Case study

How We Created Impact for a Leading Global Retail Bank by Preventing Massive Fraud Losses Leveraging Our AI/ML Driven Models & Strategy Rules

Fraud Management Framework


  • Our client had a legacy fraud management system with 100s of rules stacked over a period plus traditional models leading to high false positive rates and customer dissatisfaction​
  • Using our AI/ML-driven models and strategy rules, we were able to help our client prevent massive fraud losses and reduce false positive rates​


  • We created intelligent features using the historical transaction data and accumulated it​
  • We created futuristic-kind data-driven segments and built models for each segment​
  • A closed loop fraud model strategy was implemented to detect outliers and identify emerging anomalous patterns in the transactions made by the customers​
  • We implemented advanced AI and self-learning models enabling us to reduce false positives​



Robust fraud models to reduce false positive & increase fraud detection rate for a leading US credit card issuer


  • Reduced decline rates by 100-150 bps resulting in £16M-20M incremental turnover ​
  • Over 40% reduction in false positive rates​



  • Significant improvement in both high-level transaction fraud detections and false positive reductions​
  • Improved payment acceptance rates leveraging card acquisition data 
  • 40% increase in fraud capture
  • 70% decrease in hold rates
  • 50% reduction in sales declines

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