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Banks · Fintech · Wealth and asset management

Put real controls on AI-driven refunds, payments, and trades—before money moves.

Built for leaders who ship software and for risk and compliance who must show which rule allowed, blocked, or escalated each action—not a chat transcript alone.

When customers or advisors use assistants that can initiate refunds, wires, or trades, Prüfer checks each attempt against your policies first. Refunds go through POST /api/v1/refunds — agents never hold Stripe keys, unmatched money is denied, and a blip does not open the till. Chat stays fail-open on purpose. You get a dated allow / block / escalate record. If the agent still holds a Stripe key, Stripe.com can still pay — that is a setup choice, not a missing check.

Book a working sessionFirst block in 5 minutes

Industry reality

Boards already ask a blunt question: who is accountable when the agent acts?

Thoughtful AI strategy is necessary; it is not sufficient. The gap is operating control—the same reason you would never run wires on “the teller usually remembers the policy.” Below are three patterns that already cost real firms credibility or cash. They are the reason teams call us before the next headline is theirs.

Customer-facing AI said “yes”; the company owned the answer

In 2024 a Canadian tribunal ruled against Air Canada after its website chatbot gave a passenger incorrect information about bereavement fares. The decision treated the automated reply as the airline’s responsibility—not a one-off model glitch. The lesson for every bank and fintech shipping copilots: what the assistant *says* in chat can create liability; what it is allowed to *do* with customer money can create losses.

Regulators stopped treating “we have a policy deck” as the whole story

In U.S. capital markets, regulators have fined firms for misrepresenting AI capabilities or oversight to investors—the so-called “AI washing” cases. In banking, examiners have always asked whether controls actually ran in production, not whether they existed in a deck. New AI features concentrate both pressures on the same place: automated actions that hit payments, wires, and trading systems.

The failure mode is often boring—and expensive

Double credits when an automation retries a payment instruction. A refund that clears before anyone reads the transcript. A wire initiated from a session that looked fine to the assistant but broke your own limits. None of these require science fiction; they require the same discipline you already demand for human-initiated wires and trades.

Payments, billing, and cash movement

This is where most organizations feel the heat first. The scenarios are unglamorous and painfully easy to explain to a chief operating officer or head of fraud: money left the building, and the trail points to an automated path you did not mean to leave wide open.

  • Duplicate payout. The assistant retries a payment after a timeout; two identical ACH credits post. Your reconciliation team finds it first—or your customer does.
  • Refund without a control you can show. Policy said “manager approval above $500,” but that rule lived in prose inside a prompt. When something slips through, there is no immutable record that the control ran.
  • Social engineering at machine speed. A long chat session steers the model toward a tool that moves funds. If the only guardrail is “please be careful,” you are one bad turn away from a wire.

Trading, allocations, and advisory automation

The vocabulary changes; the requirement does not. Whether the workflow sits next to an order system, a portfolio rebalance job, or a next-best-action copilot, the question is identical: did this action satisfy your desk rules before it became an order or a booking?

  • Concentration and house lists. A model suggests building a position your restricted list or concentration limits forbid. Without an automatic check before the order goes live, you are debating after the fill.
  • Cross-session drift. Innocuous turns in the conversation add up until the next tool call crosses a threshold no single message flagged. You need state-aware evaluation, not a one-line system prompt.
  • Evidence for model risk and supervision. Examiners and internal audit ask how you know automated workflows stayed inside appetite. Logs of intent are weaker than logs of decisions on what was actually attempted.

Distribution

First blocked refund in five minutes. No checkout.

Create a sandbox. Call the refund till with no extra rule. Unmatched money is denied and no refund is created. That is the proof, not a dashboard tour.

  1. 1. Create a sandbox
    curl -sS -X POST https://api.prufer.ai/api/v1/sandbox/create \
  -H "Content-Type: application/json" \
  -d '{"name":"First block","organization":"Demo"}'
  2. 2. Use the returned api_key. Ask for a $50 refund. Expect 403.
    curl -sS -X POST https://api.prufer.ai/api/v1/refunds \
  -H "X-API-Key: YOUR_SANDBOX_KEY" \
  -H "Content-Type: application/json" \
  -d '{"agent_id":"billing-bot","amount":5000,"customer_id":"cus_demo"}'

You should see GOVERNANCE_BLOCKED / money_default_deny. Chat still fail-opens. Keys stay on Prüfer. Or start from the sandbox UI.

How Prüfer fits your organization

A control layer between the assistant and your books—not another slide on “responsible AI.”

Prompts can guide behavior; they cannot be subpoenaed as a control. Prüfer checks each concrete action the assistant is about to take—amount, instrument, customer segment, session—against the rules your risk and product teams actually own.

If the action is blocked, your payment or trading systems never see it. If it is escalated, you can route to the same human queues you use today. Either way, you leave a timestamped decision with rule context, which is what engineering, audit, and second-line risk need to stand in front of a committee with confidence.

Pilot framing we use with customers: agree on one high-impact action (for example a refund or trade), mirror one policy you already enforce manually, and measure time to the first blocked attempt in staging or a hosted sandbox. If we cannot show a clear deny with evidence, you should not buy.

Between the assistant and your systems
Illustration
1 · Request — assistant requests refund $5,000 · customer #88421

When teams get the most from Prüfer

We are direct about fit. The product shines when the work is serious—money movement or order flow—not when the only risk is generic chat text with no money or positions at stake.

Tool calls touch production money or positions

You are past the experiment where the model only summarizes PDFs. Your roadmap includes software that can debit, credit, refund, wire, trade, rebalance, or reallocate.

Risk and audit want the same artifact

You need one record both sides trust: what was attempted, which rule fired, and what happened next—without asking either side to interpret chat transcripts.

Engineering will own the integration

You have a named owner for putting a check between the orchestration layer and your core systems. We stay close to that team from day one.

Bring your hardest payment or trade scenario—we will trace it end to end

We will keep the first conversation on your workflows, not our feature list. If you share a realistic action, we will show how a decision would render and what your teams would see in the log.

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