Credit scores are a starting point, not the whole story.
We help risk teams combine what you already hold—portfolio history, internal rules, memos—with external data you trust. Output fits how you decide: flags, explanations your committee can read, not a black box for its own sake.
Trusted by 10,000+ founders & business owners.
What we can build for you by next week?
We’re not selling a generic “AI credit engine.” We wire workflows to your documents and tables so scoring and narratives match how your risk function actually works.
Beyond the bureau score
Moves past a single external number. Layers your loan history, behaviour, and the market signals you approve so default views reflect how you actually lend—not only what a score says.
Portfolio hotspots & drift
Surfaces concentration, sector creep, and early warning patterns before they land in a quarterly deck. Built from your definitions of “material,” not a generic dashboard.
Memo-ready narratives
Pulls facts from memos, tables, and policy excerpts into draft language committees can edit. Cuts the blank-page problem. Doesn’t replace sign-off.
Explainable flags for audit
When a case gets tagged, the trail points to your policy text and data inputs. Useful when someone asks “why did this surface?” six months later.
Enterprise grade Features
Trust NexUp AI with your most sensitive data. We maintain the highest security standards and regulatory compliance to protect your business.
Why most AI projects fail (and why this doesn't)
Banks don’t lack models. They lack time to wire institutional knowledge into something teams will use every Monday.
From messy inputs to a usable risk workflow in about a week
Longer than a chatbot because credit context is heavier. Still short versus traditional IT programs.
Create Knowledgebase
Policy docs, prior write-ups (redacted as needed), definitions of good/bad outcomes, approved external feeds.
Customize with AI
How conservative to be, audit trails, what must never be inferred.
Launch your Agent
Shadow mode vs. human decisions first, then narrow production use cases.
