Introduction There are many technical articles that describe supervised and unsupervised machine learning methods. In this guide, we will explain a few high level differences when it comes to choosing between the two. Comparison 1: [...]
The DataVisor Online Fraud Report took a look at our base of more than one billion users across 172+ countries in the world. Using this massive amount of data, we were able to identify some of the favorite tools and attack techniques that online criminals from around the globe favor when doing their dirty work.
As mentioned in my previous articles, traditional rule-based transaction monitoring systems (TMS) have architectural limitations which make them prone to false positives and false negatives: Naive rules create a plague of false positives that are [...]
False positives have a terrible reputation among anti-money laundering (AML) circles. As mentioned in my previous article on ending the false positive alerts plague, approximately 90-95 percent of alerts generated by Transaction Monitoring Systems (TMS) [...]
Keith Furst is the Founder of Data Derivatives, and has years of experience within a variety of financial institutions including Tier One wholesale banks, investment banks, foreign bank branches, commercial banks, retail banks, broker-dealers, prepaid [...]
Wondering if your company has any crime rings sleeping among your users? Most will acknowledge that there are likely some accounts lurking here or there, but may not realize that it’s a big problem.
Rules engines, machine learning models, ID verification, or reputation lookups (e.g. email, IP blacklists and whitelists) and unsupervised analytics? I've often been asked which one to use and should you only go with one over [...]