Big Data Analytics: The Future Of Fraud Prevention
DataVisor’s next-generation unsupervised approach combined with the scalability of our Big Data architecture is transforming the way security will be done in the future. Gone are the days when multiple point solutions are needed in combination to tackle the increasing fraud challenge on your platform. Big data parallel processing enables not only unprecedented scalability but also allows for the use of more advanced algorithms to process billions of user actions all together. Empowered by DataVisor’s technology, you can catch new, changing attacks automatically and before any damage is conducted.
At the heart of DataVisor’s technology is the patent-pending DataVisor User Analytics Engine. Utilizing a proprietary unsupervised detection algorithm operating within a Spark “in-memory” Big Data platform, DataVisor can analyze billions of events per hour and automatically discover unknown malicious campaigns early, without using labels or training data.
Unsupervised detection is uniquely effective since it does not rely on labels or training data to detect threats. But how does it work without any pre-knowledge of what the attack techniques look like?
An easy way to understand how unsupervised detection works is to think of a Pointillist-style painting. When viewing any individual dot within the painting up close, all of these dots appear to be indistinguishable from one another. However, if you step back and take a panoramic view of the entire painting, patterns begin to emerge.
DataVisor’s unique unsupervised detection works in much the same way. By taking a global view of all users within an online service, our unsupervised detection algorithm is able to find clusters of bad actors acting in a correlated fashion.
Adaptive Threat Prevention
A big challenge for any fraud solution is to detect different categories of attacks, including new unknown types of attacks, even when user activity data are incomplete or have a limited number of fields.
DataVisor’s analytics engine is able to process broad categories of user actions from unstructured data (such as account opening, login, transactions, add friend, comment, reviews or ratings) to derive a complete view of aggregated user activity patterns. The ability to analyze a variety of different types of events across hundreds of millions of users makes our analytics engine robust to missing data. It has the following unique properties:
Protects your entire service by analyzing ALL events beyond just transactional data
Detects all categories of attacks using one security solution
Supports unstructured data
Adjusts to sparse (sampled) or missing data fields
Identifies fraud without requiring historical logs
Want to be a part of the exciting journey we are undertaking to transform how cybersecurity is done? Request a trial today.