Additional information for Simility Fraud Detection
Key features of Simility Fraud Detection
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- Device ID recognizes fraudsters' identities
- Machine learning continuously refines detection
- Create, edit, and test custom manual rules
- Feed unstructured data to a Simility fraud model
Analyze fraudulent user behavior with Simility's existing models, based on experience in a number of verticals. Allow analysts to customize detection mechanisms with their own hypotheses of how to catch more fraud.
Simility's custom built machine learning improves fraud detection. Every decision made will be automatically fed back into the machine learning algorithms to catch fraud that's near impossible to see with a human eye.
Automate analysts' workflow. Simility's graphical interfaces allow analysts to see the pattern in any fraudulent behavior. A manual rule builder allows analysts to easily edit their own rules, so they can automate their own work and plug their intuitions into the machine's fraud model.