NexML is an AutoML + MLOps platform for banks, lenders, and credit unions. Build, deploy, and monitor ML with continuous drift detection, explainable AI for risk management, and examiner-ready documentation — while data stays on your servers.
NCUA compliance tools
Data stays in your environment
Works with AWS
NexML unifies data/feature management, model training, deployment, monitoring, and documentation behind rigorous machine learning governance in credit unions and financial institutions.
Result: fewer audit surprises, faster remediation, lower losses, and smoother collaboration across Risk, Finance, Data, and Tech without sending data off-prem. Our credit union AI solutions ensure regulatory AI tools work seamlessly with your existing infrastructure.
Drift detected late, fragmented evidence, board/exam pressure, struggling with credit union model risk management requirements.
Continuous monitoring, KRIs for drift/stability/bias/overrides, SoD approvals, examiner-ready binder in one click following NCUA model risk guidance 2025.
Alerts & workflows, model cards, change logs, champion-challenger under governance for audit-ready AI.
Manual, scattered documentation, and tests affecting model risk management efficiency.
Versioned lineage, reproducible validation packs, exportable binders with credit risk model monitoring software.
Model registry, dataset/feature versioning, bias & backtesting reports for continuous model validation that finance teams need.
Rising charge-offs, compliance costs, vendor sprawl impacting how to detect model drift in finance operations.
Loss avoidance via early alerts; lower TCO vs stitched tools; measurable ROI with model monitoring AI.
ROI snapshot, remediation SLAs, coverage metrics.
Data residency, integration friction, security posture for MLOps for financial institutions.
Hybrid/on-prem deployment, SSO/RBAC, SIEM & KMS integrations; AWS/Azure/GCP/Snowflake/DBX/BigQuery-ready regulatory AI tools.
Kubernetes support, API/SDK, Git/CI-CD integration.
Bottlenecks, handoffs, and reproducibility in how to detect model drift finance.
Feature store, registry, CI/CD; keep Python/notebooks while adding governance through AutoML for the credit unions platform.
Pipelines, versioned artifacts, champion-challenger automation.
Policy slippage, cut-off drift, high manual overrides affecting credit union AI solutions effectiveness.
Reason codes, policy what-ifs, challenger promotion with rollback through explainable AI for risk management.
Cut-off simulations, per-segment drift, override analytics.
Connect to core banking, LOS/LMS, cards/txn, bureau data. Governed feature store with reuse and time-travel for AutoML for credit unions.
Classification, regression; automated feature engineering & hyper-opt with leakage/imbalance guardrails. Specialized credit risk AutoML credit unions can trust.
Batch & real-time scoring, champion-challenger, canary releases, rollback capabilities designed as regulator-friendly AI for banks/credit unions.
Advanced model drift detection, stability (PSI/CSI), bias monitoring, performance SLAs; comprehensive model governance software that US credit unions require with registry, lineage, approvals, and immutable audit logs.
RBAC, SSO/SAML, SIEM hooks, BYO KMS/HSM; on-prem/VPC and air-gapped options ensuring credit union compliance with AI standards are met.
Auto, cards, unsecured lending with automated decisioning and loan default prediction AI
Cards/ACH/ATO protection with fraud detection, AutoML credit unions need
Reduce false positives and streamline compliance with AI compliance solutions for credit unions
Cross-sell propensity and dynamic limit optimization
Stress testing and portfolio risk management
Collections prioritization with predictive analytics
Validation & documentation effort reduction with audit-ready machine learning credit unions
Drift remediation timeline improvement through automated model monitoring tools
Reduction in deployment failures using MLOps for credit unions
Savings on AutoML infrastructure costs for credit union model risk management
Weeks spent manually tuning models, complex handovers between data science and engineering teams are key reasons why models fail audits that credit unions face today.
No—hybrid/on-prem deployment ensures data remains on your servers/VPC. You maintain complete control over your data and intellectual property while meeting credit union compliance AI requirements.
Yes—model cards, lineage tracking, approval workflows, and audit logs export to PDF/HTML format for regulatory review, fully compliant with NCUA compliance tools standards and audit-ready AI requirements.
Yes—local explanations generate compliant reason codes for regulatory requirements and customer transparency through credit risk AutoML credit unions.
Built-in diagnostics with configurable thresholds, automated alerts, and mitigation workflows to ensure responsible AI deployment as part of our comprehensive AI risk management approach.
Yes—register, monitor, and document your existing models alongside new NexML models in a unified model governance software US credit unions can rely on.
Yes—keep your existing data science stack while we add governance, deployment capabilities, and production monitoring through our MLOps for financial institutions platform.
