AI you can explain to an examiner
Extraction, triage and drafting inside lending, debt and insurance processes. Scoped narrowly, reviewed by a person where it matters, and built to leave the evidence trail someone will eventually ask you for.
Most of the work is reading, not deciding
The expensive part of a regulated operation is rarely the decision itself. It is everything in front of it: a document arrives in the wrong shape, somebody rekeys it, somebody else checks the rekeying, and a queue forms behind them. That is the work worth automating, and it happens to be the work these models are good at. The decision stays where your license says it has to stay.
- Read what arrives: statements, policies, correspondence, forms, photographs
- Sort it, route it, and put the fields where they belong
- Draft what has to go back out, for a person to approve
- Summarize a file so whoever decides starts from the facts
Where it earns its place
The same handful of jobs, in the three places we work. None of these replace a decision-maker; all of them shorten the distance to the decision.
Lending
- Income and identity documents read once at application instead of rekeyed and checked twice
- Adverse action notices drafted from the actual decision record
- Decisioning support that can show which factors moved an outcome
- Servicing correspondence summarized before it reaches a human queue
Debt services
- Inbound messages triaged by intent: dispute, hardship, cease contact, payment
- Disputes classified and matched against the account record
- Contact history summarized so the next conversation starts informed
- Complaint themes surfaced from what consumers actually wrote
Insurance
- First notice of loss read out of whatever the customer happened to send
- Submissions ingested and normalized before they reach an underwriter
- Claim files summarized for the adjuster picking one up cold
- Policy language checked against what was actually issued
One claim, read once
A customer sends a photograph, a PDF and three paragraphs of email. The system reads all three, pulls the policy number, date of loss and peril, checks them against the policy that was actually issued, and puts the claim in front of an adjuster with the source documents attached. The adjuster still decides. What changed is that they started from the facts instead of assembling them.
- Every extracted field keeps a link back to the page it came from
- A low-confidence read gets flagged instead of quietly filled in
- Nothing is closed, denied or paid without a person
- Input, output, model, version and reviewer are all written down
The rules we build inside
This is where AI in a licensed business differs from AI anywhere else. We design to these constraints from the start, and they hold whether or not anyone asks about them.
What we hold to
- A person decides anything adverse to a consumer: a denial, a rate, a claim outcome
- The model reads and drafts; your system of record still holds the truth
- An uncertain read is shown as uncertain and goes to a human instead of into the field
- Your customers' data is not used to train anyone's general-purpose model
- Every automated step can be switched off without stopping the process around it
What you can show afterwards
- Which model and which version produced a given output, and when
- The exact input it saw and the output it returned
- Who reviewed it, what they changed, and what they approved
- How the process behaved before the automation, so the change is measurable
How this actually gets adopted
Nobody in a licensed business should switch a model on across a live process and hope. This is the sequence, and it is deliberately slow at the start.
Pick one narrow job
One document type, one queue, one decision point. Narrow enough that you can tell whether it worked.
Run it beside the people
The model does the work, your team keeps doing it too, and the two get compared. Nothing downstream depends on the model yet.
Put a reviewer in the loop
The output becomes a starting point that someone accepts, edits or rejects. Their edits are the measurement.
Widen only what earned it
Where accuracy holds, review gets lighter on the easy cases and stays on the hard ones. Where it does not, that job goes back to people and we say so.
What this will not fix
Worth saying plainly, because most of what gets sold as AI into this sector quietly assumes otherwise.
- A process nobody has written down. Automation needs a defined path; it will not invent one for you.
- Data your systems never captured. A model cannot recover a field that was never there.
- A compliance obligation. The requirement stays yours. Automation changes how you meet it. You still have to meet it.
- A vendor contract that will not let your data leave. That is a commercial problem before it is a technical one.
Start with one queue
The two-week assessment covers exactly this: where automation would pay, where it would be a liability, and what it would take to run inside your obligations.