Applying Cross-Channel Data to Improve Fraud Detection

In light of industry initiatives and global regulations – including PSD2, 3D Secure 2.0 and Faster Payments – a single question remains open:  Does risk-based authentication really work?  This is usually followed by inquiries on fraud detection rates, false positives and the impact on customers.  The benefits of an approach based on machine learning has proven to be extremely effective by consistently demonstrating fraud detection rates up to 97%. 

Despite these results, is it possible to predict fraud even more accurately and minimize the number of good customers who are challenged?

The Ecosystem Approach —Unique and Powerful
Fraud risk management has become a burden in recent years, and not just because the attackers have gotten better at their game. The tools and technologies used to detect and mitigate fraud events are better, but they are also plentiful. A survey commissioned by RSA found that 57% of organizations use between 4 – 10 different tools within their anti-fraud operations.

In addition, the face of digital banking and commerce is changing rapidly.  There are new channels, new interactions and new types of payment methods that customers are using to engage with organizations which only adds to the complexity.

Leveraging machine learning, organizations are able to use an ecosystem of data including third-party risk information from other fraud prevention tools as well as their own business intelligence to enhance risk scoring models and improve fraud detection.

A Case Study

A case study with a major U.S. (Read more...)

*** This is a Security Bloggers Network syndicated blog from RSA Blog authored by Heidi Bleau. Read the original post at: