AI and software engineering for fintech
Fintech engineering is ordinary software with an unusual failure cost: a bug that would be a nuisance elsewhere is a reconciliation break, a regulatory report, or money in the wrong account. We build financial systems where correctness, traceability, and the ability to explain any number to an auditor are design inputs rather than later additions.
What makes it hard here
- Every figure needs a provenance trail
- Models cannot be the system of record
- Access is scoped and logged, not assumed
Services this involves
Where fintech projects actually go wrong
The hard part of financial software is rarely the calculation. It is that every number has to be explainable months later, by someone who was not there, from records that cannot be quietly edited. That constraint shapes the schema, the deployment process, and how much of an AI model you can responsibly put in the path of a decision.
Which is the honest limit worth stating early: a language model is excellent at reading documents and terrible at being the system of record. We use them where the output is checked — extraction, summarisation, triage — and keep deterministic code where the money moves.
Every figure needs a provenance trail
Records are append-only and every derived number can be traced to its inputs. This is a schema decision made on day one; retrofitting it means rebuilding the data layer.
Models cannot be the system of record
A model output is evidence, not truth. Anything affecting a balance runs through deterministic code with the model result stored alongside as an input a human can review.
Access is scoped and logged, not assumed
Who read what, and when, is as much a requirement as who wrote what. Designed in from the start, because the alternative is an access log that cannot answer the question being asked.
Systems we build for fintech & financial services
Reconciliation and exception handling
Matching engines that clear the routine volume automatically and surface the genuine breaks with enough context for a human to resolve them quickly.
Document extraction at volume
Statements, invoices, and KYC documents parsed into structured data, with confidence scores and a review queue for anything the model is unsure about.
Risk and fraud signals
Scoring pipelines that combine rules with learned signals and, critically, explain which factors drove a given score.
Internal operations tooling
The dashboards and back-office tools that finance teams otherwise build in spreadsheets, rebuilt with permissions and an audit trail.
The services this usually involves
- Custom Software DevelopmentInternal tools, operations platforms, and management systems built for how your business actually runs.
- AI DevelopmentEnd-to-end AI systems — from deciding what is worth building through to operating it once real users depend on it.
- API DevelopmentREST and GraphQL APIs with versioning, auth, and docs from the first endpoint — plus the integrations you depend on.
- Cloud & DevOpsCI/CD, infrastructure as code, monitoring, and cost controls — infrastructure you can deploy to on a Friday.
Fintech & financial services: common questions
Can you build financial software that meets audit requirements?
Is it safe to use AI in a financial product?
How do you handle sensitive financial data during development?
Building something in this sector?
Tell us the constraint you are working against. If we have not solved that particular problem before, we will tell you.