03Deterministic calculation

04Scenario & sensitivity

05Evidence-backed explanation

06Human review

Observed / supplied evidence

What must be available.

Source financial statements, imported data or other finance inputs available to the workspace.
Explicit model assumptions such as revenue growth, margins, working capital, financing or valuation inputs.
Period, currency and source context needed to interpret the financial information correctly.

Calculated

What owns the numbers.

Financial statements, ratios, forecasts, valuation and scenario values are produced by the quantitative model logic rather than invented by the language layer.
Scenario outputs remain tied to the assumptions used to produce them.
Sensitivity analysis changes defined inputs and recalculates the model instead of asking a language model to estimate the answer.

Generated

What AI may generate.

FinanceGPT can explain calculated differences, highlight material drivers and identify evidence gaps.
Generated commentary remains interpretation of the model output; it does not become the numerical source of truth.

Authority

What governs what happens next.

Model inputs and assumptions remain available for review.
Scenario intelligence can be sealed or fingerprinted where the workflow requires integrity evidence.
Human judgment remains required for material assumptions and financial decisions.

WHAT THIS DEMONSTRATES

Evidence-supported conclusions.

Language-programmable financial modelling with numerical authority retained by the model.
A visible chain from source information to assumptions, calculations, scenarios and explanation.
A reviewable boundary between calculation and generated narrative.

WHAT THIS DOES NOT CLAIM

Boundaries stay explicit.

It does not claim that a language model can replace a financial model.
It does not claim that scenario outputs are forecasts of what will happen.
It does not create authority to execute a Financial Action.