I’m Brian Weisberg, a startup CFO with 20 years of experience leading various venture-backed B2B SaaS businesses. OnlyCFO asked me to guest post a series of articles on how I am using AI in finance/accounting and some lessons I have learned.
For my first article, I wrote my playbook for leveraging AI in financial modeling.
How to build financial models with Claude
This past summer I interviewed for a role that was right in my wheelhouse and quickly found myself at the take-home stage. My homework assignment: build a revenue forecast with an accompanying outline for how I would pitch the business to a potential acquirer.
Easy enough. I have done this hundreds of times. And of course I was going to leverage AI to build a robust model.
The problem? I did steps 3 and 4 in reverse order because I was in a hurry, and then skipped step 5 entirely. This created a really bad model and I basically had to start over.
Below is the process that I should have followed (and that I do now)…
How I Built the Model:
1. “Model Builder” Project
I created a Project within Claude to hold the model context, files, and project instructions. You can think of a project as a self-contained workspace for related chat histories and knowledge bases. In this case, I packed 20 years of financial modeling best practices into a tidy, new Claude project.
Not sure where to begin with a project. That’s ok. Tell Claude what you are looking to do and it can help you workshop the instructions for your new project and even suggest potential pieces of context.
2. Building the Skills
Skills if you’re still new to the AI game are reusable instructions, scripts, and resources that dictate how to perform specialized, repeatable tasks. You can either build the skills from scratch or you can download pre-built ones. You can even customize pre-built skills to more squarely suit your needs.
From Scratch: Work with Claude/ChatGPT chat to build the skills you need to produce a good model.
Pre-Built Skills: One of the great things about AI being so popular is you don’t need to start from scratch. There are lots of smart people sharing skills they’ve built. For instance, I downloaded some skills that I saw an FP&A vendor promoting on a recent webinar and combined them with some of OnlyCFO’s model Skills from goClose (see Napoleon Dynamite characters below 😂)
3. Start with a sketch
This is my cardinal rule of model building.
Build a schema for the model in a spreadsheet and then you can give that to Claude so it knows what you’re generally looking for. It doesn’t need all the formulas, just the rows and columns to get an idea of what you’re thinking. This will save a ton of back and forth.
If you already have a working model, you can do the reverse. Ask Claude to audit your model and build a flowchart, showing you how it’s connected and test that it is fully wired.
4. Write the prompt
The prompt is where the assignment gets handed over: the target (quote it word for word), the timeframe, and the questions the model needs to answer. If you did step 3, this is mostly transcription…the prompt is easy because it is based on the sketch.
However, this was where I broke my own order. In my rush I skipped the sketch and dove right into hammering out a prompt where I effectively converted (or tried to convert) the assignment into written instructions for what I wanted Claude to build. Only to lose everything after about two hours of hyperactive typing and start from scratch to do it a second time.
5. Humans are for judgment calls, agents need details
I like to equate AI with a new puppy. It wants to please you… mostly. It wants to give you an answer more than anything. Which is great. Except for when it’s not. Our job as humans is to make judgment calls and deliver specificity to the agent.
Tell it exactly what you want. Tell it the knobs you want, how you want them designed, where to start for the assumptions, and how you want it stitched together.
You can even feed it benchmarks, industry norms, seasonality, ratios, and other trends you know about the business and then test its alignment with that data. AI agents can handle a lot of context. The more specific and structured, the better.
You’d be surprised about how quickly AI can organize those scattered thoughts into a series of quantitative checks to test the reasonableness of a model.
And then back-test the model to make sure it is working right. Have it recalculate a month you already lived through and confirm it lands on the number you actually reported.
6. One change per turn, and keep a change log
Compounding edits compound bugs.
When iterating with AI, limit feedback to one change per turn, with a confirmation of exactly which cells changed and one downstream output that responded. It can be tempting to load it up, but when you start trying to change too many things at once, the AI can get lost. Especially if you start making changes to the file and don’t detail every change.
Your colleagues will thank you as well. We all know what it’s like to receive a model from someone, only to try and diff it yourself to figure out what changed.
Regardless of whether you are having AI build, validate, or update a model for you, changelogs are the CliffsNotes of a model.
7. Make the model prove it’s alive
A model that looks right and a model that is right are different things.
My favorite test: nudge one input and confirm the output moves in the expected direction and by the expected amount. And then check it yourself. All models can be validated with some old-fashioned napkin math.
If you change the sales capacity ramp from three months to six, does that bring down your revenue forecast accordingly? If not, something is hardcoded where a formula should be.
This is one great place where Claude has a superpower to help test your model. You can give it a laundry list of scenarios to run, and it will synthesize the results. You don’t have to be building a new model to get value from this tip.
Another test you can run to prove it’s alive is to ask it to build an independent replica of the math in Python and compare it to the spreadsheet. When they disagree, one of the two is wrong, and finding out which teaches you something every time. Just beware that Python and Excel behave differently when it comes to rounding.
Final Thoughts
If you’ve made it this far, I hope you’ve learned something from this piece. I’d hate for you to make the same mistake I made this summer.
AI can be super helpful both building and validating financial models. But…only if you do it the right way. AI can also just make the model say whatever you want.
Hope you learned something and I’ll see you next week!
Footnotes:
Checkout goClose for a month-end close workspace, free AI Skills, and other templates.
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