Improving client risk classification with machine learning to increase anti-money laundering detection efficiency
| Date | 15 August 2024 |
| Pages | 93-107 |
| DOI | https://doi.org/10.1108/JMLC-03-2024-0040 |
| Published date | 15 August 2024 |
| Author | Endre Jo Reite,Johan Karlsen,Elias Grefstad Westgaard |
Improving client risk classification
with machine learning to increase
anti-money laundering
detection efficiency
Endre Jo Reite
NTNU Business School, Norwegian University of Science and Technology,
Trondheim, Norway, and
Johan Karlsen and Elias Grefstad Westgaard
Department of Economics, Norwegian University of Science and Technology,
Trondheim, Norway
Abstract
Purpose –This study aims to describeand empirically explore a new method for bank anti-money laundering
(AML) systems using machine learningmodels. Current automated money laundering detection systemsare
notorious for flagging many false positives, causing bank employees to spend unnecessary time manually
checking transactions that do notconstitute money laundering. Decreasing the number of false positivescan
free up resources for investigatingmoney laundering.
Design/methodology/approach –This study uses unique bank data on small- and medium-sized
enterprises (SMEs) to examine how various client risk classification models can predict future suspicious
transactions.This study explores various sources ofclient risk data and machine-learning approaches.
Findings –Client risk classification models can accurately predict suspicious future transactions. Adding
accounting data and credit score informationto client risk classification dramatically improves accuracy.This
makes it easier to balance the risk of missing suspicious transactions with the need to reduce the number of
false positives.
Practical implications –The suggested approach with readily available data sources and a focus on
classifying client risk in a dynamic model canhelp banks significantly improve their efficiency by targeting
their AML effortstoward the riskiest clients.
Originality/value –To the best of the authors’knowledge, this study is the first to empirically explore
machine learning in client risk classification,document how machine learning in client risk classificationcan
significantlyreduce false positives by incorporating novel, but readily available sources,such as credit risk and
accountingdata.
Keywords Money laundering, Machine learning, XGBoost, Supervised learning,
Client risk classification
Paper type Research paper
Introduction
Machine learning is increasingly used to detect money laundering activities to potentially
identify unusual financial behaviors and patterns (Alotibi et al., 2022). However, the
literature has limited researchon this technique, indicating the need for further exploration in
this area (Zhang and Trubey, 2019). With the increasing complexity and speed of banking
transactions, classifying client risk has become increasingly important in anti-money
Journal of Money
Laundering
Control
93
Journalof Money Laundering
Control
Vol.28 No. 1, 2025
pp. 93-107
© Emerald Publishing Limited
1368-5201
DOI 10.1108/JMLC-03-2024-0040
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1368-5201.htm
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