Models for Processors, Acquirers, and Issuers

Merchant Churn Models

These advanced models use transactional data to calculate the probability for a given merchant to switch to another acquirer (business churn). It allows the processors to be precise on the market actions, applying differentiated fees or marketing efforts specifically for the merchants that are about to leave. These models use the same core technology used by us to calculate market movements, with a high capacity to process large volume of live data.

Account Profiling Financial Score Models

These advanced models use transactional data to calculate the financial score of all transacting customers, anonymised or not. We can calculate the financial capacity of a plastic (card number) or the identified client view (if available), and associate them into financial scores to determine the best clients transacting on the base.

 

It allows to identify and rank the best cardholders at an individual level, how much they generate under which conditions, and which merchants hold these customers.

High-Potential Issuing Models

These models calculate the perfect cluster of users that combine the probability of a) accepting the offer; b) activating the card; c) using the card. It allows the issuer to be more precise on investing in market campaigns, and issuing cards to those who will more likely use them (revenue-driven selection).

 

This probability is calculated using a combination of several models, including Financial Score, Profiling (hyper-personalisation), and future Next Best Actions (NBA). The engine that predicts NBA is based on our proprietary market (investing) models – one of the most complex and powerful self-learning predictive AI algorithms available. It calculates 1 million different future  scenarios per 1 second based on complex transactional data, and automatically decides which one is the best to use, sensitive to the context and goals.

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