The Challenge
StyleHub, a rapidly growing fashion e-commerce platform, was losing significant revenue to sophisticated fraud attacks targeting high-value fashion items. Their basic fraud prevention tools were unable to keep pace with evolving attack patterns, and legitimate customers were being declined at unacceptable rates.
Our Solution
ThroPay deployed an advanced AI-powered fraud prevention system:
- Machine Learning Models: Custom-trained models analyzing 200+ risk factors in real-time
- Behavioral Analytics: Device fingerprinting, typing patterns, and navigation behavior analysis
- Network Analysis: IP geolocation, VPN detection, and proxy identification
- Transaction Scoring: Dynamic risk scoring adapting to transaction context and customer history
- Automated Rules Engine: Self-learning rules that adapt to new fraud patterns
- Trust & Safety Framework: Customer verification without friction for low-risk transactions
The Results
The fraud prevention system delivered remarkable results:
- Reduced fraud losses by 85% while maintaining approval rates
- Increased conversion rates by 150% through reduced false declines
- Achieved 99.8% fraud detection accuracy with <0.1% false positive rate
- Protected $12M+ in revenue during peak holiday season
- Reduced manual review workload by 90%
- Improved customer satisfaction scores by 40%
- Expanded to new markets with confidence in fraud protection
