Analyzes How Hashed Identity Elements Are Being Used in Digital Interactions
Our 身份网络 uses data aggregated from more than 200 million monthly anonymized, real-world queries to predict fraudulent vs. legitimate interactions by analyzing patterns of how identity elements are being used online.
Produces machine learning predictions based on how identity elements are used in interactions.
Aggregated Transaction-level Intelligence
Provides insight into cross-border and cross-industry fraud patterns outside of your own data.
Dynamic Decision Making
Learns with new transactions to provide answers in real time for determining fraud potential.
Unique Data Attributes Built to Improve Your Risk Models
Network Score provides insight into activity patterns we observe in our 身份网络. Based on which identity elements are used in a digital interaction, the Network Score will assess the riskiness with a prediction between 0-1.
网络风险 panel is designed to surface the top 12 signals that indicate positive or negative user activity. Signals reveal patterns that help fraud teams identify which customers are good vs. bad.
IP Risk flag assesses the risk of a given IP address in real-time using metadata about the IP address as well as Network data that surfaces how the IP address in question has previously been used.
2,000+ Companies Worldwide Trust The Ekata 身份网络
Knowing what your fraudsters are doing is just as important as knowing who they are. With an evolving landscape of fraud types and tactics, understanding how your fraudsters act will better help you prevent them from committing fraud.
See how the Ekata 身份网络 can improve your risk model. Contact us today.