Underwriting agents: what they may propose, who signs, what is recorded

A model can read an application and propose a decision. Whether that decision may be made, on what terms, and who has to sign it is a question the rules answer, not the model. This page walks that line for a lending workflow, using the rules and the runnable example this site already publishes.

BEAP v0.1 · Draft / Design Partner ReviewDated 2026-09-11. A draft the banks reviewing it can still change. Not a finished standard, and it may change before v1.0.

Which underwriting, and where

Credit underwriting: a lender deciding whether, and on what terms, to lend. Not insurance underwriting. The draft's areas are banking areas, and none of them is a policy of insurance. No market is named: the example amounts are in the currency the made-up fixtures use, and the rules are made up with them. The draft does not interpret regulation and does not claim to satisfy any of it.

What the agent may propose, and what only the rules may make true

Let models recommend. Let policy decide whether the credit decision may proceed, and who has to sign it.

In a lending workflow there are four moments where a proposal becomes something the bank has done. Each is a separate action with its own sign-offs, and an agent proposing one never gets to make it true on its own:

• Approving the loan: the credit decision itself.
• Creating the facility once the decision is made.
• Changing the rate or the term before the money goes out.
• Releasing the money to the customer's account.

Raising a limit on an existing facility is its own area with its own chain; the home page shows it beside the others.

Who signs

The card below is rendered from the same rules the runnable example is checked against, so the thresholds here cannot drift from the ones the code enforces.

What a sign-off cannot do

The lender example turns away a credit officer's sign-off pasted from one loan onto another, an amount raised after the sign-off, and an unverified customer with every sign-off in place; the examples page lists all of them. One case matters most for an underwriting agent: if the affordability assessment is missing, the decision waits for it. The rules name the missing input, and a sign-off does not fill the gap. When an assessment has been done and failed, a credit officer decides; that is a decision about the customer, and it is signed as one.

What is recorded

The draft publishes one worked example as five filled-in files. It is a disbursement of 250,000 run under the draft's own made-up rules, which ask two officers to sign at that amount; the lender example's rules ask one. The two are different made-up policies, and the files are the draft's, not the example's.

• A loan disbursement instruction, filled in
• A set of sign-offs from two approvers
• An authority to execute one disbursement
• The record of one disbursement attempt
• An auditor's extract

When the core system goes quiet after the instruction was sent, the record says so. "We do not know" is an answer, and never a reason to send it again.

What a run of the example proves, and what it does not

The lender example runs against made-up customers and a made-up core system and exits with an error if any attack gets through. That shows the boundary held. It shows nothing about whether the credit decision was right, fair, or acceptable to any regulator; those are your model's and your compliance team's questions, and no run of this example answers them.

What runs where

A bank adopts this one step at a time: first watching what the rules would have said, then binding the sign-offs, then enforcing. What adoption takes walks the three steps. What the Decionis platform itself runs today for financial infrastructure, and what it is building with design partners, is stated on its own page; this page does not restate it. Links checked 2026-09-12.

Bring one underwriting workflow

If your bank runs a credit decision with an agent in the loop, or is deciding whether to, hold the draft against it or write to beap@decionis.com. Please bring made-up values. Real customer data, real policies, and credentials do not belong in a review.