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. Deviations from the clusters indicate abnormal behaviors to be investigated. All this analysis can be done without a history of SAR filings; this is powerful when the historical SARs are not captured or when you want to find new anti-money launders and not just replicate known SAR patterns in data.
Here are the (impressive) results of this unsupervised analytic model:
- ~40% of SARs detected at 0.1% review rate
Outlier detection in unsupervised machine learning is applicable across many different industries. The applications will grow exponentially as we find that the speed and pace of data flowing at us exceeds the framework of historical data and outcome collection that we typically see in supervised analytics. Have you used outlier detection to solve any interesting problems?