Adaptive Machine Learning. Advanced Analytics.

DataVisor Enterprise is a robust fraud detection solution combining best-in-class machine learning technology with powerful investigative workflows to deliver real-time fraud analytics so businesses can take action before damage occurs.

Contextual Detection

Automatically identify and categorize different types of fraud and abuse. Combine results with granular risk-based detection scores to trigger the correct action or authentication against each account.

Transparent Results

View detailed detection reasons generated by our proprietary engine to understand in full detail how each instance of fraud or abuse was committed.

Streamlined Investigation

Review detailed activity and detection history for each account or user. Discover clusters of linked accounts and make accurate bulk decisions to save time. Analyze the latest fraud trends with customized reports and dashboards.

Efficient and effective fraud strategies

Stop evolving account takeover threats

Proactive Risk Strategy

Strategy teams can stay ahead of fraud by uncovering previously undetected fraud patterns using robust exploration and analysis tools that proactively address emerging fraud patterns.
high accuracy and coverage to detect account takeover

Automated Review and Alert Management

Bulk review capability enables analysts to efficiently manage alerts and queues, and monitoring tools automatically raise alerts based on individual thresholds at the onset of fraud attacks, saving time, and owering alert volumes.
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Holistic Data Analysis

Sophisticated unsupervised machine learning algorithms identify clusters of users and accounts with shared attributes, revealing cleverly-concealed patterns and unearthing coordinated attacks.

Uncover Complex Connections Across Millions of Accounts

Profile Information

Identify sophisticated correlations through deep dives into account details provided at the point of application or registration, such as email, phone number, physical address, and biometric data.

Cross-Accounts Linkages

Investigate potential relationships between multiple accounts through advanced analysis of interactions, transactions, and access patterns, and reveal cleverly disguised commonalities that could indicate coordinated fraud.

Content and Metadata

Analyze text, images, and other types of user content—such as posts, listings, messages, comments, profile photos, call records, chat logs, and more—to vet the legitimacy of any given account.

Behaviours and Activities 

Gain actionable insights into account histories by analyzing volume, velocity, and timing signals from login events, transactions, content posts. and other relevant activity.

Digital Fingerprints

Detect fraudulent activity by aggregating digital information logged during account access sessions, including device ID, device type, version and OS, user agent, IP address, GPS location, and more.


Ready to enhance your detection with unsupervised machine learning?