Intelligence center Case Studies

4 Case Studies of Unsupervised Machine Learning (UML) for Real-time Fraud Prevention

Fraud Tactics Are Evolving—Is Your Defense Ready?

Explore how DataVisor’s Unsupervised Machine Learning (UML) solution transforms fraud detection with real-time insights into emerging threats and coordinated fraud attacks, and protect businesses across industries.

What’s inside these case studies?

  • Overview of UML Technology: Learn how DataVisor’s real-time approach combats even the most sophisticated fraud threats.
  • 4 Detailed Case Studies: Discover how businesses in payments, lending, travel, and airlines are using UML to address transaction fraud, application fraud, promotion abuse, policy abuse
  • Key Takeaways and Actionable Insights: Understand the impact of UML and how it can elevate your fraud prevention strategy.
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4 UML Case Studies

Case Study Highlights

  • Payments Platform
    Prevented $700,000 in fraudulent losses during a BIN attack.

  • BNPL Provider
    Detected $3M in fraudulent loans monthly, shutting down 800+ fraud rings.

  • Online Travel Agency
    Protected €22M in promotional abuse losses during holiday campaigns.

  • Airline Reservations: Blocked 2M+ fraudulent ticket reservations with 98.6% precision.

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About Datavisor

DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

DataVisor is the world’s leading fraud and risk management platform that enables organizations to respond to fast-evolving fraud attacks and mitigate risks as they happen in real time. Its comprehensive solution suite combines patented machine learning technology with native device intelligence and a powerful decision engine to provide protection for the entire customer lifecycle across industries and use cases.