Let real-time data enhance your risk analysis

Our comprehensive data analysis and user-scoring algorithm provides you actionable information about the type, quality, and trustworthiness of your site's traffic so you can make smarter decisions while assessing risk.

Get in touch
Realtime Scoring Hero

Dozens of attributes, one actionable score

Our unique scoring algorithm tracks and analyzes over 170 user attributes and combines them into a single, actionable score, which allows you to easily assess fraud risk at a glance and automate your review process.

Order Scoring

Risk assessment from start to finish

Every user interaction is analyzed, beginning with the point of entry and continuing until exit. As users navigate your site, their scores are continuously updated to ensure that the number you see is as accurate as possible.

Payment Gateway EQ8 Scores

Integrated order processing

Every order is assigned its own score so you can assess fraud risk at a glance. Scoring allows for faster and more informed manual reviews, and can also be used with automated order processing based on your own custom rules and risk thresholds.

Suspicious Orders scoring

How scoring works

Fast, Front-end Scoring

Our algorithm is built to score quickly, starting as soon as a user session begins and continuously updating until the session ends.

Behavioral Analytics

Payment data and known attributes are combined with behavioral analytics to provide a more holistic view of each user.

Pre-session Data

Pre-session data helps to catch fraud earlier by identifying the patterns of each user based on the actions that lead them to your site.

Active Geo-Location

By measuring response times and comparing them to specific locations around the world, we verify whether a customer is where they claim to be.

Device Fingerprinting

This technique identifies devices and recognizes fraudulent activity when certain characteristics do not match up.

Mobile device motion detection

Humans move slightly when holding mobile devices, and this movement can be detected by the devices’ sensors. Bots are unable to mimic these movements when attempting to pass themselves off as real users.