通过自动化 AI 检测系统清除垃圾邮件和有害内容，确保平台安全，优化用户体验。
了解 Knowledge Graph 智能知识图谱产品的功能模块、技术原理、产品亮点、与其他图谱类工具的对比等，请下载智能知识图谱产品白皮书。…
Discover all the ways our customers are staying ahead of fraud by embracing AI-powered solutions that enable their organizations to know the unknown.
Delve deep into proprietary research to ensure your organization stays ahead of malicious threats.
Get experts insights on how to deploy cutting-edge fraud solutions to defeat even the most sophisticated modern attacks.
A one-time use of labels with unsupervised machine learning can expedite cluster assessments and reduce false positives.
With unsupervised machine learning (UML), we can expedite cluster assessment with a one-time use of labels. After that, the model will remain robust for years.
Discover how to build, test and deploy high-performance fraud models in a matter of minutes, instead of days.
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.
First in a three-part series focusing on fraud modeling. The series covers pre-modeling, modeling, and post-modeling.
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…
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…
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…
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…
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…
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…
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…