01
Models stall before production
The work is sound; the path from notebook to a live decision is the bottleneck.
Product · NexML · The institution-wide ML platform for Credit Unions, Banks & Lenders
Every model your institution runs, on one platform you can defend.
Most AI tools in finance solve a single decision. NexML runs all of them on one platform — credit and loan decisioning, fraud, collections, deposit attrition, member churn, and the models you haven't built yet. Build, deploy, and govern each one on your own servers, with the monitoring, explainability, and audit documentation regulated finance expects.
And if you don't have the team to run it, ours can.
You shouldn't pay for a model until it has earned its place in production.
01 · The Problem
Getting a model working is rarely the hard part.
Everything after it — production, accuracy, explainability, evidence — is where regulated institutions actually spend their effort. Those are the gaps a platform is meant to close.
02
Production gets hard to trace
When a decision is questioned, you should know exactly which model version made it.
03
Drift develops between reviews
Periodic checks miss the shifts a continuous view would catch within hours.
04
Evidence is rebuilt for every exam
Far easier when the platform captures it as each decision is made.
02 · What NexML Does
One platform, built for the models a financial institution actually runs.
Credit, fraud, collections, attrition, churn — each is a model, and each needs the same four things to run safely in a regulated environment. A new use case is a new model on the same governed platform, not a new vendor.
Capability 01
AutoML for regulated use cases
Pre-configured templates for credit scoring, fraud, churn, segmentation, and collections. Automated feature engineering and tuning cut development time by 70% or more — without sending data outside your organization.
Example output
A credit-risk model built, tuned, and documented in three days — with full feature lineage, reason codes, and a model card ready for review.
Capability 02
MLOps with lifecycle governance
Pipelines with automated testing, deployment, rollback, and champion–challenger promotion. Every version is logged and approved through a central registry, with maker-checker workflows built in.
Example output
A challenger promoted after clearing accuracy, stability, and bias thresholds — the old model archived in one click, full lineage and rollback path preserved.
Capability 03
Explainable AI for risk management
Local explanations generate compliant reason codes for adverse-action notices and customer transparency. Segment-level fairness analysis runs at every retraining step.
Example output
An auto-loan decline with a ranked, plain-English explanation of the top factors, a fairness check across protected segments, and the exact model version.
Capability 04
Monitoring & audit-ready docs
Real-time drift detection across inputs, prediction stability, and outcomes. Auto-generated model cards assemble into examiner-ready binders in one click, aligned with NCUA model-risk guidance.
Example output
A drift alert at 09:42, an owner assigned by 09:45, a challenger validated by 16:30, and the audit binder regenerated automatically that same day.
03 · Across Your Decisions
One platform, every credit model.
Each model gets the same governance, monitoring, and exam-ready documentation. Add models without adding vendors.
Lending
Residential Mortgages
Underwrite self-employed and non-salaried borrowers with a model trained on your own approvals — tax returns, business structure, DSCR, reserves.
Lending
Commercial & SMB
Decision limited-history, variable-revenue businesses consistently — with owner credit and industry risk factored in and documented.
Lending
Auto & Near-Prime
Score near-prime and subprime applicants on documented factors, and catch vehicle-market shifts through continuous monitoring.
Risk
Fraud & Verification
Flag synthetic identities and documentation inconsistencies in real time, with a complete audit trail behind every decision.
Portfolio
Collections & Loss
Surface accounts drifting toward delinquency months early, and prioritize outreach by predicted loss.
Growth
Member Churn
Spot members disengaging before they leave, and target retention where it actually changes the outcome.
Have a decision that isn't on this list? Most institutions start with the one that's costing them the most — we build that first.
04 · Platform, or Platform Plus Team
You shouldn't need a data science department to get the models.
Hiring and keeping a full ML team — data engineers, modelers, MLOps, someone who understands model risk — is out of reach for most institutions this size. That's a resourcing reality, not a failing. So NexML comes two ways.
Option A
A platform your team runs
Your data scientists keep their tools. NexML adds the governance, deployment, and monitoring layer around them — on your servers — so what they build ships and stays defensible.
Option B
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.
05 · Outcomes
What changes once every model runs on one governed platform.
