Description
Proprietary stack of authentication technologies allows building stable in time device fingerprint.
Identification of various devices belonging to the same virtual user without using personal or sensitive data.
Detecting signs of randomization and other anomalies on the device, scrutinizing of deviation on the virtual user’ side behavior and Internet connection.
Large number of partners allows monitoring applications for financial products applied on web resources and unveiling signs of social fraud risks.
Deep analysis of the device and Internet connection allows segmentation of the requests flow by disposable income level and leads to credit risks reduction.
A broad vector of data regarding device, user and his behavior, network place, etc. allows enriching the assessment of applicants who are hard to evaluate through conventional data sources, sufficiently enhancing resolution of the decision-making system, identifying low-risk groups for comprehensive product offer and increasing the approvals rate in general.
