Cashboard is starting a series of interviews with finance leaders who are using AI in their day-to-day work, hosted by me (Julian Rowlands, Cashboard’s founder/CEO). The idea is to share highly practical AI applications that other finance leaders can learn from.

First up: Jesse Rubenfeld, founder and CEO of FinOptimal.

Jesse was previously Controller of D. E. Shaw Research and CFO of LimeWire. Before AI, he taught himself Python and used it to automate massive chunks of his accounting workload. Today, he runs FinOptimal, a tech-enabled accounting firm and software company.

Jesse’s the only accountant-engineer hybrid I know, and has been at the forefront of applying technology to finance for years. We’ve known each other for a long time, and FinOptimal runs bookkeeping for Cashboard, so I trust him tremendously.

Without further ado, please enjoy my interview with Jesse, which covers how he uses AI internally within FinOptimal’s finance function.

If you only read one thing: here are Jesse’s 4 takeaways for finance leaders

• Your data must be correct in order for AI to be useful

• The better your data (dimensional tags, etc), the more AI can help you

• You can automate a ton of bookkeeping with non-AI software. If you’re on QBO, Jesse’s firm FinOptimal can help you there.

• Find a way to feed all your data into tools like Claude (and ideally make that access permanent). It can do incredible analysis and the automations you can build on top are getting really powerful.

Thanks for joining us, Jesse. Before we talk about AI, it would be great to understand what your finance stack looks like.

So, QuickBooks is our system of record; we track time in Harvest; and payroll runs through a third party on the iSolved platform. Then I gravitate towards Google Sheets.

QuickBooks is updated constantly with all my accruals and journal entries. Our close process is almost fully automated. That’s not AI, but rather FinOptimal’s own software at work. We have a bunch of internal names for these workflows, including Wrangler, Payroller, Booker, Editor, and Allocator.

Basically, we use our own products (Wrangler and Payroller, specifically) to pull data from Harvest and iSolved into a Google Sheet. That GSheet auto-calculates every person’s fully loaded cost, and allocates it by customer, team, etc according to the hours they’ve tracked.

Then our Booker tool pushes the resulting journal entries into QBO on a schedule. We also have a tool called Editor that lets us bulk-modify QBO transactions via a spreadsheet.

The result of this is a QuickBooks dataset that basically self-closes in realtime. And it’s super detailed – I can even filter my P&L by customer and see my gross margin for each.

Where does AI fit into your processes?

Let’s start by talking about what we aren’t doing.

AI doesn’t auto-write anything to our books. Our automated booking workflows are deterministic.

I don’t want to introduce any probabilistic or generative inputs. I want absolute control over my books, and absolute certainty of what gets recorded. Automating the bookkeeping is a job for clear, controlled scripts and software-driven workflows.

We use AI in our internal finance function in a variety of different ways:

• Composing SQL queries for reporting and building workflows using natural language instead of painstaking trial and error (to reconcile month-end MRR with actual monthly accrual SaaS revenue, for example)

• Analyzing the financials with a focus on month-on-month fluctuations, basically variance detection and explanation

• Reconciliation of, say, Stripe balance transactions (as downloaded from Stripe directly) versus our books’ Stripe account (to figure out why our balance doesn’t equal theirs)

Basically, we’ve piped our QuickBooks (and other) data into a PostgreSQL database, and then connected that database to our Claude Cowork instance.

QuickBooks has an MCP offering through its partnership with Claude, but we actually use FinOptimal’s own MCP server (since we dog-food our own software internally). That gets me GL data as well as monthly close notes, supporting schedules, and more. The additional tooling means Claude can pull P&L lines, drill into transactions, and answer flux questions.

So it’s mostly used for instant queries. And I can do that at my desk via Claude Cowork, or from my phone via Claude Dispatch.

This is all downstream of having great data recorded in QBO. AI data analysis isn’t helpful unless the data is high-quality. And real-time close helps too, because it means AI can run any analysis on demand, anytime.

When I’m reviewing my data in Claude, sometimes I’ll realize I need to change my Chart of Accounts and reclassify transactions. For instance, we’d been counting Claude, ChatGPT, and API tokens as ‘Software Subscriptions’. The spend grew and we realized we needed a new “AI Usage” OpEx line.

I used Claude to pull a list of all relevant transactions. I populated those into a spreadsheet, fed that to our ‘Editor’ tool to reclass, reviewed manually, and pressed the submit button. Instant reclass for all historical transactions.

I haven’t built any scheduled tasks in Claude Cowork yet, but I’m starting to explore that.

Sometimes I would prefer to write SQL against my data in Postgres, rather than view in Claude. I use Claude to write the SQL query and then run it directly against the database. Our new Magic AI Report gives users the ability to formulate a SQL query by chatting with us directly in our app.

What’s the Holy Grail you’re building toward?

I’m building FinOptimal products that can help both ourselves and our customers.

The first product is called Closer. I’ve lived through the pain of getting comments on a draft close package.

That was the most soul-killing thing as an accountant. You’d get a comment, you’d know you missed something, and now you have to dig through the GL and find some needle in a haystack. AI can dig and give you the note right away, instead of having your CFO catch mistakes. That can make the accountant’s job a delight.

Closer basically takes the first pass at that monthly package. Identifies what looks off. Investigates. Either fixes it (with approval), or hands the human a high-quality note.

The second problem we’re solving is balance sheet debris. Old reconciliations and suspense balances. The kind of mess that quietly snowballs into a disaster. Balance sheet review is where I have the highest hopes for AI.

Will AI replace CFOs?

I don’t think so. It’s going to elevate the good ones. The rent-seeking charlatans will have to find something else to do. It’s a power tool. You can serve more clients better than you could before. I’m bullish on both software and services for the next two years, at least. In ten years, who knows, of course.

About Jesse & Julian

Jesse Rubenfeld is the founder and CEO of FinOptimal. You can connect with Jesse on LinkedIn here, and learn more about FinOptimal at finoptimal.com.

Julian Rowlands is the founder and CEO of Cashboard, the AI enablement platform for FP&A. He was previously CFO of Xendit (last valued at $3bn) and Head of Finance at Spruce (exited to Zillow in 2023). You can connect with Julian on LinkedIn here, and learn more about Cashboard at www.cashboard.co.

Interviews with CFOs about AI is an interview series by Cashboard. We speak with finance leaders who use AI in their day-to-day work, and ask them really detailed questions about their setup.

If you’re a finance leader building with AI, we’d love to interview you! Email julian.rowlands@cashboard.co with a quick summary of what you’ve used AI to accomplish, and we’ll get a call booked.