An AI underwriting assistant has reviewed a single purchase-mortgage application and proposes a high-blast-radius 3-action bundle — deny + send adverse-action notice + close file. This page is a guided demo: pick a persona, review the file, submit the AI's proposal to HELM, see the deterministic decision, then take the next-step action that belongs to your role.
Four steps. Each persona sees the same loan file differently and takes different next-step actions. Try at least two personas to feel the architecture.
Five roles in the same lender. Each has different authority, different views, and different next steps.
Same application file for everyone — but the framing card shows how your role reads the situation.
The deterministic gate evaluates 6 checks. The eval is the same regardless of persona — HELM is not opinion-based.
After the decision, your persona panel on the right shows what you can actually do next. The interactive ones expand inline.
A mid-size retail bank, a community bank, or a credit union can't answer an examiner with "the model decided." When AI touches a credit decision, the burden of proof is yours — reason by reason, applicant by applicant. HELM is the layer that lets you carry it. Not a policy you promise to follow; a control that is structurally true.
No black-box denials. Each decision carries its specific principal reasons, the evidence it rested on, and the config it ran under — signed and chained. An examiner reads it the way you'd want to be read.
A notice that doesn't meet Reg B §1002.9 — specific reasons, 30-day timing, FCRA score disclosure — cannot send. The gate is the control, not a checklist someone forgot.
ZIP, surname, age, source-of-income type — no tenant can wire a prohibited-basis proxy into decision logic. The sacred line isn't a guideline; the rule engine rejects it. always block.
The same signed evidence chain answers OCC, FDIC, NCUA, and CFPB. Model risk (SR 11-7), fair lending (ECOA), and ability-to-repay (Reg Z) all read from one source of truth.
KAiM's founder spent 25 years inside regulated banking — the Federal Reserve Bank of San Francisco and Wells Fargo's regulatory-remediation program, during the most scrutinized period in the bank's history — building data lineage, model-risk, and continuous-monitoring frameworks. HELM puts governance into the operating model, not on top of it. This is the control we wished existed on the buyer's side of the exam.
The way KAiM engages is concrete: pick one high-risk AI-assisted decision you're worried about — mortgage underwriting, auto approvals, small-business credit, adverse action, exception handling, deposit/fraud holds. We map it to actor / action / authority / policy / evidence / escalation, then show how HELM gates it. No deck. No demo theater. A working session with your underwriting and compliance teams.