Detecting money laundering transactions with machine learning

Purpose The purpose of this paper is to 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. Design/methodology/a...

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Bibliographic Details
Published in:	Journal of money laundering control Vol. 23; no. 1; pp. 173 - 186
Main Authors:	Jullum, Martin, Løland, Anders, Huseby, Ragnar Bang, Ånonsen, Geir, Lorentzen, Johannes
Format:	Journal Article
Language:	English
Published:	London Emerald Publishing Limited 27-01-2020 Emerald Group Publishing Limited
Subjects:	Financial institutions Fraud Machine learning Mobile commerce Money laundering Telecommunications systems United States > US Norway Supervised learning Suspicious transaction XGBoost Machine learning
Online Access:	Get full text
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Summary:	Purpose The purpose of this paper is to 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. Design/methodology/approach A supervised machine learning model is trained by using three types of historic data: “normal” legal transactions; those flagged as suspicious by the bank’s internal alert system; and potential money laundering cases reported to the authorities. The model is trained to predict the probability that a new transaction should be reported, using information such as background information about the sender/receiver, their earlier behaviour and their transaction history. Findings The paper demonstrates that the common approach of not using non-reported alerts (i.e. transactions that are investigated but not reported) in the training of the model can lead to sub-optimal results. The same applies to the use of normal (un-investigated) transactions. Our developed method outperforms the bank’s current approach in terms of a fair measure of performance. Originality/value This research study is one of very few published anti-money laundering (AML) models for suspicious transactions that have been applied to a realistically sized data set. The paper also presents a new performance measure specifically tailored to compare the proposed method to the bank’s existing AML system.
ISSN:	1368-5201 1758-7808
DOI:	10.1108/JMLC-07-2019-0055