Definition · AI in finance
Fine-tuning
Fine-tuning is further training a base model on domain data. For fine-tuning, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
Also known as model fine-tuning, supervised fine-tuning
Why it matters
Understanding fine-tuning matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo gets domain accuracy from a connected semantic model and ontology rather than fine-tuning a model on your data, keeping numbers computed and verifiable.
In practice
Governance example
Teams use fine-tuning when they evaluate whether an AI-assisted analysis can be trusted. The useful test is whether the output is tied to approved data, repeatable logic, human review, and an audit trail.
Pluvo example
Pluvo gets domain accuracy from a connected semantic model and ontology rather than fine-tuning a model on your data, keeping numbers computed and verifiable.
In practice, teams should define fine-tuning with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding fine-tuning matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo gets domain accuracy from a connected semantic model and ontology rather than fine-tuning a model on your data, keeping numbers computed and verifiable.
A strong workflow for fine-tuning separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
Pluvo gets domain accuracy from a connected semantic model and ontology rather than fine-tuning a model on your data, keeping numbers computed and verifiable.
FAQ
What is fine-tuning a model?
Fine-tuning is further training a base model on domain data. For fine-tuning, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
Fine-tuning vs RAG: which should you use?
To use fine-tuning, start with the decision, then confirm the source data, timing, calculation logic, and owner. The analysis is strongest when a reviewer can trace the answer back to the records that produced it.