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
Your version of this lesson adapts to your role. After the 3-minute assessment, examples, scenarios, and exercises are tailored specifically to your job function and experience level.
Personalise →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:
- ■Arithmetic: Does every calculated number add up correctly? AI occasionally makes calculation errors, especially with large numbers or multiple-step calculations.
- ■Logic: Does the conclusion follow from the data? An AI might present a technically correct observation that leads to a wrong conclusion.
- ■Completeness: What did the AI not mention? Omissions in financial analysis can be as consequential as errors.
- ■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.
Key Takeaways
- ■Treat AI output as a capable but fallible analyst's work — review it, don't accept it
- ■Labelling data fully (column definitions, period, currency, known anomalies) prevents most AI analysis errors
- ■Check arithmetic, logic, completeness, and causation claims in every AI-generated financial analysis
- ■Omissions can be as consequential as errors — ask "what didn't the AI mention?" for every analysis
- ■Any number appearing in an external document must be verified against source data before publication
Before you practise
What is one specific task in your current role where you could apply what you just learned?
Ready to put it into practice?
Apply what you just learned with a hands-on exercise.