Use generative AI for financial analysis

Direct answer

For this review, separate useful finance use cases from risks: summarization, variance commentary, anomaly detection, scenario drafting, and control review.

Why this question comes up

CFOs want practical use cases, not hype. This guide turns that concern into a review that can be repeated with a defined period, consistent inputs, and a visible trail back to the records.

Records to gather

• The source records the analysis will draw on
• Reconciliation status of those records
• The specific question being asked
• Materiality thresholds for what warrants investigation
• Who reviews the output before it is used

Review workflow

1. Reconcile before you analyse. Analysis on unreconciled books produces confident, well-written, wrong answers. This is the single most important step and the most commonly skipped.
2. Ask specific questions. “How are we doing?” produces generic commentary. “Which expense accounts moved more than $5,000 versus last month, and what transactions drove them?” produces something you can act on.
3. Demand the source trail. Any material figure should be traceable to accounts and transactions. An answer you cannot verify cannot be relied on.
4. Watch for confident interpolation. Where data is missing, models tend to produce a plausible figure rather than flag the gap. Ask explicitly what was unavailable.
5. Separate fact from inference. Recorded figures, calculated results, and interpretation are three different things and should be labelled as such.
6. Keep review with a person. Nothing goes to a client, lender, or board without a human who understands the numbers checking them. The tool carries no professional responsibility.

What a useful answer should include

• Confirmation that source records were reconciled first
• A specific question, with period and entity stated
• A traceable path from each material figure to source records
• Explicit notes on missing, incomplete, or excluded data
• Facts, calculations, and interpretation separately labelled
• A named human reviewer before the output is used

Common failure modes

• Analysing unreconciled books. Well-written wrong answers, delivered confidently.
• Vague prompts. Generic commentary that reads well and says nothing.
• Accepting untraceable figures. If it cannot be verified it cannot be used for anything consequential.
• Missing the silent gap. Models fill absent data with plausible values unless asked what was missing.
• Sending output unreviewed. The tool has no professional accountability; the person who sends it does.

Community context

The linked community posts show why people search for this topic and which parts create confusion in practice. They are anecdotal. Use the reference sources and your organization’s policies for accounting treatment, tax, compliance, and final decisions.