AML Optimization – Do More with Less

With AML fines and regulator demands growing by the day, the stakes for AML teams have never been higher. All signs of potential AML activity have to be monitored, which puts a massive burden on investigation teams. But traditional approaches to AML transaction modeling are rigid, prone to false alarms and missing true incidents of money laundering. AML optimization efforts tend to be expensive and are often manual service engagements.

Unlike entrenched AML transaction monitoring solutions, our solution was built from the ground up to use the power of unsupervised machine learning to drive superior AML optimization. Rules-based and supervised machine learning systems require constant tuning as fraudsters discover new ways to evade them. Every false positive means wasted investigation cycles. Every false negative is an existential risk to your business. The good news is that we can help.

Unsupervised Machine Learning for AML Optimization

Reduced False Positives
deep visibility into subtle relationships that traditional systems miss in exchange for  better efficiency
Reduced False Negatives
we don’t rely on prior knowledge of specific money laundering patterns, increasing effectiveness.
Easy, Automatic Upgrades
modern deployment means you don’t have to deal with painful 3-5 year upgrade cycles. Get the newest capabilities the minute they are ready.
Standalone or Add-on Deployment
no need to replace an existing AML transaction monitoring solution until you are ready