The holiday season provides fraudsters a perfect ground to attack. DataVisor’s Unsupervised Machine Learning can help buyers & sellers in marketplace.
This post provides a few details about synthetic identity fraud and S.B. 2155, including what businesses should expect when it comes to the rollout of the law.
Application fraud is sophisticated and legacy systems are unable to combat it. DataVisor’s Unsupervised Machine Learning identifies hidden attack patterns.
With Supervised ML becoming increasingly commoditized, businesses are often left with various components rather than a solution that provides real value.
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.
Last week, DataVisor attended Money20/20 Las Vegas, the latest financial and payment technology conference of the year. Here are the top three takeaways from the event for fraud and risk teams.
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.
Are your fraud analytics tools ready for unknown fraud detection? Here are the top five considerations and insights needed for a dashboard to provide actionable insights for unknown fraud.
An SR 11-7 compliant validation framework includes 3 core elements: An evaluation of conceptual soundness, ongoing monitoring, and outcomes analysis.