You vibe-coded a fintech MVP. Now real money is about to move through it.

Using AI to build a fintech MVP is a genuinely good decision — it's the cheapest way to find out if the product has legs before you spend real money building it properly. The danger isn't in having built it with AI. The danger is the moment you flip it from "demo with fake data" to "real users, real balances, real transactions," because fintech breaks in ways that never surface while you're testing it yourself.

Here's what to check before the first real dollar flows.

The money math

AI frequently stores money as floating-point numbers, because that's the default for numbers in most languages. Floats can't represent cents exactly — 0.1 + 0.2 doesn't equal 0.3 — so balances drift by fractions of a cent, and those fractions accumulate across every transaction until your totals don't reconcile. In a demo nobody notices. In a live ledger it's the bug that erodes trust the fastest. Money should be stored as integer cents or an exact decimal type, and AI usually doesn't do that unless told.

The authorization

The question that matters in fintech isn't "can a user log in" — AI gets that right. It's "can user A see, move, or touch user B's money." AI-built apps routinely enforce that boundary in the interface and forget it on the server, so a direct request for someone else's account or transaction succeeds even though the button is hidden. Every endpoint that touches a balance has to be checked, individually, against a hostile user — not a trusting one.

The transaction integrity

What happens if a payment request is sent twice? If two withdrawals hit the same balance at the same instant? If a webhook from your payment provider arrives with no signature check? AI-generated payment flows almost never handle these — no idempotency, no locking, no webhook verification — because they work perfectly in the single-user, one-request-at-a-time world of your own testing. Real users, retries, and race conditions are where duplicate charges and negative balances are born.

The data and the secrets

Financial data raises the stakes on the usual AI failures: keys committed into the codebase, sensitive data in logs, no audit trail of who moved what. In fintech these aren't just security issues — they're the things that end a partnership with a bank or a payment processor the moment their security team looks.

Why the demo passing tells you nothing

Every one of these is invisible until the exact conditions that a demo never creates: concurrency, retries, a hostile user, real volume. That's the whole problem with judging an AI-built fintech app by clicking through it — you are the one user who behaves. Finding these gaps means having an engineer who has shipped and operated financial systems read the code and probe the money paths directly.

Before you go live

Our code audit is built for exactly this moment. A senior engineer reads your AI-built codebase and reports on the money handling, authorization, transaction integrity, and data security — with a prioritized roadmap of what to fix before real transactions start. Fixed price from $995, written report in five business days.

If money is about to move through something AI built, get it read first. It's a lot cheaper than the alternative.

Related: read the auth security breakdown · book a vibe coding audit

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