Unsupervised Learning Aids in Anti Money Launde...

Outlier detection leverages known and typical patterns to gain insights on the unknown. To do so, it uses unsupervised analytics. This isn't just theoretical: outlier detection machine learning is actively used in anti-money laundering efforts. When executing outlier analytics, countless customer's banking transactions continually adjust the behavioral archetypes associated with client accounts. When we plot the archetypal distributions of customers, we see that many SARs (suspicious activity reports) are outliers from normal customers along certain archetypes.