AI chatbots for fintech companies

What makes fintech support different

A user whose transfer is stuck is anxious, not curious. They want the status, the reason and the time. They also ask questions that sound like advice: should I switch plans, is this a good time to invest. Both kinds land in the same chat window.

Regulation shapes what the bot may say. Statements about fees, risk or returns are often regulated communications, and mistakes can become complaints that you must log and answer. The design starts with what the bot must refuse.

Volume also swings with events. A product outage, a new fee or a phishing wave can triple contacts in a day. A bot that carries the explanation for a known incident, updated by your team in minutes, takes pressure off the support queue when it matters most.

Where a chatbot helps

• Onboarding help: which documents verify identity, why a photo was rejected, how long checks usually take.
• Product explainers: plan features, limits, supported countries and fee schedules, quoted exactly from the live fee table.
• Status answers: when logged in, tell the user where a transfer, card order or verification stands, using read-only data.
• Security guidance: how to freeze a card, spot phishing, update a device. Real fraud reports go to your fraud team at once.
• Complaint intake: capture the facts and route through your complaints procedure, rather than argue in chat.
• Developer support if you offer an API: answer questions from your documentation.

We connect to your support stack, for example Zendesk, Intercom or Freshdesk, to identity providers such as Auth0 or Okta for sign-in, and to read-only views of your own data. We do not build anything that can execute payments from chat.

Question types and limits

Card details, passwords and one-time codes should never be typed into chat. The bot detects these patterns, masks them and tells the user why.

Compliance points to raise early

Rules depend on what you do and where. A payments firm, a lender, an investment app and a bookkeeping tool face different regulators in the US, UK, Australia and Ireland, and some require records of customer communications or a complaints process with set timeframes. Talk to your compliance lead before launch and tell us the boundaries. Payment data brings PCI DSS into scope, which is one reason we keep card details out of chat. The FTC also expects accurate statements in automated customer interactions.

We log every message, the sources used and the version of the knowledge base, so you can reconstruct why the bot said something. Our AI agent security checklist lists the controls we apply.

How the build goes

1. Risk workshopWith your compliance lead we list the topics the bot must never touch and the exact refusal text.
2. Source of truthFees, limits and policies are loaded from a controlled document set with version dates.
3. Build with authenticationLogged-in answers use read-only calls scoped to the signed-in user.
4. Red-team testWe try prompt tricks, advice requests and data-probing questions and fix what leaks.
5. Staged launchFirst to a small segment, with a human reviewing every transcript.

The team works remotely from Pune and overlaps with your hours for review calls. General method is on AI chatbot development.

Pricing

A public FAQ and onboarding bot from approved documents can sit near our $1,500 minimum. Logged-in status lookups, multiple products or markets, and extra review rounds add hours at $30 an hour, and the quote is fixed within one working day of a short call. Security review and the quality of your internal APIs are the biggest drivers. See the AI chatbot cost guide for the general factors.