Banks · credit unions · auto and consumer finance
The 2026 AML/CFT programme proposals point examiners at whether your programme operates effectively, not whether it exists. Demonstrating that means producing, on demand, what was reviewed, on what basis, and what happened when it was wrong. For most institutions that evidence currently lives in people's heads.
The economics
Financial-crime compliance costs roughly US$61 billion a year across the United States and Canada, and about 57% of that is labour — people reading files. A mid-size bank runs an AML programme costing between half a million and three million dollars annually before a single enforcement finding.
None of that spend is discretionary, and almost none of it is defensible as a competitive advantage. It is the cost of proving that a file was looked at properly. Densery's argument is narrow: the reading, the checking and the writing-up can be completed by an agent, with the evidence attached, and the people can move to the exceptions.
The question is not whether your team reviews files correctly. It is how long it takes to prove that they did.
A consent order or MRA on BSA/AML. An announced merger. A newly appointed Chief AI, Data or Automation Officer. A BPO or legacy OCR contract inside twelve months of renewal. If any of those is live at your institution, the conversation is materially shorter.
The multilingual estate
A commercial loan file at a bank serving Chinese-American, Korean-American or Hispanic communities is not one document in one language. It is a corporate registration in simplified Chinese, a lease in English, a personal guarantee signed in Korean, and a bank statement scanned at an angle in 2019.
Generic tooling reads all of it, badly, and reports no error — which is the dangerous failure mode. The reviewer cannot tell the difference between a field the model read correctly and one it guessed, so the whole output has to be re-checked by hand and the automation saves nothing.
Densery scores every extracted field for confidence, routes anything below threshold to a named human with the source page attached, and records both the machine decision and the human override as evidence. The file is completed either way.
AI document processing and workflow automation running entirely on the bank's own hardware, with no customer data crossing the institution's boundary. A Vietnamese commercial bank's document estate is mixed-script, heavily scanned and regulator-supervised — structurally the same problem a US bank has with a bilingual loan file.
Auto and consumer finance
A funding package is a pay stub photographed on a phone, a utility bill, an insurance binder, a title, and a contract with four signatures in the wrong places. Someone checks each one against the deal, by eye, under a funding-time service level.
Densery completes that check: reads each stipulation, verifies it against the contract terms, flags the mismatch with the page and the field highlighted, and writes the result into the loan origination system. Where the captive's parent has a data-residency policy, it runs inside your walls.
Credit file, title and contract document automation, in production inside a captive auto-finance lender. For a US captive with a Japanese or Korean parent, this is a peer reference on the same corporate reporting line rather than a case study about a stranger.
Fit
| Institution | Size band | Why this band |
|---|---|---|
| Banks and thrifts | US$2B–100B in assets; sweet spot US$10B–50B | US$10B is where enhanced BSA/AML and consumer-protection obligations bite. Above US$100B institutions build in-house; below US$2B there is no dedicated operations-automation budget. |
| Credit unions | Above US$1B in assets | Consolidation is rapid and every merger is a forced document-estate migration with a budget already attached to it. |
| Auto and consumer finance | Above US$1B in receivables | Captives and specialist lenders where stipulation and title volume is continuous rather than seasonal. |
| Not a fit | Top-10 national banks; cloud-native neobanks | The largest institutions have in-house AI platform teams and multi-year enterprise agreements. Digital-native lenders already hold structured data and have no document problem to solve. |
The next step
A scoping session is not a demo. Bring twenty real files, redacted if you need to. We take three numbers off you — annual volume, fully loaded cost per file today, and what happens when the output is wrong — and hand back a one-page value case in your own KPIs.
If the arithmetic says we are not a fit, we will tell you in the room rather than six weeks later.
If your data can go anywhere and your documents are already clean and digital, you do not need us. Use a hyperscaler document API and spend the money on something harder.
Densery