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.
Discover advanced strategies for managing the rapidly-evolving fraud attacks plaguing the modern banking sector.
Build and maintain trust by stopping fraud before reputational damage occurs.
Discover AI-powered fraud strategies for preventing financial and reputational damage in this powerful eBook.
Keep your platform safe and secure by purging spam and harmful posts.
DataVisor has partnered with Momo for more than two years to help them take on fraudulent account creations and compromised accounts.
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.
Delve deep into proprietary research to ensure your organization stays ahead of malicious threats.
Access proprietary data and research results to discover the latest attack techniques and prevention strategies.
Learn from leading experts in the fields of AI, machine learning, and fraud prevention as they provide rich insights on fraud trends and solutions.
Understand the range of modern fraud attacks to ensure complete coverage for your organization.
What is UML?
Generate comprehensive sets of features to describe user accounts without prior history.
Identify suspicious clusters of accounts that have strong similarity in the feature space.
Enhance initial detection results with digital signals for advanced feature engineering.
Output results to train supervised machine learning models to further boost detection.
At the core of the platform is DataVisor’s UML Engine that combines clustering techniques with graph analysis to discover correlated fraudulent and suspicious patterns from unlabeled data.
The DataVisor Platform, from computation to data access and storage, is built on the latest big data infrastructure stack. Apache Spark, HDFS, Hadoop, Apache HBase and Elasticsearch are all being used to support the system in different capacities.
The DataVisor Global Intelligence Network leverages deep learning technologies to provide real-time, comprehensive digital intelligence based on the industry’s widest set of digital data.
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 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.
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…