Drive

“critical to quality”

aspects of long-range financial forecasts.

Data & Segmentation

  • Forward looking / lead indicators

  • Campaign specific outlooks

  • Macro-environment linkages

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.

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Less than a week’s planning cycle.

Generate a suite of challenger ML models.

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No one technique fits all types and length of data. Deploy a range of ML techniques for forecasting such as SARIMAX, Long-Short Term Memory (LSTM), Age-Period-Cohort (APC), Generalized linear Model (GLM), Cox PH to name a few.

Scale, speed and efficiency.

In just a few weeks.

Case Study

A leading US bank was unable to accurately forecast aging losses due to underestimation of realizations, observing over 16% variance to actuals. Scienaptic’s Ether platform was able to achieve higher accuracy using bleeding edge ML techniques. Read the case study to understand more.  

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Whitepaper

Hitting the Bull’s eye: A practitioner’s note on forecasting Retail Credit Losses.

 

Science of making accurate financial forecasts in the environment of post 2008 crisis regulations. Learn how AI & ML Time Series methods are being leveraged to get near 99% accuracy in predicting financial (loss, revenue) forecasting outcomes.

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