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25%
Increase in Customer Experience
30%
Increase in data-driven decision-making
70%
Increase in weekly sprint automation

The Client

Our client is one of the largest credit card issuers in the United States of America and one of the United States' biggest financial services organizations.

The Challenge

The client was facing high delinquency rates and a meager collection for all delinquent accounts. On closer inspection, we found that all of their accounts were distributed randomly across different collection teams. Collection teams were unable to handle the growing volumes of tasks provided to them.

Solutions Provided

  • To cut down the number of tasks, we used Hadoop to structure the unstructured data to bring it into a relevant format.
  • We used IBM Watson for a detailed analysis of delinquent customers' past payment behavior and to develop a model to predict the propensity of payment at different stages of collection.
  • Using Advanced Analytics, we segmented the portfolio to develop a treatment strategy using the model score based on -
    • High Propensity of Payment: No collection calls for 30 days. Most of the customers pay on their own.
    • Med Propensity of Payment: Maximize collection efforts. Drive high payments
    • Low Propensity of Payment: Reduce losses. Enable Treatment solutions like settlement/ restructuring.

Brand Impact

  • Our segment portfolio strategy resulted in a 25% Increase in Customer Experience.
  • Our structured data led to a 30% Increase in data-driven decision-making for the collection teams.
  • Our solutions led to a 70% Increase in positive user interactions and user retention.
  • Our automated solution handled the data and provided tasks to collection teams that resulted in a 70% Increase in productivity in weekly sprints.

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