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Merchant Fraud Risk Score Development

3AI August 17, 2020

A leading payment processor

Problem Statement

  • Modify existing fraud rules in the system to improve the performance
  • Reduce false positives (Detection Noises) in merchant fraud detection model without compromising on adverse action rate

Analytics Led Approach

  • Developed a score to identify potentially fraud merchants from the rule-based detections
  • Identify a cut off for direct adverse action and another for risk based due diligence
  • Implemented the scoring model and score-based rules in the rules engine, Blaze Advisor

Business Impact

  • 15% reduction in review cost due to false positive reduction
  • Another 5% review cost savings for direct adverse action recommendations
  • A web-based monitoring system developed to track benefits of the strategy through monthly reports

Critical Success Factors

  • Knowledge of fraud rules
  • Expertise in various analytics techniques like decision tree, logistic regression etc
  • Ability to identify fraudulent patterns based on historical data analysis
  • Continuous monitoring and refinements of fraud rules for improved fraud detection over time

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