In finance the question is never only “does it work”. It is “can you show why it decided that, eight months later, to someone who is not technical”. Build for that from the start and the rest is ordinary engineering.
Onboarding and document checks
Identity documents, proof of address, company records. Extraction and matching cut the manual pass down, but the decision to approve or reject stays with a person and stays logged. Every automated step keeps the input, the model version and the output it produced.
Monitoring and review queues
Rules engines catch the known patterns. Models are useful for ranking what a human should look at first, which is a much safer job than deciding alone. A better ordered queue is worth more than a clever verdict nobody trusts.
Support that does not give advice
A finance assistant answers about the product — fees, limits, status, how to do a thing in the app. It does not offer guidance on money. That boundary is written into the system prompt, tested in the evaluation set, and re-tested on every release. See AI chatbots and assistants for how the handoff works.
What the compliance team will ask for
- Which model version produced this output, and when
- What input it saw, retained for the required period
- Who reviewed it and what they changed
- How you know the system has not drifted since launch
We build the answers in rather than bolting them on, because retro-fitting an audit trail means rebuilding the feature.
Marketing side
Paid channels restrict finance advertising hard, so growth here leans on search and trust content. That is the same argument the fintech section makes, and it does not change because a feature now uses a model.
Back to the AI map, or read LLM development services for the evaluation layer this depends on.