“critical to quality”
aspects of long-range financial forecasts.
Data & Segmentation
Forward looking / lead indicators
Campaign specific outlooks
Unique ML methodology
Ensemble of ML techniques and methods
Standards for best Model selection
Confidence intervals around the forecasts
Ether forecasting engine
Full forecasting solution via Ether platform which integrates seamlessly with bank's data and batch processes
Is your approach accurately forecasting potential revenue and losses? Scienaptic brings together the key pillars of data, machine learning (ML) and proprietary forecasting engine to drive efficiency, sensitivity and robustness in the forecasting process.