Category: Technical Posts

Fraud Modeling with Automation, Complete Control, and Domain Expertise

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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.

Defeat Fraud with a Comprehensive AI-powered Solution

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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.

SR 11-7 Compliance: 3 Core Elements for Model Validation

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

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

How DataVisor Optimizes its AWS Stack to Protect 4B+ Online Accounts from Fraud

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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.

Detecting New and Evolving Fraud Patterns in Digital Commerce

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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…

Managing Thousands of Spark Workers in the Cloud: DataVisor Presents at SAIS 2018

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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.

Fighting Fraudsters Among Billions of Users: DataVisor Presents at SAIS 2018

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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.

Why Unsupervised Machine Learning is the Best Option for Anti-Fraud

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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…

A Few Key Differences Between Supervised and Unsupervised Machine Learning

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An overview of how to choose between supervised and unsupervised ML.

In this guide, we will explain a few high level differences when it comes to choosing between supervised and unsupervised machine learning.