Data Science at a Global Retail Bank



A global retail bank wanted us to identify the impact introductory offers applied to their products had on putting customers into financial difficulty.

What We Are Doing

  • Using predictive analytics to identify gaps in the design, approval and maintenance of financial products that could put customers at risk
  • Pre-empt and identify customers in financial difficulty appropriately and identify failure to meet regulatory post-sale service 
  • Support the modelling design using data warehouses
  • Aggregating and consolidating information from multiple sources
  • Cleansing and processing data so it’s ready for visualisation
  • Cost-efficient approach to using 100% of the population data to provide detailed risk insights and assurance not achievable through traditional monitoring approaches, or without the investment of significant resource 
  • The outcome being a repeatable model that can be deployed across separate business lines and geographical regions

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