Leverage the power of unsupervised machine learning to defeat fraud with speed and agility.
dCube is the complete AI-powered fraud management solution that enables the proactive defeat of emerging fraud.
Get detailed fraud signals in real time, and take proactive steps to defeat both known and unknown fraud.
This is part one of a three-part blog post series highlighting some of the key things to look for when it comes to choosing a third-party fraud prevention solution. In this post, we go over topics such as multi-layer protection, target use cases, global reach and data, etc.
Stop application and transaction fraud, account takeover, money laundering, and more.
Learn how leading financial institutions are using ML to proactively detect card application fraud.
Build and maintain trust by stopping fraud before reputational damage occurs.
Every company is different, and every attack is different. When it comes to defeating fraud, success is determined organization by organization. From mass registrations and fake listings, to ATO and spam, to promo abuse and bot attacks,…
Keep your platform safe and secure by purging spam and harmful posts.
Understand the range of modern fraud attacks to ensure complete coverage for your organization.
Discover all the ways our clients are staying ahead of fraud by embracing AI-powered solutions that enable their organizations to know the unknown.
5 stories. 5 victories against fraud. See how organizations across industries are proactively defeating attacks.
Get experts insights on how to deploy cutting-edge fraud solutions to defeat even the most sophisticated modern attacks.
Discover advanced strategies for managing the rapidly-evolving fraud attacks plaguing the modern banking sector.
Delve deep into proprietary research to ensure your organization stays ahead of malicious threats.
Customers online want convenience, ease, and access. Fortunately, your business offers it all. Unfortunately, that’s what fraudsters want too. To a cyber criminal, those features means vulnerabilities. To bring you the very latest and most…
Learn from leading experts in the fields of AI, machine learning, and fraud prevention as they provide rich insights on fraud trends and solutions.
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 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.
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…
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.