FraudNet
Fraud Detection Using Machine Learning vs. Rules-Based Systems
Summary
A European Buy Now, Pay Later provider transformed its fraud detection and risk scoring by integrating machine learning with traditional rules-based systems, resulting in significant reductions in fra...
Value Drivers
Challenges
- Rising account takeovers and compliance risks
- Lack of real-time visibility into operations
- Difficulty in scaling current infrastructure
- High operational costs due to legacy systems
3 more challenges available
Solutions
- Integration of machine learning models with existing rules-based fraud detection systems
- Deployed real-time monitoring and analytics platform
- Migrated to cloud-based scalable infrastructure
2 more solutions available
Outcomes
- Account takeovers dropped by 90%
- Achieved 24/7 real-time visibility and monitoring
- Successfully scaled operations to support 3x growth
- Decreased operational costs by 40% annually
- Improved data accuracy and accessibility by 85%
- Automated 70% of manual processes
- Enhanced decision-making with unified analytics
6 more outcomes available
Products
Software
Fraud.net Fraud Detection Platform
A customizable fraud detection solution combining machine learning models with rules-based frameworks for dynamic risk scoring and real-time fraud prevention.
Target Organization
European Buy Now, Pay Later provider
Description: A financial technology company offering buy now, pay later services to consumers and merchants in Europe.
Industry:Fintech, Payments