The Challenges of Implementing Anti‐Money Laundering Regulation: An Empirical Analysis

Abstract

For over three decades, money laundering has been an area of concern for policymakers and law enforcement, with significant efforts undertaken at national and international levels to combat it. Recently, laundering of criminal proceeds using real property has attracted increased attention amongst policymakers. Various efforts are now being undertaken to tackle money laundering in the UK property market, but there are still significant difficulties in its practical implementation. Drawing upon semi-structured interviews with estate agents and compliance officials, this study identifies critical aspects of AML compliance that are particularly problematic for those involved in it. In so doing, this article delivers a new perspective, by analysing data gathered with the first empirical study on the implementation of AML obligations in practice (in the UK property market) since the introduction of the 2017 Money Laundering Regulations.

Predicting Suspicious Money Laundering Transactions using Machine Learning Algorithms

“…Researchers have observed that inter-agency coordination and collaboration for AML compliance and AML regulations are essential to detecting and preventing money laundering (Loh 2021;Zavoli and King 2021;Ofoeda et al 2022). Effective collaboration between AML compliance and AML enterprises can promote a unified approach to curbing money laundering while helping to create an environment of mutual trust among law enforcement, banks, and other involved institutions (Ofoeda et al 2022;Zavoli and King 2021;Lokanan 2022). Collaborative efforts can ensure that companies and organizations comply with applicable AML legal requirements, further strengthening domestic and international AML defenses (Chen et al 2018;Masciandaro and Filotto 2001).…”

“…The key takeaway from the literature is that SARs can be incredibly useful in detecting money laundering signs. However, this is only true when SARs are used with additional measures such as transaction monitoring, CDD, and KYC (Singh and Best 2019;Lokanan 2022;Zavoli and King 2021). This underscores the need for multiple layers of defense to stay ahead of money launderers' attempts to infiltrate financial systems (Lokanan 2022;dalla Pellegrina et al 2020;Ketenci et al 2021).…”

“…This underscores the need for multiple layers of defense to stay ahead of money launderers' attempts to infiltrate financial systems (Lokanan 2022;dalla Pellegrina et al 2020;Ketenci et al 2021). Studies have also shown that the accuracy and reliability of SARs are highly dependent on the knowledge and expertise of those preparing them (Zavoli and King 2021;Loh 2021). Accordingly, compliance personnel must receive training and appropriate mentorship to produce useful SAR reports that meet regulatory standards (Zavoli and King 2021;Chen et al 2018).…”

“…Studies have also shown that the accuracy and reliability of SARs are highly dependent on the knowledge and expertise of those preparing them (Zavoli and King 2021;Loh 2021). Accordingly, compliance personnel must receive training and appropriate mentorship to produce useful SAR reports that meet regulatory standards (Zavoli and King 2021;Chen et al 2018).…”

“…ML has been used to analyze transactions and identify those that may violate sanctions governments or international organizations have imposed. With the growth of international trade and the diversity of customers' financial activities, it is becoming increasingly complex and timeconsuming to ensure that one is not violating a sanction national or international authorities have imposed (Zavoli and King 2021;Kurum 2020;Nissan 2012). ML and advanced AI-based technologies offer automated solutions for analyzing financial activities of customers and organizations and for detecting potential violations of AML regulations (Kurum 2020;Lokanan 2022;Rocha-Salazar, Segovia-Vargas, and Camacho-Miñano 2021).…”