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.
The 16th ChinaJoy kicked off in Shanghai, China on August 3, 2018. The Global Game Industry Summit was held concurrently. David Ting, Vice President of Engineering from DataVisor, gave a speech titled “How AI Technology Can Improve the ROIs of Acquiring New Users” where he discussed the fake app install problems and introduced DataVisor’s fraud detection technology for the mobile gaming industry.
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.