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.
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.
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.
Process structured and unstructured data with feature engineering to identify common fraud patterns and correlations.
Deploy machine learning models to directly operate on production data. Detect new fraud attacks with no need for labels or historic data.
Benefit from consistent model performance over time, and bypass the need for extensive training and frequent retuning.
Prevent known and unknown fraud with the comprehensive fraud management platform that combines advanced unsupervised machine learning algorithms with an enterprise workflow to give full control to your fraud and data science teams. Build and deploy high-performance models to proactively defeat known and unknown fraud with speed and agility.
Protect your organization with a robust fraud detection solution that combines adaptive machine learning technology and powerful investigative workflows to deliver real-time fraud signals. Rely with confidence on holistic data analysis and accurate risk scores to proactively detect and defuse emerging fraud attacks.
DataVisor partnered with a top U.S. credit card issuer to deliver a scalable solution to fight application fraud.
Learn how financial institutions are successfully saving millions in the fight against fraud.
Innovation is a hallmark of modern finance, and as banking expands online, organizations face intense pressure to defend against massive and rapidly-evolving attacks, while simultaneously preserving frictionless customers experiences, and meeting regulatory requirements. Discover the AI-powered…
Access proprietary data and research results to discover the latest attack techniques and prevention strategies.
Download the Q1 2019 Fraud Index Report from DataVisor to receive unparalleled data-driven insights into the latest attack trends, and the most effective prevention strategies, based on analysis of over 44 billion events, 800 million users, 396 million IP addresses, and more.
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.
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