AI automation for fintech companies

Controls first, speed second

A fintech operations team has two bosses: growth and the regulator. Tickets grow with every new customer. Compliance staff are hard to hire. Automation is attractive and risky in equal measure, because a wrong automated decision can become a reportable event.

So we build around a simple split. The model reads, sorts, summarises and drafts. A rule engine you control and a human decide. Nothing in our builds approves a loan, clears a sanctions alert or closes a fraud case.

Operations queues and where AI fits

Notice how every row has a stored trail. Auditors ask for it, and it is cheaper to design in than to bolt on.

What we build

• A ticket triage layer on your helpdesk that tags, prioritises and drafts, with account facts fetched read-only from your own API.
• A KYC intake pipeline that gathers documents, checks completeness and extracts fields into a case file for your reviewer.
• An exception grouping tool for settlement and reconciliation files, built with finance ops on real examples.
• An evidence collector that pulls access logs, change records and policy extracts for audits such as SOC 2.
• A decision log in a format your compliance lead can query without asking an engineer.

We build on n8n, Make or custom services in your cloud account. Data stays in your environment where your policy requires it, and we choose model providers that offer zero-retention or enterprise terms.

Regulation and our limits

Financial firms face rules on data protection, outsourcing, anti-money-laundering and consumer duty, and these vary by country and licence. In the US the FTC and state regulators set expectations, and the Stripe documentation describes payment-provider requirements. In the UK, the ICO sets data protection rules. In Ireland, the Data Protection Commission applies GDPR.

We are an engineering partner, not your compliance adviser. We do not give regulatory advice. We document each data flow, model provider and subprocessor so your compliance lead and outsourcing review can assess them. Automated decisions that significantly affect customers can carry specific legal requirements, so we avoid building them. Check with your counsel and regulator.

How a project runs

1. Risk and queue reviewA call with operations and compliance to rank queues by volume and by risk.
2. Data map and approvalWe write down what data each step touches and get sign-off before any build.
3. Build with a test setWe use anonymised past cases, then measure agreement with your analysts.
4. Shadow, then limited liveShadow mode runs beside your team first. Live use starts with the lowest-risk queue.

We work from Pune and keep one named lead on the account, with calls in your hours.

Price and what drives it

Projects start at $1,500 and our rate is $30 an hour, quoted as a fixed price within one working day of a short call. A single-queue triage layer is near the minimum. Security reviews, private hosting, multiple systems of record, audit logging requirements and several jurisdictions add work.

Model and hosting costs are separate monthly lines. Our AI automation cost guide explains the drivers and the AI agent security checklist lists controls worth requiring from any vendor. The base service is described on AI automation.