Money laundering is the process of transferring profit from crime and illegal activities into legitimate assets. Based on the United Nations Office on Drugs and Crime, 2 to 5% of global GDP, or $800 billion – $2 trillion, is laundered globally on an annual basis. The laundered money often finances drug trafficking, human trafficking and terrorist activities.
Advanced analytic techniques are increasingly being employed to identify and reduce illegal activities such as money laundering. Machine Learning (ML) is playing an increasingly important role by way of two main mechanisms: transaction behavioral pattern analysis and network structure. Many financial institutions combine these two mechanisms to construct a rule based system to flag suspicious transactions. A challenge is that these systems can generate significant false positives which require tedious resource-intensive investigations.
In addition, such rule-based systems are challenged when seeking to detect new patterns and/or activities. Modern data mining and machine learning methods can help financial institutes by reducing system-generated false positives. In this talk, new machine learning methods employed to discover money laundering patterns will be presented.