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?
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.
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.
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.
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.
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.
Explore the latest in fraud intelligence.
First in a three-part series focusing on fraud modeling. The series covers pre-modeling, modeling, and post-modeling.
An overview of how to choose between supervised and unsupervised ML.
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…