Qualifying and raising anti-money laundering alarms with deep learning

We propose a deep learning approach to qualify and raise anti-money laundering alarms in banks. The motivating idea is to replace predefined rules with latent features automatically extracted from transaction sequences. To this end, we experiment with recurrent and Transformer encoder layers. We test the approach with a large data set from Spar Nord Bank. Our best model employs gated recurrent units and self-attention. When used to qualify alarms raised by a traditional AML system, the model is able to reduce the number of false positives by more than 33.3% while retaining 98.8% of all true positives. In a small experiment, we also use the model to raise 75 new alarms on clients with high risk scores but with no traditional alarm inquiries in the last 12 months. After expert review, this prompted 26 clients to be reported to Danish authorities.