Meridian Bank's forecasting team was relying on quarterly spreadsheet models with a 12% error rate—costing $4.3M annually in over-stock and under-stock decisions. Leadership needed a real-time, AI-powered solution that could integrate with their existing Oracle infrastructure.

Lumina's data science team conducted a 10-day diagnostic of Meridian's historical data (5 years, 14 data sources). We identified three key predictive signals that had been systematically ignored by the legacy model.

We built and deployed a custom AI forecasting engine using gradient boosting and time-series neural networks. The model integrates with Oracle in real time, delivering 97%+ accuracy forecasts with confidence intervals and anomaly alerts.