Hunt Score is HL Hunt’s attempt to package underwriting as an API

HL Hunt Financial’s Hunt Score is an AI underwriting engine sold to other lenders and platforms rather than a consumer credit product. The company describes a white-label system that returns an approve, decline or refer-to-manual decision in under 250 milliseconds and can be embedded through a widget or headless REST API.

The engine is designed to combine traditional bureau information with alternative signals. HL Hunt says Hunt Score can use FICO and VantageScore data, tradeline history, bank-transaction information, rent payment history and utility records, among more than 1,000 signals. The stated purpose is to give lenders more information on thin-file or no-file applicants, but more data does not automatically make a decision more accurate or fair.

HL Hunt markets the product across more than 55 use cases, from BNPL, auto loans and leases to rent screening, medical and education finance, gig-worker onboarding, business term loans, trade credit and commercial insurance. That breadth makes the product look more like decisioning infrastructure than a single credit model. Each use case still requires its own policy, data permissions, adverse-action explanations and validation.

Pricing is tiered by volume. Starter is listed at $495 per month plus $2.50 per assignment for up to 100 monthly assignments. Professional is $1,495 plus $1.75 per assignment for up to 1,000 assignments, and Enterprise is $4,995 plus $0.95 per assignment with no listed assignment cap. Custom risk models and business underwriting are included in the fuller tiers according to the company’s product materials.

HL Hunt’s comparison materials claim more approvals, lower defaults and a high pre-approval fraud-catch rate versus an unnamed traditional model. Those are company-reported figures; the product page does not provide an independent audit, test population or full methodology. Any institution deploying the engine remains responsible for data quality, fair lending, privacy, model governance, human review and clear explanations to applicants.