Synthetic identity theft - when a fraudster creates a composite identity from a mix of real and fake information and applies for loans with the identity - is a growing problem for financial institutions.
The fraud landscape within the mobile user acquisition space is very complex with many sophisticated attack techniques involved. In this blog post, we will cover the tools and techniques used by fraudsters and why it's difficult to detect them.
Evolving money laundering patterns are leading to huge fines and mounting pressure on FIs to become more vigilant. Learn how unsupervised machine learning and its inherent merits can help FIs to uncover hidden money laundering patterns and improve AML detection.
This blog post is part one of a two-part series that details the UA fraud problems in the mobile app industry. The series highlights the impact of the fraud problem, the tools and techniques fraudsters use and why UA fraud is getting harder to detect.
Today's AML & Compliance leaders face dual challenges of increasingly sophisticated digital financial crimes and the threat of growing fines from regulators. Learn how AI and Machine Learning can help FIs detect more crime and better triage alerts.
This is the final part 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 adaptability, scalability, ease of integration, and flexibility in deployment options.
This is part two 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 explainability, engaging visualizations, data privacy processes, and etc.
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.
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.
Preventing fraud and protecting users is integral to IT strategy and core to maintaining a company’s competitive advantage. Learn how IT teams can be proactive on the lookout for fraud and able to react to it at the speed it happens.
Online digital lenders have proliferated in the last few years, and traditional lenders have also rebalanced their focus and have increased their digital efforts across all products trying to catch up with their nimbler rivals. As a result, the potential target for fraudsters to attack has become significantly larger and more lucrative and they haven’t held back their efforts to inflict maximum financial damage.
Fraudsters are constantly coming up with new and innovative ways to commit fraud. Today we are taking a look at product listing fraud, a relatively new type of fraud that is a rapidly growing problem for online marketplaces.
A successful spam campaign is one that obtains maximum return-on-investment (ROI) to the spammer. This means that a spam campaign must reach as many end users as possible, must be robust in the face of blacklisting efforts, and must be scalable. This blog post describes some of the recent techniques employed by spammers to distribute malicious URLs on social media platforms as observed by DataVisor.
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 methods have limitations and more importantly a few reasons why unsupervised machine learning is gaining traction.
In recent years, many mobile applications, including mobile games, have expanded to the global market. However, as they expand, fake traffic is becoming a growing problem that has long plagued many game developers. It directly results in the waste of marketing resources, making it impossible for developers to have real control over the ROIs of acquiring new users.
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.
Does the fact that UML doesn’t require labels mean that there is no benefit at all to labels? If label data exists already, how can it be used to improve UML detection results? In this article we discuss how labels can be effectively used in UML detection, even if they are not required.
Unsupervised Machine Learning (UML) is a topic that we get a lot of questions about here at DataVisor, because UML is at the core of our detection platform. In this 5-minute primer on UML, we start by defining the overarching field of Artificial Intelligence, then we drill down to the sub-field of Machine Learning, and lastly we discuss the various machine learning techniques, including UML, and when each ML technique is most effective.
The DataVisor Online Fraud Report took a look at our base of more than one billion users across 172+ countries in the world. Using this massive amount of data, we were able to identify some of the favorite tools and attack techniques that online criminals from around the globe favor when doing their dirty work.