Enterprise Data & AI Analytics Platform

Enterprise Data & AI Analytics Platform

The problem

A regional banking group was data-rich but insight-poor. Every function had its own reporting, every report had its own definitions, and every executive review started with arguments about the numbers. Predictive use cases like credit risk, fraud, and customer next-best-action were trapped in pilots because the data foundation underneath couldn't sustain them.

The mandate: design and stand up a unified data platform with embedded AI analytics, with the governance to make finance, risk, and operations actually trust it.

The approach

A single engagement covering platform, models, and governance.

• Unified data platform. A modern lakehouse architecture pulling 20+ sources into a governed, query-friendly layer, replacing the per-function data marts.
• Self-serve BI. A semantic layer with shared definitions, so the same metric means the same thing in every dashboard.
• AI/ML models. Six predictive models taken from notebook to production: credit-risk scoring, fraud signals, churn, next-best-action, deposit forecasting, and operational anomaly detection.
• Governance framework. Data ownership, access controls, model risk management, and the decision rights that make the platform trustworthy at audit.
• Operating model. The platform team, model-ops cadence, and chargeback approach that keep the platform sustainable.

The impact

• Reporting time cut by 75%. Analysts spend their hours on analysis, not on reconciling data.
• 20+ source systems unified into one governed data layer.
• 6 AI/ML models in production, moved from pilot to live decisions, with monitoring and a re-training cadence.

The bank now operates a single trusted source of analytical truth, with the platform foundation to keep adding AI use cases on top of it.