Industries · Finance · Credit Unions
Say yes to more members, without loosening the standards you're examined on.
Credit unions are expected to serve members the fintechs won't touch, and to stand up to the same model-risk scrutiny as a bank — usually without a bank's data science team. We close that gap: governed, explainable models across lending, fraud, and collections, running on your own servers, and available with our team operating them if you don't have your own.
A model that decides who gets a loan should be one you can explain — to a member, and to an examiner.
01 · The Credit Union Reality
The same scrutiny as a bank. Rarely the same resources.
Credit unions carry a member-first mandate and a full model-risk burden at the same time — often on legacy systems and lean teams. That combination is where good intentions stall.
Serving members others decline
Self-employed, thin-file, and near-prime members deserve a fair look — one you can defend on fair-lending grounds.
Model risk without a model team
NCUA and SR 11-7 expectations don't scale down because you're smaller — but hiring a full ML function rarely pencils out.
Decisions that drift by branch
When judgment lives in people, standards vary across underwriters, branches, and cycles — and that's hard to evidence.
Evidence assembled after the fact
When documentation isn't produced at decision time, every exam becomes a reconstruction project.
02 · Where Decision Intelligence Helps
Across the decisions your credit union makes every day.
Each is a model on one governed platform — trained on your own history, explained at decision time, and documented for review. Add decisions without adding vendors.
Underwrite self-employed and non-salaried members with a model trained on your own approvals — reflecting how your best underwriters already reason, with every decision explained.
Extend fair, consistent decisions to near-prime members on documented factors, and catch vehicle-market shifts through continuous monitoring rather than at the next review.
Flag synthetic identities and documentation inconsistencies in real time — protecting members and the balance sheet, with a complete audit trail behind every call.
Surface members drifting toward delinquency months early, so outreach is timely and supportive — prioritized by predicted loss, not guesswork.
Spot members disengaging before they leave, and target retention and deposit programs where they actually change the outcome.
03 · Built for How Credit Unions Actually Operate
You shouldn't need a data science department to get the models.
Hiring and keeping a full ML team is out of reach for most credit unions this size. That's a resourcing reality, not a failing. So this comes two ways.
A platform your team runs
Your analysts and risk team keep their tools and gain a governed layer for deployment, monitoring, and examiner-ready documentation — on your own servers.
Or a platform our team runs for you
We build the models, operate the pipelines, own the monitoring, and produce the audit evidence end to end. As your own capability grows, your people take on more, whenever you're ready.
The models run on NexML — governed by design.
Every model your credit union runs, built, deployed, monitored, and documented on one platform aligned to NCUA and SR 11-7 — on your own servers, with no data egress required.
04 · How We Start
Build it together. Prove it first.
We build your first model at no upfront cost. You test it in your own environment. You move to production — and pay for the decisions it makes — only once it's earned it.
Discovery
Define the decision, the data, and what success looks like.
Build
We develop and validate on your historical decisions, governance designed in.
Pilot
The model runs in your environment. You test it against live decisions.
Govern
Audit trail, documentation, and monitoring set up alongside it.
Scale
Once it's proven, move to production at your own pace.
Aligned incentives. Usage-based pricing means you pay for the decisions a model makes in production — not a fixed license, and nothing upfront to prove the first one. We win when you win.
05 · FAQ
What credit union leaders ask first.
The questions that come up before a first conversation. Don't see yours? Talk to a senior team member.
01Is this aligned with NCUA model risk expectations?+
Yes. Models are built around NCUA model risk guidance and Federal Reserve SR 11-7 principles. Documentation, lineage, explainability, and maker-checker approvals are generated at decision time and assemble into examiner-ready binders. It supports your compliance posture; it doesn't replace independent validation.
02We don't have a data science team. Can we still use it?+
Yes — this is the common case at this size. Our team can operate the platform for you, building models and owning monitoring and audit evidence end to end, while your own capability grows into as much of the work as you want.
03Can it help us lend to self-employed or thin-file members?+
Yes. Models are trained on your own historical approvals to reflect how your best underwriters reason — tax returns, business structure, cash flow, reserves — with every decision explained and documented for fair-lending review.
04Where does our member data live?+
On your servers. The platform is hybrid or on-premise by design — no member-data egress to a third-party cloud at any stage. SSO, RBAC, SIEM, and KMS integrations are supported.
05How does pricing work?+
Usage-based. You pay for the decisions models score in production. The first model is built in a pilot at no upfront cost, and you scale only once it's proven itself in your environment.
06Does it replace our core or existing systems?+
No. It connects to your existing data and core systems and adds the governance, deployment, and monitoring layer around them — it doesn't force a rebuild of what already works.
Ready when you are
Start with the decision that's costing you the most.
A 30-minute conversation with a senior team member who understands credit unions, your examiners, and the members you're trying to serve. We'll help you pick the first model — and prove it before you commit to anything.
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