Building AI for Finance Teams - Why It's Harder Than It Looks

Finance AI Isn't Hard Because of the AI

Everyone's building AI copilots. We're building AI for invoices. Turns out, that's the harder problem.

I joined Neoflo to build AI for finance teams.

First week, I sat with an AP manager. He showed me his screen: 47 browser tabs, 3 spreadsheets, an ERP that looked like it was built when Blackberry was cool, and a folder called "INVOICES_FINAL_FINAL_v3."

"This is how we process invoices," he said.

I thought I understood finance. I didn't.

A few months in, I've learned something that changes how I think about AI products entirely.

Finance AI isn't hard because of the AI. It's hard because finance is controlled chaos that's been duct-taped together for decades.

What Finance Actually Looks Like

Most people think finance = spreadsheets and calculators.

Here's what it actually is:

Money coming in (Order-to-Cash): Customer orders → You fulfill → You bill → You wait → You wait more → You send a reminder → They pay (maybe) → You figure out what they paid for → You update your books

Money going out (Procure-to-Pay): Someone needs something → They request → Someone approves → You order → It arrives (hopefully) → Invoice comes in → You check if it matches → You approve payment → Money leaves

The reconciliation nightmare (Record-to-Report): Did the money that left match what we said would leave? Did the money that came in match what we expected? Why is there a $4,847.32 discrepancy? Where did it go? It's month-end and we need to close the books in 6 hours.

Simple, right?

Now multiply by thousands of transactions. Multiple bank accounts. Multiple currencies. Multiple subsidiaries. Rules that change by vendor, by amount, by department, by phase of the moon.

Welcome to finance ops.

Why This Is Harder Than It Looks

I've built AI for healthcare. I've built AI for video. Finance is different.

Data Arrives in Chaos

Invoices show up as:

• PDFs (sometimes scanned at an angle)

• Email attachments (forwarded 4 times with "FYI" as context)

• Images (photos of a screen showing a PDF)

• Spreadsheets (with creative formatting)

• WhatsApp messages with photos of receipts

• Handwritten notes (seriously)

And that's just invoices.

Remittance advice is worse. A customer sends $47,832.15. The payment description says "July." Which invoices from July? All of them? Some? There were 23 invoices. The payment doesn't match any obvious combination.

This is called cash application. Someone's job is to solve this puzzle. Hundreds of times a day.

The Rules Are Different Everywhere

Every company is its own universe.

Matching rules:

• Company A: Invoice must match PO exactly

• Company B: 5% tolerance is fine

• Company C: One invoice spans multiple POs

• Company D: One PO has multiple invoices and GRNs

• Company E: "It depends, ask Sundip"

Approval hierarchies:

• Under $1K: Auto-approve

• $1K-$10K: Manager approval

• Over $10K: VP approval

• Over $50K: CFO approval

• Except for marketing. Marketing has different rules.

• And except for that one vendor we've used for 15 years. They're auto-approved.

You can't build "one AI" and deploy everywhere. The edge cases aren't edge cases. They're the entire product.

Everything Connects to Everything

Finance isn't isolated workflows. It's a web.

Late invoice processing → Delayed payments → Angry vendors → Supply chain issues

Slow cash application → Unclear AR aging → Bad collection decisions → Cash flow problems

Reconciliation breaks → Delayed close → Late reporting → Compliance issues

Touch one thing. Ripple everywhere.

Finance Doesn't Forgive Mistakes

In most AI products, 95% accuracy is great. Ship it. Iterate.

In finance, 95% means:

• 5 out of 100 invoices paid wrong

• Duplicate payments

• Missed early payment discounts

• Vendors paid twice, vendors not paid at all

• Audit findings

• Angry CFO

Finance people have a saying: "The books have to balance." Not "mostly balance." Balance. To the cent.

Trust Is Measured in Transactions

I asked that AP manager: "What would it take to trust an AI to do this?"

His answer: "Show me it works. For months. On real transactions. And let me check every single one until I believe it."

Finance teams have seen automation before. They've seen it fail. They've been the ones staying late to fix it.

You don't get to say "trust the AI." You have to earn it. Transaction by transaction. Exception by exception. Month-end by month-end.

