Financial institutions have a regulatory requirement to monitor account activity for anti-money laundering (AML). Regulators take the monitoring and reporting requirements very seriously as evidenced by a recent set of FinCEN fines. One challenge with AML is that it rarely manifests as the activity of a single person, business, account, or a transaction. Therefore detection requires behavioral pattern analysis of transactions occurring over time and involving a set of (not obviously) related real-world entities. For large transactions, banks file Currency Transaction Reports (CTR) that are used by FinCEN for processing and analysis.

Trying to think of a practical, real world use of Machine Learning? Bank and financial institutions have regulatory requirements to monitor account activity for money laundering activities. Regulators around the world take these monitoring and reporting requirements very seriously. However, the big challenge facing anti-money laundering (AML) efforts is that money laundering rarely appears in the activity of a single person, business, account, or a transaction. Money launderers have gotten quite sophisticated.

Financial institutions have a regulatory requirement to monitor account activity for anti-money laundering (AML). Regulators take the monitoring and reporting requirements very seriously as evidenced by a recent set of FinCEN fines. One challenge with AML is that it rarely manifests as the activity of a single person, business, account, or a transaction. Therefore detection requires behavioral pattern analysis of transactions occurring over time and involving a set of (not obviously) related real-world entities. For large transactions, banks file Currency Transaction Reports (CTR) that are used by FinCEN for processing and analysis.