By combining rich feature and algorithmic innovations, Ether creates treatment strategies in response to changes in customer behavior and portfolio performance to deliver the results you expect.

A better experience for customers.

Lower charge-off for financial institutions.

Data driven by


Common data model helps you get more out of your internal customer and performance data while giving you the tools to experiment with external alternative data. Powerful NLP engine creates predictive features from collector notes with minimalistic human inputs.

Don't guess.


Neural network enabled predictive collections model will score and segment customer accounts by exposure, risk, behavioral factors, and even willingness to pay and preferred contact channel. These models offer deeper, more nuanced understanding of at-risk customers to maximize collections efficiency.

Individualized collection strategies.

More targeted interventions.

Ether treats every customer as a “segment of one”, optimizing early self-cure identification, value-at-risk assessment, pre-charge-off offers, post-charge-off decision, contact time, channel, and frequency for every single customer.

Case Study

AI powered predictive analytics for better delinquency treatment. A case study on how Ether delivered lower delinquency, cost and attrition. 

Scienaptic Systems Inc.

50 West 47th Street, 16th Floor, Ste. 1614, New York, NY, 10036

Ph: +1 212 244 4030

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