Money laundering risk from emerging markets: the case of Vietnam

Abstract

Purpose The purpose of this paper is to apply the framework Price (2008) and HSBC Money Laundering Risk Procedures 2016 for estimating the risk contribution of each individual customer in Vietnamese banking system using the information from the survey in South East region in Vietnam in general and Ho Chi Minh city in specific. Design/methodology/approach Based on the collected data from the survey, the Money Laundering Risk Score (MLRS) is calculated for each customer who is using the services and products of Vietnamese commercial banks by the enhanced measurement model of Christopher Price, the ordinary least squares and three variations of logistic regression model. Findings This paper proposes an appropriate estimation of the money laundering risk (MLR) for personal customer using the most significant factors that affects MLR and suggests practical recommendations for commercial banking system. Practical implications This paper suggests an intuitive method to estimate the contribution of each customer factor on their MLRS. Originality/value The higher respondent’s group of age lead to the higher MLR occurred in the financial market. Follow the works of Wolfsberg et al. (2006), Sathan and Mahendhiran (2007), Usman Kemal (2014) and Reganati and Oliva (2017), this paper also confirms negative relationship between MLR with the respondent’s group of salary and the academic level. This indicated that the lower amount of money the respondents earn and lower academic level they were, the higher degree of MLR.

Predicting Money Laundering Using Machine Learning and Artificial Neural Networks Algorithms in Banks

“…The and ANN algorithms employed in this paper perform reasonably well in identifying and labelling money-laundering transactions (see also Features such as the time of the day and the amounts of money coming in and transferred out should be comprehensively monitored and scrutinized with regulatory technology. Other factors that should be monitored closely are international payments, sudden changes in the source of income, considerable anomalies in the amounts of money transfers, and any other suspicious activity that should be immediately scrutinized as part of the due-diligence process (Ba and Huynh, 2018).…”

“…Financial institutions should use the ndings from this paper to maintain lower money-laundering risks and conduct due-diligence background checks on the source of the cash-in funds and the destination of the transfer-out funds (Ba and Huynh, 2018;Tran and Nguyen, 2017). The machine-learning and ANN algorithms can be used to inform and continuously update money-laundering risks for each customer and incorporate new features such as salary, occupation, and source of income (Tran and Nguyen, 2017).…”

“…While rule-based methods have long been the standard for detecting and preventing money laundering, AI models offer a more sophisticated approach considering various factors. Studies have shown that ML and AI models can outperform rule-based methods in several ways, including accuracy, speed, and scalability (seeBa and Huynh, 2018;Jullum et al, 2020;Singh and Best, 2019). Money laundering models based on ML and AI are said to have higher predictive accuracy than the traditional rule-based approach to detection(Chen et al, 2018;Jullum et al, 2020).…”

Money Laundering Intentions of the Bankers: Some Insights from Past Literature

“…This separation can be done through a series of financial transactions designed with complex transaction networks to be traced. This stage is related to money abroad through a series of complicated financial transactions (Ba & Huynh, 2018). Such trades are designed to disguise the audit trail and provide anonymity to obscure the origin of illegal funds.…”

“…ML can disrupt the functioning of market mechanisms (Ba & Huynh, 2018). The illegal acquisition of money causes no protection of property rights, the market becomes inefficient as indicated by increased market transaction costs, and there is asymmetrical access to market information.…”