If you decline someone, you generally have to tell them why, in terms specific enough to act on. That single obligation determines where a language model can sit in a credit process and where it cannot.
Adverse-action rules in the US, and GDPR Article 22 together with the revised Consumer Credit Directive in the EU, oblige a lender to explain a decline in terms specific enough to act on. Anti-discrimination and fair-lending rules oblige you to show the decision was not driven by a protected characteristic or a proxy for one. Under the EU AI Act, creditworthiness assessment of natural persons is treated as high-risk, with documentation, human oversight and record-keeping obligations that follow. This describes the engineering constraint; it is not legal advice.
A language model is not a credit scorecard and should not be used as one. It does not produce a stable, auditable reason code, its outputs vary across runs, and its decision boundary cannot be characterised in the way a regulator expects. Deploying an LLM as the decisioning function is the mistake this page exists to warn against.
Where it is genuinely valuable is everywhere around that decision — the parts of the process that are currently manual, slow and inconsistent, and that carry no adverse-action obligation of their own.
Extraction is verified, not trusted. Every figure the system pulls from a document carries a link to the source page and, where confidence is low, is flagged for human confirmation before it reaches a decision input. An unverified extracted number feeding a scorecard is a silent error with a regulatory tail.
Nothing the model produces enters the decision function without passing through a deterministic validation step your team owns. Schema, range checks, cross-document consistency — the boring layer that turns a probabilistic output into an auditable input.
And the whole path is recorded: which model version, which prompt, which documents, which extracted values, which human confirmed what. That record is the deliverable that makes the system defensible, and it is far cheaper to build in than to reconstruct.
statements · payslips · accounts
every figure linked to its page
schema · ranges · cross-document
human check on low confidence
deterministic, explainable
from the scorecard, never the model
To phrase an explanation in plain language for a customer, yes — from reason codes your scorecard already produced. To generate the reasons themselves, no. The explanation must be a faithful account of why the model decided as it did, and a language model writing a plausible-sounding rationale after the fact is producing something worse than nothing.
No. It classifies creditworthiness assessment of natural persons as high-risk, which brings obligations rather than prohibition: risk management, data governance, documentation, logging, human oversight and accuracy monitoring, alongside conformity assessment and registration, with a carve-out for fraud detection. Deployer-side duties, including a fundamental-rights impact assessment for credit institutions, sit with you. The document-processing work described here is likely to be a preparatory task rather than the high-risk decision itself, which your own provider and deployer assessment should document. The classification and the obligations that follow are for your legal and compliance functions to confirm; this is not legal advice.
It varies by how document-heavy your product is and we would rather not quote a number we have not measured on your process. What we would do is measure it on a sample of your real applications before you commit to a build.
The document work is where the time goes. The decision boundary should stay where a regulator can see it.
30-minute technical call · no deck
What we built, what stage it is at, and a link to the live thing wherever there is one.
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Live at coherence.com: brand identity generated with AI agents we directed, the motion system and website for the group behind Sonora, AboutAI, WakeUp and MudShadows. Agent work remains in build.
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Apps, platforms and campaigns for these teams, long before the AI work. Every one is written up on the work page — what we did, and where our part ended.
