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Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection

Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection This paper studies the interplay of machine learning and sampling scheme in an empirical analysis of money laundering detection algorithms. Using actual transaction data provided by a U.S. financial institution, we study five major machine learning algorithms including Bayes logistic regression, decision tree, random forest, support vector machine, and artificial neural network. As the incidence of money laundering events is rare, we apply and compare two sampling techniques that increase the relative presence of the events. Our analysis reveals potential advantages of machine learning algorithms in modeling money laundering events. This paper provides insights into the use of machine learning and sampling schemes in money laundering detection specifically, and classification of rare events in general. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Economics Springer Journals

Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection

Zhang, Yan; Trubey, Peter
Computational Economics , Volume 54 (3) – Oct 25, 2018
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21 pages

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Publisher
Springer Journals
Copyright
Copyright © 2018 by This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply
Subject
Economics; Economic Theory/Quantitative Economics/Mathematical Methods; Computer Appl. in Social and Behavioral Sciences; Operations Research/Decision Theory; Behavioral/Experimental Economics; Math Applications in Computer Science
ISSN
0927-7099
eISSN
1572-9974
DOI
10.1007/s10614-018-9864-z
Publisher site
See Article on Publisher Site

Abstract

This paper studies the interplay of machine learning and sampling scheme in an empirical analysis of money laundering detection algorithms. Using actual transaction data provided by a U.S. financial institution, we study five major machine learning algorithms including Bayes logistic regression, decision tree, random forest, support vector machine, and artificial neural network. As the incidence of money laundering events is rare, we apply and compare two sampling techniques that increase the relative presence of the events. Our analysis reveals potential advantages of machine learning algorithms in modeling money laundering events. This paper provides insights into the use of machine learning and sampling schemes in money laundering detection specifically, and classification of rare events in general.

Journal

Computational Economics – Springer Journals

Published: Oct 25, 2018

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