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AI Fundamentals for Finance· 17 min read·Personalise for your role →

Data Literacy Basics for AI-Assisted Finance

Understand how to prepare financial data for AI, interpret AI-generated analysis critically, and spot when the AI has made an error. Analytical skepticism is your most important asset.

In practice: Variance analysis: half a day → 30 minutes

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The Critical Reader Mindset

Working with AI in finance requires a specific mindset: use it as a capable but fallible analyst whose work you review, not a reliable authority whose output you accept. The discipline of critical review is what separates finance professionals who use AI well from those who create risk.

Preparing Data for AI

AI works best with clean, contextualised data. Before sharing any data:

Label everything. "Column A is revenue in GBP, Column B is revenue in USD at spot rate, Column C is units sold" — don't make the AI guess.

State the period and currency. "This is monthly revenue for FY2024, January-December, in thousands GBP."

Flag anomalies you already know about. "Month 7 shows a one-time restructuring charge of £2.3M. Exclude this from trend analysis."

Provide benchmarks. "Our industry average gross margin is 42%."

This context prevents the most common AI analysis errors.

Reading AI-Generated Financial Analysis

For any AI-generated financial analysis, check:

  1. Arithmetic: Does every calculated number add up correctly? AI occasionally makes calculation errors, especially with large numbers or multiple-step calculations.
  2. Logic: Does the conclusion follow from the data? An AI might present a technically correct observation that leads to a wrong conclusion.
  3. Completeness: What did the AI not mention? Omissions in financial analysis can be as consequential as errors.
  4. Causation vs correlation: AI often identifies correlations and presents them as explanations. Question every causal claim.

The Verification Rule

Any specific number in an AI output that will appear in an external document must be verified against source data. No exceptions. The time to verify is before publication, not after a board member finds an error.

Why data context prevents analysis errors — see it in action

The prompt

Here's my revenue data. What are the trends? [pastes table with no labels or context]

Claude's response

Looking at your data, the numbers show an overall upward trend with some variation. The highest value appears in position 7 and there is a notable dip around position 4. Overall the trajectory appears positive, though there is some volatility worth monitoring. The data suggests growth is occurring, particularly in the second half of the dataset.

Quick check

An AI tool confidently states that a competitor's gross margin improved by 340 basis points last quarter. What should you do before including this in a board presentation?

Key Takeaways

  • 1Treat AI output as a capable but fallible analyst's work — review it, don't accept it
  • 2Labelling data fully (column definitions, period, currency, known anomalies) prevents most AI analysis errors
  • 3Check arithmetic, logic, completeness, and causation claims in every AI-generated financial analysis
  • 4Omissions can be as consequential as errors — ask "what didn't the AI mention?" for every analysis
  • 5Any number appearing in an external document must be verified against source data before publication

Your challenge this week

Apply what you learned in a real task

Take a dataset you've already reviewed manually this month and paste it into Claude with full context labels, period, currency, and any known anomalies. Ask for the four-point analysis: trends, anomalies, correlations, and causal hypotheses. Note what it found that you hadn't.

Starter prompt · paste into Claude
Here is [description of dataset] covering [period] in [currency/units]. Column definitions: [list each]. Note: [any known anomalies or one-off items]. Please identify: (1) the main trend, (2) any anomaly, (3) any correlation between columns, and (4) the three most plausible explanations for [specific pattern you want explained].

Before you practise

Think about a time you included a number in a report that later turned out to be wrong. Where did the error come from — source data, calculation, or assumption? How would you catch that same error if it came from an AI tool rather than your own spreadsheet?

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Put it into practice

You've read the lesson — now apply it in a guided hands-on exercise. It takes about 5 minutes.

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