Staff View: Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants

Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants

Money laundering represents a significant global threat, necessitating the vigilance of professional accountants in detecting and reporting suspicious customer activities within their jurisdiction to the relevant authorities. Despite the legal obligation to comply with anti-money laundering regulati...

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Main Authors:	Masrom, S., Tarmizi, M.A., Halid, S., Rahman, R.A., Abd Rahman, A.S., Ibrahim, R.
Format:	Article
Published:	2023
Online Access:	http://scholars.utp.edu.my/id/eprint/37609/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168963282&doi=10.21833%2fijaas.2023.07.007&partnerID=40&md5=82cfa52483069e52edc2fedf6897bc56
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spelling	oai:scholars.utp.edu.my:376092023-10-13T13:04:55Z http://scholars.utp.edu.my/id/eprint/37609/ Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants Masrom, S. Tarmizi, M.A. Halid, S. Rahman, R.A. Abd Rahman, A.S. Ibrahim, R. Money laundering represents a significant global threat, necessitating the vigilance of professional accountants in detecting and reporting suspicious customer activities within their jurisdiction to the relevant authorities. Despite the legal obligation to comply with anti-money laundering regulations, professional accountants' adherence to these measures remains insufficient. Previous research on machine learning techniques for combating money laundering has predominantly concentrated on predicting suspicious transactions, rather than evaluating compliance behavior. This study aims to develop a machine learning prediction model to assess the inclination of professional accountants towards adhering to anti-money laundering regulations, serving as an early signal system to gauge their willingness to abide by the law in their professional responsibilities. The research elaborates on the design and implementation of machine learning models based on three algorithms: Decision Tree, Gradient Boosted Tree, and Support Vector Machine. The paper offers two types of comparisons from distinct perspectives: firstly, the performance of each algorithm in predicting real cases of anti-money laundering compliance, and secondly, the contribution of attributes measured by weights of correlation in different algorithms. Alongside demographic factors, the study evaluates the effectiveness of each algorithm in anti-money laundering compliance by utilizing five attributes derived from the Protection Motivation Theory (PMT). The findings demonstrate the significance of all attributes, including demography and PMT, in all machine learning models, with both Gradient Boosted Tree and Support Vector Machine achieving a proportion of variance of 0.8 or higher. This indicates the potential of these algorithms in effectively measuring and predicting professional accountants' intentions to comply with anti-money laundering regulations. Â© 2023 The Authors. Published by IASE. 2023 Article NonPeerReviewed Masrom, S. and Tarmizi, M.A. and Halid, S. and Rahman, R.A. and Abd Rahman, A.S. and Ibrahim, R. (2023) Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants. International Journal of Advanced and Applied Sciences, 10 (7). pp. 48-53. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168963282&doi=10.21833%2fijaas.2023.07.007&partnerID=40&md5=82cfa52483069e52edc2fedf6897bc56 10.21833/ijaas.2023.07.007 10.21833/ijaas.2023.07.007 10.21833/ijaas.2023.07.007
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Institutional Repository
url_provider	http://eprints.utp.edu.my/
description	Money laundering represents a significant global threat, necessitating the vigilance of professional accountants in detecting and reporting suspicious customer activities within their jurisdiction to the relevant authorities. Despite the legal obligation to comply with anti-money laundering regulations, professional accountants' adherence to these measures remains insufficient. Previous research on machine learning techniques for combating money laundering has predominantly concentrated on predicting suspicious transactions, rather than evaluating compliance behavior. This study aims to develop a machine learning prediction model to assess the inclination of professional accountants towards adhering to anti-money laundering regulations, serving as an early signal system to gauge their willingness to abide by the law in their professional responsibilities. The research elaborates on the design and implementation of machine learning models based on three algorithms: Decision Tree, Gradient Boosted Tree, and Support Vector Machine. The paper offers two types of comparisons from distinct perspectives: firstly, the performance of each algorithm in predicting real cases of anti-money laundering compliance, and secondly, the contribution of attributes measured by weights of correlation in different algorithms. Alongside demographic factors, the study evaluates the effectiveness of each algorithm in anti-money laundering compliance by utilizing five attributes derived from the Protection Motivation Theory (PMT). The findings demonstrate the significance of all attributes, including demography and PMT, in all machine learning models, with both Gradient Boosted Tree and Support Vector Machine achieving a proportion of variance of 0.8 or higher. This indicates the potential of these algorithms in effectively measuring and predicting professional accountants' intentions to comply with anti-money laundering regulations. Â© 2023 The Authors. Published by IASE.
format	Article
author	Masrom, S. Tarmizi, M.A. Halid, S. Rahman, R.A. Abd Rahman, A.S. Ibrahim, R.
spellingShingle	Masrom, S. Tarmizi, M.A. Halid, S. Rahman, R.A. Abd Rahman, A.S. Ibrahim, R. Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants
author_facet	Masrom, S. Tarmizi, M.A. Halid, S. Rahman, R.A. Abd Rahman, A.S. Ibrahim, R.
author_sort	Masrom, S.
title	Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants
title_short	Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants
title_full	Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants
title_fullStr	Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants
title_full_unstemmed	Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants
title_sort	machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants
publishDate	2023
url	http://scholars.utp.edu.my/id/eprint/37609/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168963282&doi=10.21833%2fijaas.2023.07.007&partnerID=40&md5=82cfa52483069e52edc2fedf6897bc56
_version_	1781707931800043520
score	13.211869

Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants

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