Trading Desk Behavior Modeling via LSTM for Rogue Trading Fraud Detection

Marine Neyret, Jaouad Ouaggag, Cédric Allain

Abstract

Rogue trading is a term used to designate a fraudulent trading activity and rogue traders refer to operators who take unauthorised positions with regard to the mandate of the desk to which they belong and to the regulations in force. Through this fraudulent behavior, a rogue trader exposes his group to operational and market risks that can lead to heavy financial losses and to financial and criminal sanctions. We present a two-step methodology to detect rogue trading activity among the deals of a desk. Using a dataset of transactions booked by operators, we first build time series behavioral features that describe their activity in order to predict these features’ future values using a Long Short-Term Memory (LSTM) network. The detection step is then performed by comparing the predictions made by the LSTM to real values assuming that unexpected values in our trading behavioral features predictions reflect potential rogue trading activity. In order to detect anomalies, we define a prediction error that is used to compute an anomaly score based on the Mahalanobis distance.

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