Detecting money laundering transactions -- which transactions should we learn from?

We develop, describe and validate a machine learning model for prioritising which financial transactions should be manually investigated for potential money laundering. The model is applied to a large data set from Norway's largest bank, DNB. This is one of very few published anti-money laundering models for suspicious transactions that has been trained and validated on a realistically sized data set. We demonstrate that the common approach of not utilising non-reported alerts, i.e.~transactions that are investigated but not reported, in the training of the model can lead to sub-optimal results. We also demonstrate the benefit of including normal (uninvestigated) transactions in the training of the model, and study whether explicitly modelling the probability that a transaction is a normal transaction can improve the money laundering detection rate. Therefore, we present a new performance measure for comparing our method to the existing anti-money laundering system in the bank. Using this performance measure, we clearly outperform the bank's current approach.