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Authors: Marine Neyret 1 ; Jaouad Ouaggag 2 and Cédric Allain 1

Affiliations: 1 DataLab, Institut Louis Bachelier, Paris, France ; 2 DataLab, Inspection Générale et Audit, Société Générale, Paris, France

Keyword(s): Behavior Modeling, LSTM, Recurrent Neural Networks, Outliers Detection, Trading Activity, Rogue Trading Detection, Unexpected Behavior Detection.

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 pre diction error that is used to compute an anomaly score based on the Mahalanobis distance. (More)

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Paper citation in several formats:
Neyret, M.; Ouaggag, J. and Allain, C. (2020). Trading Desk Behavior Modeling via LSTM for Rogue Trading Fraud Detection. In Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-440-4; ISSN 2184-285X, SciTePress, pages 143-150. DOI: 10.5220/0009680701430150

@conference{data20,
author={Marine Neyret. and Jaouad Ouaggag. and Cédric Allain.},
title={Trading Desk Behavior Modeling via LSTM for Rogue Trading Fraud Detection},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA},
year={2020},
pages={143-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009680701430150},
isbn={978-989-758-440-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA
TI - Trading Desk Behavior Modeling via LSTM for Rogue Trading Fraud Detection
SN - 978-989-758-440-4
IS - 2184-285X
AU - Neyret, M.
AU - Ouaggag, J.
AU - Allain, C.
PY - 2020
SP - 143
EP - 150
DO - 10.5220/0009680701430150
PB - SciTePress