Definition · AI in finance
Explainable AI
Explainable AI is methods that make a model's outputs interpretable to humans. For explainable AI, 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 XAI, interpretable AI
Why it matters
Understanding explainable AI 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 makes every figure explainable by construction: a number opens its definition, basis, calculation, and source records, not a post hoc rationalization.
In practice
Governance example
Teams use explainable AI 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 makes every figure explainable by construction: a number opens its definition, basis, calculation, and source records, not a post hoc rationalization.
In practice, teams should define explainable AI with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding explainable AI 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 makes every figure explainable by construction: a number opens its definition, basis, calculation, and source records, not a post hoc rationalization.
A strong workflow for explainable AI 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 makes every figure explainable by construction: a number opens its definition, basis, calculation, and source records, not a post hoc rationalization.
FAQ
What is explainable AI?
Explainable AI is methods that make a model's outputs interpretable to humans. For explainable AI, 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.
Why is explainability important in financial AI?
Understanding explainable AI 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 makes every figure explainable by construction: a number opens its definition, basis, calculation, and source records, not a post hoc rationalization.
Sources
- Explainable AI in Finance: Meeting Stakeholder Needs CFA Institute Research and Policy Centerrpc.cfainstitute.org
- Why Explainable AI is Critical for Financial Decision Making Corporate Finance Institutecorporatefinanceinstitute.com
- What is Explainable AI (XAI)? IBM https://www.ibm.com › think › topics › explainable-aiibm.com