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Project to identify the profiles of customers about to leave


With many countries introducing consumer protection laws to allow customers to change banks more easily, churn is a major challenge for the financial services industry.

Banks need to be able to detect weak signals that indicate which customers are likely to leave so that they can take action to boost retention.

They also need to be able to tailor retention efforts to specific customer profiles.

The project

Development completed on the customer’s big data environment (Dataiku)

Machine learning: gradient boosting and decision tree (GBDT)


Probayes developed a configurable, explainable model that can identify customer departures more than a year in advance

Six customer churn profiles were identified

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Last name First name

Position – Company

Success stories:


Detect credit card fraud

Deux personnes se serrant la main


Detect customers that are likely to leave


Give customers and employees a conversation agent