DataVisor's Yinglian Xie discusses the three things every company needs to fight fraud with AI as a featured speaker in the "Security with Blockchain and AI" Panel at RISE 2018.
Yinglian was invited to speak about how DataVisor stops Fraud and Abuse with Unsupervised Machine Learning at the 2018 Microsoft Startup Showcase.
DataVisor's Director of Strategy Igor Bidny discusses cutting edge trends and techniques for applying machine learning to fraud investigation. With the rise of coordinated, sophisticated attacks, taking advantage of the latest big data and machine learning technology is ever more important.
The DataVisor report provides the latest insights into trends in global fraud activity and attack techniques. Last quarter, we uncovered a total of 900 million malicious activities and transactions. The report reveals dramatic growth in fraud infrastructure, with fraudulent accounts growing by 50% from Q4 2017 to Q1 2018.
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.
Does the fact that UML doesn’t require labels mean that there is no benefit at all to labels? If label data exists already, how can it be used to improve UML detection results? In this article we discuss how labels can be effectively used in UML detection, even if they are not required.
Unsupervised Machine Learning (UML) is a topic that we get a lot of questions about here at DataVisor, because UML is at the core of our detection platform. In this 5-minute primer on UML, we start by defining the overarching field of Artificial Intelligence, then we drill down to the sub-field of Machine Learning, and lastly we discuss the various machine learning techniques, including UML, and when each ML technique is most effective.
Introduction There are many technical articles that describe supervised and unsupervised machine learning methods. In this guide, we will explain a few high level differences when it comes to choosing between the two. Comparison 1: [...]