Anomaly Detection using a Deep Learning Multi-layer Perceptron to Mitigate the Risk of Rogue Trading

Sammanfattning: The term Rogue Trading is defined as the activity of someone at a financial organisation losing a large amount of money in bad or illegal transactions and trying to hide this. The activity of Rogue traders exposes financial organisations to huge risks and may lead to the organisation collapsing, which will affect other stakeholders like, for example, the customers. In order to detect potential Rogue Trading cases, Control Systems that monitor the employees and the positions they take on financial markets must exist. In this study, a two-step control system is suggested to monitor the margins on Foreign exchange (FX) Forwards traded by employees at the Swedish bank Skandinaviska Enskilda Banken (SEB). The first step in the control system uses a Deep Learning neural network trained on transactional data to predict the margin. The errors of the predictions versus the actual values are then in the second step of the control system used to find outliers which should be flagged for further investigation due to a too high deviation. The results show that the model hopefully can decrease the number of false positives yielded by the current Control Systems at SEB and thus reduce manual inspection of flagged transactions.