The first step for fraudsters to commit an intended fraud on a given platform begins with fake account creation. Learn how AI and Machine Learning can help reduce fake account creation.
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.
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.