Definition · AI in finance
Grounding
Grounding is anchoring model outputs in verified, retrievable source data. For grounding, 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 AI grounding, grounded AI
Why it matters
Understanding grounding 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. Grounding is Pluvo's core: every figure is computed from connected systems of record and traceable to source in a couple of clicks, never guessed.
In practice
Governance example
Teams use grounding 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
Grounding is Pluvo's core: every figure is computed from connected systems of record and traceable to source in a couple of clicks, never guessed.
In practice, teams should define grounding with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding grounding 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. Grounding is Pluvo's core: every figure is computed from connected systems of record and traceable to source in a couple of clicks, never guessed.
A strong workflow for grounding 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.
Grounding is Pluvo's core: every figure is computed from connected systems of record and traceable to source in a couple of clicks, never guessed.
FAQ
What is grounding in AI?
Grounding is anchoring model outputs in verified, retrievable source data. For grounding, 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.
How does grounding prevent hallucination?
Reduce risk around grounding by tying the workflow to approved data, explicit logic, review ownership, and an audit trail. The control should make the output explainable before it reaches reporting or decision-making.
Sources
- Definition: Ground. from 29 CFR § 1926.968 LII | Legal Information Institute https://www.law.cornell.edu › definitionslaw.cornell.edu
- Accounting for Land | Finance & Business - UC Davis UC Davis Finance and Businessfinanceandbusiness.ucdavis.edu
- Understanding Footings in Accounting: Definition, Process, ... Investopedia https://www.investopedia.com › ... ›investopedia.com
- Ontological grounding of accounting frameworks CEUR-WS.org https://ceur-ws.org › Vol-3618 › forum_paper_15ceur-ws.org