Global Intelligence Network
Leverage comprehensive fraud signals across the widest set of digital data
Feature Extraction Requires Deep Domain Expertise
Effectively leveraging digital fingerprint data (such as IP addresses, email addresses, OS versions, phone prefixes, or location) requires deep domain experience. Rules engines and machine learning models can not use these data types directly until they have been properly converted into features.
Ineffective Reputation-Based Systems
Existing solutions often provide coarse-grained reputation signals based on the history of a digital fingerprint (e.g., a device ID). These history-based reputations are often unreliable (e.g., due their dynamic natures such as dynamic IPs) or of limited coverage (e.g., unobserved before or new versions).
Unable to Analyze Patterns Across Multiple Data Sources
Existing solutions generally compute signals from each data type independently, ignoring the rich intelligence from the combinations of multiple digital fingerprints. For example, a user whose location is in the US but whose device is a phone that is sold only in China would not be marked suspicious by a digital fingerprint solution that looks at device type or locations only.
Extensive Domain Knowledge
Uncover Patterns Across Multiple Features
Analyze Each Feature in Detail
DataVisor’s Global Intelligence Network goes beyond history-based reputation scores, and outputs enriched signals such as population size, frequency, diurnal patterns, the usage among specific types of customers, and more.
Real-Time Updates from DataVisor’s Network of 2B Users
The DataVisor Global Intelligence Network is one component of the DataVisor Detection Solution and works in concert with the Unsupervised Machine Learning Engine, the Supervised Machine Learning Module, and the Automated Rules Engine.