Generative AI: Fintech

Generative AI in fintech

In fintech the question is never whether an LLM can do the task. It is whether your model risk team, your auditor and your regulator will accept how it does it. We build KYC extraction, AML investigation copilots and support agents with read only tools by default, card data kept out of every prompt, human approval on anything that moves money, and an evaluation record your second line can actually review.

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Key highlights

We build KYC and KYB document extraction, AML alert investigation copilots, customer support agents, credit memo assistants and text to SQL over your ledger.
Agents get read only tools by default. Refunds, payouts and card actions need an explicit human confirmation and an idempotency key.
Card numbers never enter a prompt, so PCI DSS scope stays where it is, and other personal data is redacted or tokenized before it reaches a model.
Every model ships with a validation pack: intended use, test results on a golden dataset, limitations and monitoring, in the shape model risk teams expect.
A measured prototype usually takes about 4 weeks, and a production agent wired into your systems 10 to 16 weeks.

Why fintech AI projects stall

Fintech teams rarely lack ideas for generative AI. Projects stall at second line review, at the vendor risk questionnaire, or at the moment someone asks what happens if the agent gets it wrong with real money.

Model risk will not sign off. The validation team asks for intended use, test evidence, limitations and an ongoing monitoring plan. The prototype has a demo video and a prompt in a notebook, so it goes back to the queue.

Analysts drowning in AML alerts. Most transaction monitoring alerts are false positives, yet each still needs an analyst to pull history, screening hits and adverse media into a case narrative by hand.

KYC documents still keyed by hand. Passports, proof of address, company registry extracts and ownership charts arrive as images and PDFs. Operations retype them, applicants wait, and conversion drops.

Support bots that cannot do anything. A FAQ chatbot cannot see a transaction, freeze a card or explain a fee, so customers escalate anyway, and complaints that should be logged under Consumer Duty or DISP slip past.

Fear of an agent moving money. Nobody wants to explain to a regulator why an AI issued a refund or changed a payee. Without hard action limits, the project is cancelled rather than designed safely.

Third party risk on model vendors. Procurement and DORA style ICT third party reviews want to know where data goes, which sub-processors are involved and what the exit plan is if a model provider changes terms.

Use cases

Generative AI use cases we build for fintech

Each use case keeps decisions with a person or a deterministic rule, and uses the LLM for the reading, gathering and drafting that slows teams down.

KYC and KYB document extraction

OCR plus LLM extraction of identity documents, proof of address, company registry filings and ownership structures into typed JSON, cross checked against your Onfido, Sumsub or Persona results, with low confidence fields routed to a review console.

AML alert investigation copilot

A tool using agent gathers transaction history, counterparties, sanctions and PEP hits and adverse media for each alert, then drafts the case narrative and a suspicious activity report draft. The analyst decides and files, never the agent.

Customer support agents with tools

Chat and voice agents that look up transactions, explain fees and disputes and freeze a card after the customer confirms. Complaints and vulnerability signals are flagged and routed to a human with a summary.

Credit memo and underwriting assistant

Open Banking transactions from Plaid or TrueLayer are categorised, affordability figures are computed in code, and the LLM drafts the credit memo narrative for the credit officer. The lending decision stays with your rules and your people.

Text to SQL over ledger and finance data

Finance and operations ask questions in plain English and get validated, read only SQL against the warehouse, with the query shown alongside the answer so reconciliation and month end figures can be checked.

Policy and regulatory change RAG

Retrieval over your policies, procedures and the regulatory texts you rely on, with citations, plus an agent that compares a new rule or guidance update against current procedures and lists the gaps for compliance to review.

