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. However financial institutions have to also monitor for "structuring" or "smurfing" which are multiple (usually smaller) related deposits designed to avoid the currency reporting requirements.
Monitoring performed by financial institutions broadly fall into two complementary categories, knowledge-based systems and link analysis. There are a variety of approaches to knowledge-based AML systems including statistical analysis, machine learning.and data visualization.
Applying machine learning to AML has been challenging due to the limited availability of labeled datasets. However there are a number of unsupervised techniques that may be worth considering.
Network modeling is a powerful approach to AML analysis (Mser). Each account and real-world entity is set up as a node of a graph and transactions constitute the edges. Edges can have weights. Edge weights typically reflect the volume or the monetary value of transactions flowing between nodes.
Once a graph structure has been created, analysis can reveal the relationships between the nodes including: