Financial institutions’ Know Your Customer (KYC) and Anti-Money Laundering (AML) processes have historically incorporated “negative news” searches. When done correctly, negative news searches can inform [...]

“It is the job of the IC (Intelligence Community) to analyze data, connect disparate data sets, apply context to data, infer meaning from data, and ultimately make [...]

The Paycheck Protection Program (PPP) is responding to the COVID-19 pandemic and its economic impact with remarkable speed. But this rapid response also provides [...]

Quantifind Leverages Global Name Models to Identify the Right Mike, Fast Quantifind has developed a global name rarity model for 172 countries that reduces [...]

Effective risk monitoring for banks means keeping up with a rapidly changing, dynamic world. Modern risk management systems must break from patterns of the past [...]

Quantifind can eliminate 90% of an alert backlog with a one-time batch screening Many financial institutions were already struggling to keep up with increased requirements [...]

Automatically correlating public campaign contribution data with criminal data to reveal high-risk links.   At Quantifind we believe in the power of combining two datasets [...]

Using machine learning to discover signals that categorize companies into high-risk areas. A common use case at banks and other financial institutions is to discover [...]

Building out a criminal network using entity extraction over a large number of unstructured documents. As mentioned in our introductory post, one of our primary use [...]

Analyzing the unstructured data available in news, forums, and public social media to increase investigation efficiency and better calibrate your customer risk scoring. At Quantifind, [...]