AI Lending & Loan Origination Development

Reference architecture

1. Loan Application
2. Identity & Income Verification
3. Alt-data Enrichment
4. Underwriting Decision Engine
5. Decision (approve / counter / decline)
6. Adverse-action / Offer Notice
7. Audit Log

A typical automated origination flow: the application feeds identity and income verification, alternative data enriches the applicant profile, and a decision engine returns an approve, counter-offer, or decline. Declines route to an ECOA-compliant adverse-action notice, and every decision is written to an immutable audit log for fair-lending review.

Frequently asked questions

• Can you support alternative-data underwriting for thin-file applicants?+

  Yes. We ingest bank-transaction, cash-flow, income, and payroll data (Plaid, MX, Finicity, Argyle, Pinwheel and similar), categorise it, and turn it into underwriting features. That lets your model score near-prime and thin-file applicants a bureau score alone would decline, while keeping every derived feature explainable.

• How do you handle adverse-action notices and Reg B?+

  Every decline or counter-offer carries reason codes generated from the same features that drove the decision, and we assemble the ECOA/Reg B adverse-action notice from those codes. We build the pipeline; your compliance and legal teams own the credit policy those codes describe and the final notice content.

• Can your models be reviewed for fair-lending / disparate impact?+

  That is the point. We favour explainable decision engines and ship per-decision feature attribution plus the tooling to run disparate-impact testing across protected-class proxies. The engineering makes review possible; the fairness threshold and the legal sign-off are yours.

• Do you integrate with credit bureaus and loan-servicing systems?+

  Yes: Experian, Equifax, and TransUnion on the bureau side, and LoanPro, Peach, or Canopy on the servicing side. We wire origination into your LMS so an approved application becomes a booked loan without manual re-keying.

• How do you roll out credit-policy or model changes safely?+

  Policy rules and models are versioned, and new models ship as challengers against the current champion, back-tested on historical applications and shadow-scored in production before any promotion. Every change is auditable and reversible, so a policy tweak never becomes an untraceable outcome.