Fraud Detection and Prevention Enhancements


Enhancing fraud detection for ZendNow by integrating machine learning models and customizable fraud rules.

Project Scope

The project aimed to enhance ZendNow’s fraud detection and prevention capabilities by integrating advanced machine learning models, customizable fraud rules, and an intuitive dashboard for monitoring flagged transactions. The goal was to minimize revenue loss due to fraud, improve detection accuracy, and empower merchants with greater control over fraud prevention.

The Challenge

  • High false positive rates led to unnecessary transaction disruptions and revenue loss.
  • Merchants lacked control over fraud prevention settings, limiting risk management.
  • Existing systems struggled to adapt to evolving fraud tactics and diverse risk profiles.

Phased Implementation

Phase 1: Discovery and Problem Definition

  • Stakeholder Interviews: Engaged merchants, customer success teams, and risk experts to identify pain points.
  • Data Analysis: Found that 20% of flagged transactions were false positives, impacting merchant revenue.
  • Competitive Benchmarking: Compared ZendNow’s fraud prevention features with market leaders.

Phase 2: Solution Design and Planning

  • Designed machine learning models for real-time fraud detection.
  • Developed customizable fraud rules allowing merchants to define risk parameters.
  • Created an intuitive dashboard for monitoring flagged transactions.
  • Drafted user stories to align fraud prevention with merchant needs.

Phase 3: Development and Integration

  • Trained ML models using transaction data to detect fraud patterns.
  • Built a rule builder interface enabling merchants to adjust fraud sensitivity.
  • Integrated fraud detection into ZendNow’s payment processing pipeline.

Phase 4: Testing and Validation

  • Internal Testing: Verified system performance under high transaction volumes.
  • User Acceptance Testing (UAT): Piloted the new tools with select merchants.
  • A/B Testing: Achieved a 25% reduction in false positives and 40% improvement in fraud detection accuracy.

Phase 5: Launch and Rollout

  • Soft Launch: Released to select merchants for final validation.
  • Full Deployment: Accompanied by training materials and webinars.

Phase 6: Post-Launch Monitoring

  • Set up dashboards to track fraud detection accuracy and merchant satisfaction.
  • Gathered continuous feedback to refine fraud detection tools.
  • Implemented iterative enhancements to improve fraud prevention.

Key Results

  • 40% improvement in fraud detection accuracy.
  • 25% reduction in false positives, saving merchants millions in lost revenue.
  • 95% satisfaction score for the fraud prevention tools.
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