Building Financial Models with AI
Financial modeling — projecting a company's future financials to inform a decision or valuation — is where finance professionals spend enormous time. AI can accelerate the structure, logic, and even the formulas of a model, but here more than anywhere the Module 2 lesson is law: the actual computation belongs in a spreadsheet, not in the language model's predicted text. This module shows you how to use AI to build better models faster without letting it fabricate your numbers.
What a financial model actually is
A financial model is a structured set of assumptions and the calculations that flow from them, usually built in a spreadsheet, projecting future financial outcomes. The most common is the three-statement model — projecting income statement, balance sheet, and cash flow together so they remain internally consistent. From there you get derived models: discounted cash flow (DCF) for valuation, scenario models, budgeting models, and so on.
The core truth of modeling: a model is only as good as its assumptions. The math is the easy part. The judgment about what assumptions to make — growth rates, margins, capital needs — is the entire game. This is exactly the split that determines how to use AI.
Use AI for the structure and logic of the model — what line items to include, how they relate, what drives what, what formulas to use. Keep the actual calculation in a spreadsheet (Excel, Google Sheets) or in executed code. Never let the language model be the calculator for a model that informs a real decision.
How AI accelerates modeling
The spreadsheet-AI workflow
The most robust modeling workflow keeps each tool in its lane. Tools like Claude for Excel and Gemini in Google Sheets are bridging the gap — they can operate on your actual spreadsheet — but the principle holds: real numbers live in the sheet.
| Step | Tool | What happens |
|---|---|---|
| 1. Design structure | Claude / ChatGPT | AI proposes line items, drivers, and statement links |
| 2. Build in spreadsheet | Excel / Google Sheets | You construct the model; real formulas compute real numbers |
| 3. Generate formulas | AI assist in-sheet | AI writes specific formulas you verify and place |
| 4. Run scenarios | Spreadsheet | Change assumptions; the sheet recomputes deterministically |
| 5. Interpret & write up | Claude | AI helps explain what the outputs mean |
Asking an LLM to "build me a 5-year DCF for this company" and accepting the numbers it types out. It will produce a beautifully formatted model with completely fabricated intermediate calculations. The output looks like expert work and may be arithmetically wrong throughout. If the model matters, the math happens in a spreadsheet or executed code — full stop.
Sensitivity and scenario thinking
The most valuable thing a model does is show how outcomes change as assumptions change. AI is excellent at helping you think through which variables matter most and what scenarios to test.
"My valuation is most sensitive to the discount rate and the terminal growth rate. Help me design a sensitivity table and reasonable bull, base, and bear scenarios for each." Then you build and run that sensitivity in the spreadsheet, where the recalculation is real.
The point of a model isn't the single number it spits out — it's the structured thinking about what drives the business and how outcomes range under different assumptions. AI helps you build that thinking faster and document it better. But the assumptions, and the judgment about which scenario is most likely, are yours. A model with someone else's assumptions is someone else's model.
Next
Module 6 focuses on one of the highest-value AI applications in all of finance: rapidly analyzing earnings reports, SEC filings, and other long financial documents — turning hours of reading into minutes of focused insight.