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 — credit risk, fraud, 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 — and do it with the governance to make finance, risk, and operations actually trust it.

The approach

An end-to-end engagement covering platform, models, and governance.

• Unified data platform — modern lakehouse architecture pulling 20+ sources into a governed, query-friendly layer, replacing the per-function data marts.
• Self-serve BI — 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 re-training cadence.

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