C-Score uses a comprehensive approach to user analysis, combining multiple data sources to accurately determine the risk level. The system collects digital footprints, identifies anomalies, and forms a trust rating - even before traditional verification.
Data Collection
Key stages of the system’s operation
The system collects the user’s digital footprints
Email Analysis
Temporary email signs, suspicious domains, data breaches
Phone Verification
Identification of temporary numbers, high-risk operators
IP Determination
Geolocation detection, VPN/proxy usage, and other address spoofing signs
Device Digital Fingerprinting
Language settings, IP time, screen resolution, installed plugins, hardware characteristics
Flexible Customization for Your Business
The benefits that make your business safer
92%
Accuracy of algorithms
90%
Accuracy in identifying users who will never be profitable
8 out of 10
Cases of detecting traffic source spoofing
Data analysis and anomaly detection
Using machine learning, the system compares the user with the accumulated data on fraudulent schemes and the typical behavior of real customers
Risk assessment based on registration history and activity
Comparison with databases of known fraudsters
Analysis of the speed and sequence of actions
Dynamic Data Updates
With each new user, the system becomes even more accurate
Risk Rating Formation
Based on the collected data, the system assigns a risk rating to the user
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