International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 10, Issue 7 (July 2023), Pages: 48-53

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 Original Research Paper

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

 Author(s): 

 Suraya Masrom 1, Masetah Ahmad Tarmizi 2, Sunarti Halid 2, Rahayu Abdul Rahman 2, *, Abdullah Sani Abd Rahman 3, Roslina Ibrahim 4

 Affiliation(s):

 1Computing Science Studies, College of Computing, Informatics and Media, Universiti Teknologi Mara, Perak Branch Tapah Campus, Perak, Malaysia
 2Faculty of Accounting, Universiti Teknologi Mara, Perak Branch Tapah Campus, Perak, Malaysia
 3Faculty of Sciences and Information Technology, Universiti Teknologi Petronas, Perak, Malaysia
 4Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-7787-1096

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.07.007

 Abstract:

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.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Money laundering, Professional accountants, Anti-money laundering regulations, Machine learning prediction model, Compliance behavior

 Article History: Received 25 January 2023, Received in revised form 9 March 2023, Accepted 10 May 2023

 Acknowledgment 

We acknowledge the financial support granted by the Ministry of Higher Education under FRGS grant (600-IRMI/FRGS 5/3 (208/2019). We also appreciate Universiti Teknologi MARA for the full support.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Masrom S, Tarmizi MA, Halid S, Rahman RA, Rahman ASA, 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): 48-53

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 Figures

 Fig. 1 Fig. 2

 Tables

 Table 1 Table 2 Table 3 Table 4

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