As we wrap up the year 2020, we thought it’d be the perfect opportunity to look back on some of the top blog posts we’ve shared throughout the year (in no particular order). From how to leverage graph analytics in money laundering and fraud investigations to our latest releases, breaking coverage on the FinCEN files […]
Researchers found that losses due to fraud cost organizations worldwide more than 80% of the UK’s entire GDP(1) (up to USD 5.127 trillion!). In times of the COVID pandemic, 93% of anti-fraud professionals anticipated an increase in fraud over the next year, and 51% predicted the increase will be significant(2). Fraudsters continue to exploit new technology […]
It’s estimated that between $800 billion to $2 trillion is laundered annually. Neutralizing money laundering networks is critical to fighting back against drug trafficking, human exploitation, corruption, and more. Traditional Anti-Money Laundering (AML) technologies can’t keep up with the scale and flexibility required to unmask criminal networks. This article will present the benefits of graph […]
Anti-money laundering (AML) and graph analytics is a match made in heaven. A lot of anti-money laundering use cases require identifying suspicious connections whereas graph analytics is designed to analyze complex connections from big data at scale. In this article we will provide a series of examples where graph analytics can be used to fight back […]
Why should Anti-Money Laundering (AML) teams pay attention to graph analytics? In this article we will dive into the problems with today’s AML compliance technology, how it impacts anti-money laundering and how graph analytics can provide a path forward for a more effective detection and investigation of AML cases.
Discover our user stories and learn about the challenges they overcame with graph technology. In this post, our partners from tech4pets explain how their organization is helping animal welfare associations and authorities dismantle pet trafficking networks through the collection and analysis of online data.