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.
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.
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.