Capture new attacks fast. Detect incubating fraud early.
Proactively detect unknown and emerging threats with the power of proprietary unsupervised machine learning (UML). There’s no need for labeled data – DataVisor’s UML engine uses advanced clustering and graph analysis techniques to identify correlated groups of fraudulent activities and bot attacks in real time. It provides early detection by capturing incubation accounts before any damage occurs. By identifying fraudulent clusters – not just anomalies or outliers – DataVisor Enterprise ML reduces false positives and delivers extremely accurate results.
Leverage unstructured data. Discover insightful patterns.
Integrate heterogeneous data from various channels and sources across the organization in real time, supporting SQL database, Amazon S3 and any local files. DataVisor Enterprise ML dynamically derives hundreds of enriched features from unstructured and structured data, including IP addresses, emails, user names, timestamps, device information, transaction, user events and more. Using the power of digital data and enriched features, DataVisor Enterprise ML uncovers hard-to-surface patterns and increases detection performance.
Make confident decisions. Boost review efficiency.
Analyze fraud techniques and monitor fraud trends over time, and gain valuable insights with detailed reason codes. DataVisor Enterprise ML boosts operational efficiency by enabling you to take automatic actions with accurate results and make bulk decisions on hundreds of correlated cases. Investigate complex cases and uncover sophisticated patterns efficiently using Knowledge Graph to visualize multidimensional connections among entities, groups and money flow.
Get insights across the world. Enhance detection for business.
Improve fraud detection by leveraging actionable insights from DataVisor’s Global Intelligence Network (GIN). The GIN is powered by more than 4.2B protected accounts across various industries, regions and use cases. It contains rich information on digital data such as IP address subnets, prefixes, proxies and data centers, user agent strings, device types and OS, email address domains and more. Information from the GIN feeds into machine learning algorithms to optimize overall detection.
Capture significantly more fraud to increase security and power growth.
Achieve high accuracy and low false positives for positive customer experiences.
Achieve high accuracy and low false positives to promote positive customer experiences.