How a Leading Finance Firm Transformed AI Delivery with NexML

Challenges

Despite significant investment in data science talent and infrastructure, the client faced major roadblocks in operationalizing AI:

• Slow ML Deployment Cycles

  Model development was manual, time-consuming, and lacked standardization.

• Lack of Continuous Monitoring

  Once deployed, models operated in a “set and forget” mode, with no live performance tracking or drift alerts.

• Regulatory Blind Spots

  Compliance teams lacked access to explainable models and end-to-end audit trails.

• Limited Collaboration

  Silos between data science, IT, and business units led to inconsistent model governance and handoff issues.

The result? Valuable models remained underutilized, and the business couldn’t respond fast enough to market or regulatory shifts.

Solution

To address these challenges, we designed and deployed NexML — a modular AutoML + MLOps framework tailored for the financial sector.

Key components of the solution included:

• AutoML Engine

  Enabled automated feature engineering, model selection, and tuning using pre-configured financial use-case templates — reducing development time by over 70%.

• CI/CD for Machine Learning

  Introduced GitOps-style pipelines that triggered automated testing, deployment, and rollback of models — aligned with existing DevSecOps processes.

• Model Registry & Version Control

  Every model version was logged, tracked, and approved via a centralized registry — ensuring traceability and approval workflows across teams.

• Integrated Explainability & Audit Trail

  NexML integrated explainable AI (SHAP, LIME) for transparent predictions and auto-generated documentation to support regulatory audits.

• Domain-Centric Use Case Layer

  Delivered out-of-the-box support for key financial applications: credit risk scoring, fraud detection, churn prediction, and customer segmentation.