When Does Vibe-Coding Actually Work in Treasury?
Use cases, common errors, and where to draw the line. AI can generate a working prototype in minutes — the question isn't whether it works, it's whether it's safe to trust for what you're using it for.
- ✓ Name the treasury use cases where AI-generated code is genuinely low risk
- ✓ Recognize the errors vibe-coded tools make even in those safe use cases
- ✓ Spot the sneaky failures that waste your time before you notice them
- ✓ Know exactly which treasury tasks need real engineering, not prompts
- ✓ Apply one simple rule to decide which side of the line any idea falls on
Alina Turungiu | TreasuryOS · treasuryease.com
Where it works
Low risk. No live data. No transactions.
Show your manager what a cash dashboard could look like. Mockup in 2 hours instead of a 20-page RFP.
FX break-even, loan amortization, interest accrual. Self-contained math. No connectivity needed.
Reformat a CSV, merge reports, pivot a table. Wrong output? Just re-run it. No consequences.
Budget vs actual on cash flow. Bank fee comparison. Static data in, analysis out. Zero risk.
Where it works
Learning, exploring, tracking.
Board pack layouts, weekly cash summaries, KPI trackers. Read-only output, no live feeds.
Bank accounts, guarantees, LC expiry dates. From exported data, a structured view of what you have.
How does an API work? How to read a SWIFT message? Build a test script. Break it. Learn. No risk.
If the output is wrong and nobody loses money, no payment goes to the wrong account, and no compliance rule is broken — then vibe-code away.
Errors you'll hit
Even in the safe use cases, expect these.
The LLM generates demo data to make the output look good. Your dashboard works, but the numbers are invented.
No bank API, no ERP, no database. You get a UI with no backend. A beautiful empty shell.
It picks a random FX rate, converts in the wrong direction, or ignores multi-currency entirely. Classic LLM blind spot.
It picks a day count convention without knowing which one applies to your instrument. It guesses — and doesn't tell you it guessed. The output seems correct until you check it manually.
Errors you'll hit
The sneaky ones that waste your time.
Refresh the page and everything is gone. No database, no persistence. Your work disappears.
Upload a bank statement and it reads 90% of it correctly. The other 10%? Silently skipped. No error, no warning.
The LLM invents rules that sound right: netting logic, entity hierarchies, consolidation steps. None of it is real.
It works today. Tomorrow you ask for a change and the whole thing breaks. No structure, no documentation.
Where it breaks
Live data, real money, real consequences.
Same standard, different implementation per bank. The LLM parses the happy path. It won't handle the exceptions each bank throws at you.
One bug = real money in the wrong place. This needs tested, audited, hardened infrastructure.
Entity structures, intercompany netting, multi-currency consolidation. The logic is too deep to build from one single prompt.
EMIR, IFRS, audit trails. The format, the timing, the rules. One mistake and it's not a bug, it's a liability.
The takeaway
Vibe-code to explore. Engineer to operate.
Use AI to prototype fast, learn new tools, and automate the boring stuff.
But when real money moves, real banks connect, and real compliance matters — you need infrastructure, not prompts.
Safe: prototypes, calculators, data cleanup, variance analysis, reporting templates, tracking tools, learning scripts — no live data, no transactions.
Watch for: fake data, empty backends, wrong currency logic, guessed formulas, lost state, silent parsing failures, hallucinated business rules, unmaintainable code.
Off limits for vibe-coding: live bank connections, payments & FX execution, cash forecasting on live positions, regulatory & compliance reporting.
The one rule: if a wrong output can't cost money, misdirect a payment, or break a compliance rule — go ahead and vibe-code it.
AI for Treasury: Build Your Own Tools
The three levels of building — and the sanitization rule for whichever one you pick.
Next step · CourseAI for Treasury Practitioners
What real, production-grade AI infrastructure looks like once you're past the prototype.
Alina Turungiu | TreasuryOS · treasuryease.com