FlatworldEdge was engaged by a banking client to combat rising credit card defaults. The goal was to develop a predictive model for anticipating default behaviors and classifying delinquency levels.

The model was successfully created and integrated, improving the hit-rate by 8 base points, providing personalized insights, and seamlessly fitting into the client's existing IT systems, saving time and cost.

Story of the Customer 

A top financial firm was struggling with increased credit card defaults, threatening their financial stability. To tackle this, they sought a predictive model to identify and categorize potential defaulters.

The challenge involved creating and integrating this model into their existing IT systems seamlessly, without causing disruptions.

The Challenge 

  • The client grappled with the challenge of the need for a new model that can accurately predict default behaviors of credit card customers in the next 12 months.
  • Another challenge is the creation of clusters based on the level of delinquency, which requires complex data analysis and modeling.
  • Lastly, there is a challenge of integrating the new predictive model seamlessly with the existing IT systems without causing any disruptions

The Solution 

  • Predictive models were refined for better risk forecast of credit card defaults.
  • An 8 base-points rise in hit-rate was achieved, enabling data-driven customer insights.
  • Seamless integration with current IT infrastructure led to significant time and cost savings.

The Result 

  • The predictive model enhanced the hit-rate by 8 base points, reducing potential defaults.
  • By leveraging historical data, the model provides personalized customer insights, boosting decision-making and productivity.
  • The model was integrated seamlessly into the existing IT system, saving significant time and resources.