Automation and AI agents for fintech
We build automation for fintech: document checks, onboarding and case review. Clean cases go straight through. Unclear ones go to a named reviewer with everything gathered. Every run logs its inputs, its decision, the rule or model behind it and how sure it was. Your cloud, your keys, and an off switch on each pipeline.
How we deliver it
How we ship it inside a regulated fintech
Fintech operations carry a queue that automation was meant to remove and mostly has not, because the tools that could read a document could not show why they decided what they did. We build the review so it reads and checks what a customer submitted, clears the cases a clear rule covers, and hands the rest to a person with the history, the flags and the policy already gathered. Every decision can be traced per case, records are kept to your policy, and the whole pipeline stops in one action when you need it to.
- 01
Map the process, build the test set
We trace the checking and case-review process as it really runs, exceptions included, and sort each decision into a fixed rule, judgement that can be written down, or judgement that stays with a person. Then we take a sample of your real past cases and build the scored test set that everything after is measured against. The map and the test set are yours whether or not you build, because most onboarding friction turns out to be a step nobody owns.
- 02
Review with straight-through processing
A supervising helper holds the case. Specialist helpers each own one small task: reading the document, checking it against your records, running sanctions and watchlist checks through your existing providers. Cases that clear a clear rule go straight through. Everything else goes to a reviewer with the extraction, the mismatches, the customer history and the relevant policy already gathered, so the person spends their time on the decision, not on collecting evidence.
- 03
The data boundary and the audit trail
For regulated work the default is your cloud, your keys and your retention policy, with AI providers held to zero-retention terms or run in your own setup where your policy requires it. Every case carries a full trace: the inputs, the decision, the rule or model behind it, how sure it was, and the person who reviewed it where one did. Automation you cannot evidence is a liability however accurate it is, so the trace is built first, not added afterwards.
- 04
Test in shadow, then go live
The workflow runs in shadow mode on live traffic without acting, so you can compare its decisions with your team's on the same cases with nothing at stake. It goes live on the lowest-risk part first and widens as the scored accuracy holds. The tests re-run on a schedule and whenever the model changes, drift raises an alert before your operations team feels it, and an off switch stops the pipeline in one action.
More about Automation and AI agents for fintech
What does this look like in a regulated fintech?
An automated process where an AI holds the goal of clearing a case and decides the next step: read the submitted document, pull out the fields, check them against your records, look at what came back, and then pass the case, ask for more or hand it to a person. The steps are decided case by case, which is why it copes with the blurry passport photo and the address that will not validate instead of breaking on them.
That is the difference from the rules engine most fintechs already run. A fixed decision tree does the same thing every time and fails when a case arrives in a shape nobody expected, which in verification is a large minority of cases. This is built for that variety, and it passes on what it cannot resolve instead of guessing.
It is not a chatbot either. Nobody types a prompt. It runs on the queue behind your onboarding, it acts on your systems, and what it hands back is a cleared case or a well-prepared handover to a person.
How do you keep an automated decision auditable?
Every run records its inputs, the decision it reached, the rule or model behind it, how sure it was, and the reviewer who signed it where a person was involved. The trace can be exported per case and records are kept to your policy. A compliance team that rejects automation it cannot question is right to, so this is built to be questioned.
Which decisions stay with a person is agreed before the build, not discovered after it. Anything with a real cost, a sign of fraud or a reporting duty goes to a person by design, with the background gathered and the judgement left to them.
Where does automation actually pay in fintech?
The onboarding queue is usually first. Identity checks are where funnels lose most of the users they paid for, and the drop-off has two causes: a confusing sequence and a wait behind a manual review. We rebuild the sequence and automate the review behind it, and getting back a user you already paid for is cheaper than finding another.
After that: case review and regular refreshes of customer checks, pulling document details into your records, sorting transaction and dispute cases, and preparing due-diligence packs on other companies. The shape that pays is the usual one: lots of volume, real repetition, and a cost of a wrong answer that a human reviewer can still catch.
The work not worth automating has a shape too: a one-off decision, anything that cannot be undone without a person anyway, and a process that is broken by design. Automating a broken onboarding flow produces the same drop-off faster, so we say so while we map it, not after the build.
Good questions
Common questions
Where does the data go, and who holds the keys?
For regulated work the default is your cloud, your keys and your retention policy. The AI providers a workflow calls are held to zero-retention terms, or run in your own setup where your policy requires it, and that boundary is fixed in scoping before anything is built. Moving it later is a rebuild, not a setting.
Will our compliance team accept an automated verification decision?
That is their call, and the workflow is built so it is an informed one. Every case carries a full trace: inputs, decision, the rule or model behind it, how sure it was, and the reviewer who signed it where one did. We also agree which decisions stay with a person before the build starts, so nothing you need a person on is quietly automated.
Can you reduce onboarding drop-off without loosening KYC?
Yes, and the two are not in tension. Most drop-off comes from a confusing sequence and a wait behind manual review, not from the checks themselves. We rebuild the sequence around setting expectations and showing progress, and automate the review so clean cases clear without a queue. The checks that matter still run.
What happens when a model or a rule changes underneath a live workflow?
The tests are re-run on a schedule and on every model change, so a problem shows as a lower score before your operations team feels it. Rules and connections are versioned, failures retry under a clear policy before they go to a person, and an off switch stops the pipeline in one action.
Do you work with crypto?
No. We take regulated fintech work and decline crypto and Web3. If that is your market, another partner will serve you better than we would, and we would rather say so on the first call.
Ready when you are
Ready to automate the checks without losing the audit trail?
30 minutes. Tell us where the onboarding queue backs up and what your compliance team needs to see. We will tell you which decisions are safe to automate, which stay with a person, and what the audit trail would look like before you commit to a build.