Why Ravelin
Built different by design
Ravelin was built from the ground up for the complexity of modern commerce fraud. Here is what sets us apart.
Custom Machine Learning
One-size-fits-all models miss the fraud that matters to you.
Every Ravelin model is 100% optimized for each client's specific fraud challenges. Instead of applying a generic model across all merchants, we train on your transaction data, your chargeback patterns, and your customer base. The result is higher detection rates, fewer false positives, and a system that adapts as fraud patterns evolve.
Consortium Data Network
9 billion identity signals shared across a trusted network.
Ravelin's consortium database aggregates 9 billion anonymized, GDPR-compliant identity elements across the merchants in our network. This shared intelligence lets you spot fraudsters who target multiple merchants, identify synthetic identities, and enrich your decisions with signals no single merchant could see alone.
Graph Network Analysis
See the hidden connections that rules engines miss.
Our graph network maps relationships between identities, devices, payments, and behaviors in real time. Visual link analysis reveals hidden connections - the shared phone number, the linked IP range, the coordinated account creation - that signal organized fraud rings and multi-accounting operations.
Agentic AI Innovation
Built for the next wave of commerce - AI shopping agents.
As agentic commerce grows, fraud patterns shift. Ravelin is purpose-built to protect agentic transactions: AI-driven shopping bots, autonomous checkouts, and API-originated orders. Our models distinguish legitimate automated traffic from abuse, keeping your business ready for the AI-native future of e-commerce.
Proactive partnership, not just software
Every Ravelin customer is paired with a dedicated data scientist and an account manager who know your business. We help you tune models, investigate emerging fraud patterns, and adapt your strategy as your business grows. It is support that goes beyond a ticket queue.
Talk to our team