How we deliver

From one queue to a governed rollout

01
Use case discovery and data readiness
We rank use cases by analyst hours saved and risk, map which systems an agent would need to read or write, and agree data handling for PCI and personal data. Output: a written scope, a risk tier for each use case and acceptance criteria.
02
Evaluation harness and prototype
A golden dataset of real, masked cases is built with your operations team, and two or three models are tested for accuracy, completeness, cost and latency. Results go into the first draft of the validation pack.
03
Guarded pilot with humans in the loop
Analysts or agents use the tool on live work with every output reviewed. Overrides and edits are logged, action tools stay behind confirmation, and the pilot runs alongside the current process until the numbers hold.
04
Production rollout with monitoring
Canary release behind a feature flag, per tenant cost caps, drift and accuracy dashboards, and quarterly model reviews. Your second line gets the monitoring evidence on a schedule rather than on request.

AI governance for fintech

Financial regulators already have rules for models, outsourcing and customer outcomes. Generative AI has to fit inside them, so we design for model risk, data protection and conduct from the first sprint.

SR 11-7 model risk (US) PRA SS1/23 model risk (UK) FCA Consumer Duty DORA ICT third party risk (EU) EU AI Act (credit scoring high risk) ECOA and Regulation B PCI DSS v4.0 GDPR and UK GDPR

Model risk evidence is a deliverable. Each model ships with a written intended use, the golden dataset and test results, known limitations, prompt and model version history, and the monitoring thresholds that trigger review. That maps to what SR 11-7 in the US and PRA SS1/23 in the UK expect, and it is what your validators will ask for anyway.

Sensitive data never reaches the prompt. Card numbers are excluded by design so PCI DSS scope does not grow, and account numbers and personal data are tokenized or redacted with tools such as Microsoft Presidio. Models run on enterprise endpoints with retention disabled, in the region your data must stay in, or self hosted when policy requires it.

Credit decisions stay explainable. Under the EU AI Act, creditworthiness assessment and credit scoring of individuals is a high risk use, and in the US adverse action notices under ECOA and Regulation B need specific reasons. We keep scoring in deterministic or classical models, use the LLM for narrative and document work, and log the reasons behind every decision.

Models and tooling we use for fintech AI

Enterprise model endpoints with retention off, open weight models where data must stay inside, and the same auditable infrastructure we use for ledgers and payments.

Claude on AWS Bedrock GPT on Azure OpenAI Gemini on Vertex AI Llama (self hosted) LangGraph Temporal workflows PostgreSQL and pgvector AWS Textract Plaid and TrueLayer Microsoft Presidio PII redaction Langfuse tracing Python and TypeScript

FAQ

Common questions

How much does a fintech generative AI project cost?

It depends on scope and risk tier, so we start with a fixed price prototype and price production from a written scope. An internal AML copilot with read only tools is smaller than a customer facing agent that can take actions. Model API costs are passed through at cost and reported per case or per conversation. You get a written estimate within 48 hours, and our pricing page explains how engagements are structured.

Can we use LLMs with customer financial data?

Yes, with the right controls. Card data is kept out entirely, other personal data is tokenized or redacted where the task allows, and models run on enterprise endpoints with retention disabled in your required region, or self hosted. Every prompt, tool call and response is logged with access controls so your audit team can see exactly what the model saw.

How do you handle hallucinations in financial answers?

By never letting the model invent a number. Figures come from tools and SQL, not from the model's memory, answers cite the record or policy they used, and outputs are tested against a golden dataset before release. Anything customer facing or decision related is checked by a person or by deterministic validation.

How long does it take to get an AI agent into production?

Usually 10 to 16 weeks. A measured prototype takes about 4 weeks, then a guarded pilot runs alongside the current process before rollout. Second line review is planned into that timeline with the validation pack produced as we go, which is usually what decides the date.

Should we build or buy AI for AML and KYC?

Buy the core screening and verification, build the glue. Providers such as ComplyAdvantage, Onfido and Sumsub already do screening and identity checks well. Where we add value is the investigation copilot, document extraction for edge cases and the workflow around your specific risk policy, which off the shelf tools rarely fit.

Will an AI agent ever move money on its own?

No, not in our designs. Money movement, payee changes and card actions are behind an allowed action list, an explicit human confirmation and an idempotency key, and every attempt is logged. Agents can prepare the action and explain it, a person or a rule approves it.

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