Each figure is measurable, tracked in the platform, and reportable to your board, your regulator, and your finance team.
Hours, not months
Mean time to detect drift drops from quarterly reviews to hours through continuous, automated monitoring.
< 7 days
Remediation cycle time, with tracked owners, defined SLAs, and challenger-ready workflows.
50–70%
Validation cycle time reduced through auto-generated, audit-ready documentation.
100%
Every production model registered, monitored, and documented in one inventory — legacy and third-party scores included.
Same-day
Credit decisions move from days to same-day, with a defensible explanation attached to every outcome.
50–70% lower cost
Hybrid and on-premise deployment removes the per-API-call and data-egress charges of cloud-only AutoML.
Figures are typical of NexML deployments in regulated-finance environments — representative of the platform's impact rather than any single named institution.
06 · How It Works
Three layers underneath — built to fit your stack, not replace it.
Keep your existing data lake, notebooks, and CI/CD. NexML adds the governance, deployment, and monitoring layers around them, on your servers.
Layer 01
Data & features
Connects to your existing lake or warehouse — AWS, Azure, GCP, Snowflake, Databricks, BigQuery — with a versioned feature store reused safely across teams.
Layer 02
Training & registry
AutoML engine, model registry, CI/CD, and champion–challenger automation. Python and notebook-friendly, Kubernetes-ready, with API and SDK access.
Layer 03
Monitoring & governance
Continuous monitoring for drift, stability, and bias, with maker-checker approvals, immutable audit trails, and one-click examiner-ready binders.
Hybrid or on-premise by design. No data egress to a third-party cloud is required at any stage of the model lifecycle. SSO, RBAC, SIEM, and KMS integrations are supported for security-operations alignment.
07 · The Pilot
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.
1
Discovery
Define the decision, the data, and what success looks like.
2
Build
We develop and validate on your historical data, governance designed in.
3
Pilot
The model runs in your environment. You test it against live decisions.
4
Govern
Audit trail, documentation, and monitoring set up alongside it.
5
Scale
Once it's proven, move to production at your own pace.
Usage-based by design. You pay for what the models actually score in production — aligned to usage, not a fixed enterprise license. Net-new models are scoped as focused development work, and deployment on your own infrastructure means compute scales with your servers, not a vendor invoice.
08 · FAQ
The questions worth asking before you commit.
What buyers ask before booking a call about NexML. Don't see yours? Talk to a senior team member.
01What is NexML?+
An institution-wide ML platform for regulated finance. Where most AI tools solve a single decision, NexML runs every model an institution depends on — credit and loan decisioning, fraud, collections, deposit attrition, and member churn — on one platform that handles building, deployment, monitoring, explainability, and audit documentation. It runs hybrid or on-premise, as a platform your team operates or one our team operates for you.
02Can NexML govern models we already have?+
Yes. Register, monitor, and document your existing models alongside new NexML models in a single governance plane. The platform brings legacy and third-party scores under unified model risk management — not a rebuild.
03Does it replace our data science stack?+
No. NexML adds governance, deployment, and monitoring around your existing Python, notebook, and CI/CD environment. Your data scientists keep their tools. The institution gets the operating layer that ships and defends what they build.
04Is it aligned with NCUA, OCC, and the Fed?+
NexML is built around current US model-risk frameworks — NCUA model risk guidance, OCC examination standards, and Federal Reserve SR 11-7 principles. It supports your compliance posture; it doesn't replace independent validation or your own regulatory judgment.
05How is NexML priced?+
Usage-based (inference) pricing — you pay for the decisions models score in production, not a fixed license. Net-new models are scoped as focused development work, and on-infrastructure deployment means compute scales with your servers, not per-API-call or egress charges.
06Where does our data live?+
On your servers. NexML is hybrid or on-premise by design — no data egress to a third-party cloud at any stage of the model lifecycle. SSO, RBAC, SIEM, and KMS integrations are supported.
Ready when you are
We don't start with a contract. We start with a working session.
A 30-minute conversation with a senior team member who understands your models, your audits, and the decision you're trying to move. The proposal gets written around what you need — not a template.
No commitment30 minutesA senior team member, not a BDR
Founded 2020 · AI & ML engagements delivered across North America, Australia, and India · ISO 27001 certified