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Leverage DataVisor's cutting-edge approach to detect new and unknown attacks before damage is done.

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Designed for the Digital Era

The DataVisor approach combines cutting-edge AI and machine learning technologies to correlate fraudulent and suspicious patterns across billions of accounts in real time. Patented and proprietary unsupervised machine learning (UML) algorithms work without labeled input data to automatically detect new and previously unidentified fraud and abuse patterns.

What is UML?

How the Unsupervised Machine Learning Engine Works

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Dynamic Feature Extraction

Generate comprehensive sets of features to describe user accounts without prior history.

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Correlation Across User Accounts

Identify suspicious clusters of accounts that have strong similarity in the feature space.

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Advanced Feature Engineering

Enhance initial detection results with digital signals for advanced feature engineering.

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Supervised Machine Learning Augmenting

Output results to train supervised machine learning models to further boost detection.

Decisions Without Assumptions

The DataVisor Unsupervised Machine Learning Engine processes all events and account activities simultaneously to analyze patterns across hundreds of millions of accounts. This enables detection of suspicious connections between malicious accounts, even when those accounts are incubating, mimicking legitimate user activities, or changing attack techniques. This also allows the UML Engine to detect all the members of an attack ring at once, ensuring the attack is fully stopped.

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Unsupervised Machine Learning Engine

At the core of the platform is DataVisor’s UML Engine that combines clustering techniques with graph analysis to discover correlated fraudulent and suspicious patterns from unlabeled data.

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Big Data Architecture

The DataVisor Platform, from computation to data access and storage, is built on the latest big data infrastructure stack. Apache Spark, HDFS, Hadoop, Apache HBase and Elasticsearch are all being used to support the system in different capacities.

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Global Intelligence Network

The DataVisor Global Intelligence Network leverages deep learning technologies to provide real-time, comprehensive digital intelligence based on the industry’s widest set of digital data.

Architecture For Real Time Detection

The DataVisor UML Engine is designed to operate in batched and real-time modes. While they are built using the same algorithms, they are designed for different optimization goals. The batch system targets maximal coverage and accuracy, while the real-time system also addresses latency and throughput requirements.

Leading Insights

Explore the latest in fraud intelligence.

Defeat Fraud with a Comprehensive AI-powered Solution

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First in a three-part series focusing on fraud modeling. The series covers pre-modeling, modeling, and post-modeling.

DCube’s powerful array of capabilities combine to put real power in the hands of users, enabling teams to accelerate the pre-modeling process. Data scientists are able to focus on what really matters—building high-performance models—instead of cleaning up poor quality data.

A Few Key Differences Between Supervised and Unsupervised Machine Learning

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An overview of how to choose between supervised and unsupervised ML.

In this guide, we will explain a few high level differences when it comes to choosing between supervised and unsupervised machine learning.

Detecting New and Evolving Fraud Patterns in Digital Commerce

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As attacks grow in scale and velocity, businesses are forced to evolve their fraud detection methods from manual…

As attacks grow in scale and velocity, businesses are forced to evolve their fraud detection methods from manual detection involving blacklists and rule engines to machine learning algorithms that can detect known and emerging types of fraud. This article highlights why existing fraud detection…

Enhance detection with unsupervised machine learning.

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