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Money laundering transaction detection with classification tree models

The detection of money laundering is a very important problem, especially in the financial sector. We propose a mathematical specification of the problem in terms of a classification tree model that ”automates” expert based manual decisions. We operationally validate the model on a concrete application that originates from a large Italian bank. The application of the model to the data shows a good predictive accuracy and, even more importantly, the reduction of false positives, with respect to the ”manual” expert based activity. From an interpretational viewpoint, while some drivers of suspicious laundering activity are in line with the daily business practices of the bank’s anti money laundering operations, some others are new discoveries.