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Trust Center FinanceGPT applications
Large Quantitative Models

Quantitative engines for financial calculation, simulation and risk.

FinanceGPT LQMs are governed quantitative model systems built over financial numerical data, mathematical relationships and financial structure. They produce reproducible outputs such as valuations, forecasts, risk measures, scenarios and optimizations.

View pricing Build an LQM LQM ecosystem
Model families
Deterministic finance
Cash flows, valuation, ratios and financial mathematics
Statistics & econometrics
Regression, distributions, forecasting and time-series methods
Risk & portfolio
Covariance, factor, VaR, optimization and portfolio construction
Simulation & generative
Monte Carlo, scenario generation and learned quantitative models
How it works

Models calculate. Evidence stays attached.

Financial dataStatements, market data, economic data and governed features.
Quant kernelDeterministic math, statistics and numerical methods.
LQM modulesRisk, pricing, forecasting, simulation and learned models.
EvaluationVersion, validation, lineage and reproducibility evidence.
OutputsForecasts, distributions, valuations, scenarios and optimizations.
Quantitative integrity

The language model is not the calculator.

FinanceGPT separates language interpretation from quantitative calculation. Financial outputs can carry the model, inputs, assumptions, version and execution evidence used to produce them.

Scale

“Large” describes the quantitative problem, not a marketing parameter count.

FinanceGPT uses the appropriate quantitative model family for the task, from deterministic financial mathematics to larger simulations and learned specialist models.

Build and publish

Take a governed LQM from training to a portable evidence-backed artifact.

LQM Builder seals training lineage, evaluates quantitative fidelity and robustness, emits a versioned Evidence Packet, and can publish an exact checkpoint with its model card, evaluation evidence, evidence schema and ML-BOM. Publication is separate from activation inside FinanceGPT.

Open LQM Builder docs
Train
Reproducible numeric/time-series model lineage.
Evaluate
Statistical fidelity and robustness gates.
Evidence
Versioned typed Evidence Packet contract.
Publish
Attributable model-supply-chain disclosure package.
Next layer

QLMs orchestrate LQMs, data, tools and policy.

Use language to express a financial question while FinanceGPT routes the quantitative work to inspectable model services.

Explore QLMs