Category: Technical Posts

Fraud model building must be rapid enough to respond to fraud threats and abuse in real time. DCube facilitates collaboration between fraud and data science teams to build models, review detection results, compare models, improve performance, and deploy in production for enhanced efficiency.

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.

An SR 11-7 compliant validation framework includes 3 core elements: An evaluation of conceptual soundness, ongoing monitoring, and outcomes analysis.

Learn about the latest trends and insights in the world of online fraud from our analysis of over 4 billion accounts from some of the largest internet properties and financial services in the world.

DataVisor's VP of Engineering David Ting discusses how DataVisor optimizes its AWS stack with spot fleets, dynamic instance launches, & RT cost tracking, all while protecting over 4B users from fraud at the AWS Summit Anaheim 2018.

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…

DataVisor's Yuhao Zheng and Boduo Li share advanced techniques for managing thousands of spark workers to analyze billions of events at a time, including clustering workers and automated, optimized management of DataVisor's spark infrastructure.

DataVisor's Ting-Fang Yen and Arthur Meng present a novel deep learning technique for scalable online fraud detection among billions of users.

Fraud patterns are constantly changing, and new methods of fraud are being introduced. Traditional anti-fraud methods cannot be upgraded in real time. There will be a time-consuming process of tag accumulation, calculation, analysis, and testing. This process often takes half a month and sometimes…