AI development for fintech and financial services
Finance is the sector where 'mostly right' is worthless. Every automated decision needs an audit trail, a reviewer and a way to explain it to a regulator two years later.
What is different here
The high-value, low-risk work is reconciliation between systems, onboarding document checks, searching regulation and internal policy, transaction categorisation, and drafting reports a person signs off.
We design these as deterministic pipelines with AI at specific steps, not as an AI that runs the process. That distinction is what makes the system explainable — you can show exactly which rule or which extracted value produced an outcome.
- Deterministic controls around every AI step
- Immutable audit logs of inputs, outputs and reviewers
- Four-eyes approval on anything that moves money
- Data residency by jurisdiction
- Reproducible outputs with model and prompt versioning
- Reporting built for examination, not just for dashboards
Frequently asked questions
Can AI make lending or credit decisions?
Not autonomously, and we would not build it that way. Regulated decisions need explainability and fairness testing that generative models do not provide. AI can gather, extract and summarise evidence; a documented rule or a person makes the decision.
How do you handle model changes for auditability?
Every run records the model version, prompt version and inputs, so any historical output can be explained and reproduced. Providers deprecate models, so this record matters more than teams expect.
Do you work with regulated data residency requirements?
Yes — regional endpoints, in-region storage and, where required, entirely self-hosted processing.
Tell us what you are building.
Send a short description of the problem and we will reply within one business day with an honest view of scope, cost and whether we are the right person for it.
Or email directly: contact@hire-ai-dev.com