MODEL GOVERNANCE
Control model evaluation, promotion, evidence and lifecycle across finance.
Govern quantitative and language-model capabilities through evaluation, evidence, promotion, drift review and lifecycle controls instead of treating every available model as production-approved.
THE FINANCIAL PROBLEM
Why this use case matters.
Financial institutions need to know which model is approved for which task, what evidence supports it and whether performance or policy has changed since approval.
OUTCOMES
What FinanceGPT helps the team achieve.
Track evaluation, drift and retraining evidence
Keep publication and promotion as separate governed states
WORKFLOW
How the use case moves through FinanceGPT.
The sequence keeps analysis, deterministic calculation, AI assistance, human review and financial authority visible as separate responsibilities where they apply.
FINANCEGPT CAPABILITIES
Capabilities used in this workflow.
- LQM registry and evaluation
- Evidence Packets and ML-BOM
- Promotion controls
- Drift and retraining
- Public LQM evidence where published
RELATED FINANCEGPT PATHS
Go deeper from this use case.
FAQ
MODEL GOVERNANCE with FinanceGPT.
What models does FinanceGPT govern?
FinanceGPT includes governance for Large Quantitative Models, Quantitative Language Model composition and other managed model/runtime choices used by the platform.
Are model publication and promotion the same?
No. Public publication evidence and internal promotion for permitted runtime use are separate states.
Can model drift affect production use?
FinanceGPT includes drift, retraining and lifecycle controls that can support review or suspension according to configured governance.
Move from a financial problem to governed work in FinanceGPT.
Use the audience and platform paths to evaluate the capabilities, evidence and controls relevant to your organisation.