December 14, 2017
December 13, 2017
December 11, 2017
Digital transactions continue to proliferate apace, whether customers are buying, selling or accessing services and content online Knowledge sharing across industries and channels could streamline the user experience as well as reduce fraud and operational cost.
As more and more consumers buy goods and access services and content through digital channels, gaining insight across different industries and channels can accelerate and personalize user experience and reduce fraud and operational cost. ThreatMetrix’s Digital Identity Network, built on the shared intelligence from over a billion transactions per month, can help correlate seemingly disconnected events and security incidents in real time. This provides organizations with anonymized intelligence across financial services, ecommerce, payments, social media, insurance and other diverse industries.
ThreatMetrix’s products allow organizations to differentiate between trusted users and potential threats by analyzing the relationship between devices, digital personas and contextual behavior over time to establish a true digital identity that is continuously evaluated in the context of every interaction.
ThreatMetrix believes not just in the power of big data, but more specifically in the power of big networks. Billions of data points and interactions from tens of thousands of websites combine to build an accurate view of an individual or device prior to the transaction of interest. Organizations can then link identities across devices and channels based on global patterns. ThreatMetrix conducts link analysis across devices, identities and locations in real-time, enabling organizations to link identities across devices and channels based on internal and external (global) linkages and patterns of behavior.
The challenge of tracking fraudulent behavior however, is that the legitimate user and the fraudster may be transacting at the same time. How can an organization protect a trusted user whilst preventing fraud? ThreatMetrix enables the creation of user-specific profiling, identifying anomalies between current and historical behavior to help differentiate between trusted and fraudulent users.
Behavior Analytics tools provide an extremely powerful way of detecting and analyzing changes in user behavior. ThreatMetrix’s behavioral model is based on the user’s history derived from the Digital Identity Network and uses behavior and metadata for automated detection of user behavior patterns and anomalies. ThreatMetrix’s Behavior Analytics tool-set allows organizations to create custom variables, rules, models and policies to analyze patterns specific to their organization as well as their users. Rather than a static and linear behavior model, the ThreatMetrix policy engine can determine whether an event falls within a “good user” or “bad user” pattern, whilst still allowing for changes in trusted user behavior (for example if a trusted user legitimately increases the number of devices used). This helps organizations more accurately differentiate between true fraud and legitimate behavior change, reducing the step-up frequency without increasing overall risk.
ThreatMetrix uses behavior, age and location variables to examine the historical data related to a given transaction to run a deep behavioral assessment:
ThreatMetrix believes not just in the power of big data, but more specifically in the power of big networks.
In addition, ThreatMetrix’s Expression Variable allows attributes and variables that are available in a particular transaction to be combined into a mathematical expression. The main goal of this tool set is to allow risk analysts to create polices that help prevent fraud and reduce customer friction by analyzing behavior at a granular level. For example, the ability to analyze a payment transaction by looking at the average daily amount for a consumer for a specific period of time will deliver additional insight by combining pre-existing variables using mathematical operators. With Behavior Analytics, customers have the ability to architect complex decisioning polices to address their specific business needs with minimum friction to end-users.
Utilizing sophisticated analytics, a customer can tune the system to produce strong anomaly detection rules that only fire when behavior is truly out of character:
While other behavioral models can suffer from false positives, through leveraging global shared intelligence and deep behavior analytics ThreatMetrix is in a unique position to provide a behavioral model with reduced false positives.