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 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.