Putting advanced analytics, robust rule management and optimization, and efficient case management to work to defeat known and unknown fraud.
Discover how to build, test and deploy high-performance fraud models in a matter of minutes, instead of days.
A three-part series focusing on fraud modeling: pre-modeling, modeling, and post-modeling.
An SR 11-7 compliant validation framework includes 3 core elements: An evaluation of conceptual soundness, ongoing monitoring, and outcomes analysis.
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 methods have limitations and more importantly a few reasons why unsupervised machine learning is gaining traction.
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.