Scienaptic Systems Inc.

224W, 35th Street, Suite 800, New York, NY 10001 Ph: +1 212 244 4030

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aspects of long-range financial forecasts.

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.

Data and 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

Less than a week’s planning cycle.

Generate a suite of challenger ML models.

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.

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Scale, speed and efficiency.

In just a few weeks.

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predictive underwriting
risk models for banks
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neural network credit models
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Pankaj Kulshreshtha
Kevin Oden
Ray Duggins
Subbu Venkataramanan
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Douglas Merrill
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AI ML credit underwriting
Future of Credit underwriting
Ai ml credit platform
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underwrite.ai

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credit management
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credit risk modelling
credit risk monitor
credit score companies
credit underwriting models
internal credit risk models
loan application underwriting process
loan underwriting

predictive underwriting
risk models for banks
trends in credit underwriting
underwriting
underwriting process
underwriting risk analysis
neural network credit models
credit line increase
Pankaj Kulshreshtha
Kevin Oden
Ray Duggins
Subbu Venkataramanan
Pankaj Jain
Douglas Merrill
Zest finance

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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.  

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.