The Alphabet Soup (Explained)

If you're not in finance, the acronyms are overwhelming. Here's the cheat sheet:

P2P (Procure-to-Pay): Everything from "I need to buy something" to "money left the account"

O2C (Order-to-Cash): Everything from "customer ordered" to "money hit the account"

R2R (Record-to-Report): Making sure all the numbers match and reporting them

AP (Accounts Payable): Money you owe others

AR (Accounts Receivable): Money others owe you

PO (Purchase Order): "Yes, we agreed to buy this"

GRN (Goods Receipt Note): "Yes, we received what we ordered"

3-way match: Checking that PO, GRN, and invoice all agree

Cash application: Figuring out which invoices a payment is for

Reconciliation: Making sure two sets of numbers that should match actually match

Month-end close: The chaos of finalizing the books every month

Now you can survive a conversation with a CFO.

Why Now?

Finance automation isn't new. ERP systems promised this decades ago. Why would AI be different?

AI can finally read documents.

OCR existed. But understanding that "Net 30" means payment terms, that "Ref: PO-2024-0847" links to a specific order, that the vendor "ABC Corp" is the same as "ABC Corporation Inc" in your system — that required human judgment.

LLMs can do this now. They understand context, not just characters.

AI can hold enough context.

Finance decisions need the full picture: The invoice. The PO. The GRN. The contract. The vendor history. The matching rules. The approval policy. The GL codes.

Old systems couldn't hold all this at once. New models can.

AI can finally act.

This is the big one.

Old automation: "Here's a report of invoices that might have issues."

New automation: "I matched the invoice, created the entry, flagged the exception, and routed it to the right approver."

That's the shift from analysis to action. From copilot to agent.

What We're Building at Neoflo

AI for the CFO tech stack.

Not a dashboard. Not a chatbot. Not "AI-powered insights" that nobody acts on.

AI that does the work:

For P2P:

• Read invoices in any format

• Match to POs and GRNs (even messy multi-way matches)

• Flag exceptions with reasons

• Route approvals intelligently

• Execute payments

For O2C:

• Apply cash automatically (even when remittance says "various")

• Manage collections with context

• Send dunning at the right time to the right person

• Track disputes and deductions

For R2R:

• Reconcile accounts continuously

• Surface breaks before month-end

• Prepare journal entries

• Accelerate close

Finance doesn't forgive. Get it wrong and someone's paycheck bounces.

Get it right? Your CFO stops dreading the last week of every month. That's the win.

Do This Next

If you're building AI for finance ops (or thinking about it), here's what matters:

[ ] Spend time with actual finance teams. Not just CFOs. Talk to the AP clerk. The AR analyst. The people doing the work. They'll show you the chaos your product needs to solve.

[ ] Build for the edge cases first. The "happy path" is 20% of transactions. The other 80%? Multi-PO invoices. Partial payments. Currency mismatches. That's your product.

[ ] Design for trust, not just accuracy. Finance teams need to verify before they trust. Build transparency into every decision. Show your work.

[ ] Understand the compliance layer. SOX controls. Audit trails. Segregation of duties. These aren't nice-to-haves. They're deal-breakers.

[ ] Test with real month-end pressure. Your AI might work great on day 15. What about day 30 at 11 PM when the books need to close and there's a $10K discrepancy nobody can explain?

Start with one workflow. Get it to balance. Every time. Then expand.

Key Takeaways

• Finance AI is hard because finance is chaos. Data arrives messy. Rules differ everywhere. Everything connects. Mistakes are unforgiving.

• 95% accuracy isn't good enough. In finance, you either balance or you don't. There's no partial credit.

• Trust is earned in production, not demos. Finance teams have seen automation fail. Show them it works on real transactions. For months.

• The boring problems are the hard problems. Nobody's putting invoice matching on a conference stage. But that's where the value is.

• AI finally makes real automation possible. Not because the AI is magic. Because it can read chaos, hold full context, and take action. That's new.

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I write about what I'm building and learning at heyshubh.com.

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Next week: Claude's new Chrome integration - is it actually better than Comet and Dia, or just more hype? I've been testing all three. The results aren't what I expected.