As a complement to our online identity verification tools, IDMatch+Predict steps up your fraud prevention efforts with a solution that leverages machine learning technology and special data elements from online transactions.
IDMatch+Predict goes beyond the standard data verification by comparing your customers’ online data with special behavioral data points during an online transaction to calculate risk.
IDMatch+Predict creates intelligent friction so you can earn your customers’ trust and reduce transaction abandonment. We use our databases to create a trusting relationship between you and your customers that also ads barriers to a fraudster’s transactions.
Our fraud fighting databases consist of more than just names, addresses, and IP addresses. We compare your customers’ profiles against our databases to predict and prevent fraud for both customer onboarding as well as for account takeover.
Some examples of our enhanced data points used to calculate a risk include:
Email Address On Social Networks
IP Location and User Address Comparison
Site Navigation Shows Patterns of Fraud
Device Associated with the Identity
Similar identity seen >2x in <120 seconds
Shipping Methods and Address Formats
Site Navigation Patterns
IDMatch Features & Benefits
Reduces Risk of Fraud and Chargebacks
Our fraud prevention solution adds minimal friction to your existing onboarding or checkout process. In addition, this added layer of security will help your organization reduce chargebacks, detect fraudulent activity and accept more profitable business online.
ID Verification Designed for End Users
Online identity verification and fraud detection should be difficult for fraudsters and easy for customers. That’s why we design all of our solutions with your customers in mind. Overall, we created a seamless user experience with intelligent friction that helps foster trust between you and your customers.
Create Your Own Approval Rules
You can create your own rule sets specific to your company’s needs for fraud prevention. Custom variables, rules, models and policies to analyze dynamic behavioral history to accurately detect complex fraud patterns with minimal false positives.