Advanced Analysis and Prevention
For an anti-fraud system to function effectively, it must thoroughly analyze incoming traffic. This involves several methods, ranging from basic checks (like IP address verification) to more advanced techniques that examine device parameters, big data, and machine learning to detect suspicious activities.
UCLIQ’s anti-fraud system is fully integrated, providing real-time analysis of incoming traffic. This system compares current traffic patterns against historical data and assigns risk levels based on a variety of factors. Unlike external plugins, UCLIQ’s integrated system has access to a vast database of historical traffic data, making it more effective at detecting fraud.
Common Fraud Detection Techniques
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VPN and Bot Detection: Identifying traffic coming from corporate networks, VPNs, or automated bots.
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Mismatch Detection: Spotting mismatches in connection type, device, language, time zone, or operating system (e.g., emulators).
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Repeated Use of IPs: Flagging repeated attempts from the same IP address.
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Motivated Traffic Sources: Identifying traffic generated from social networks, instant messengers, or other suspicious sources.
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Touch Support Discrepancies: Detecting discrepancies between the stated machine parameters and actual touch support, typically seen in emulators.
