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