Purpose The purpose of this paper is to analyse privacy and national surveillance laws in Australia, including but not limited to the federal Privacy Act 1988 and the federal Anti-Money Laundering and Counter-Terrorism Financing Act 2006 (AML/CTF), to determine whether a tension exists between these two statutes within the context of the deployment of central bank digital currency (CBDC) in Australia. Design/methodology/approach The paper adopts doctrinal and normative approaches for analysis of the relevant legislations. Furthermore, the paper adopts a functionalist theoretical perspective to interrogate the interrelationship between regulation and society. Findings The paper suggests that the relevant legislations contain undefined terms, such as “reasonable grounds”, which may give the appearance of a balanced approach to the interactions between privacy and national surveillance laws within the context of deployment of CBDCs. The paper argues that lack of definition of these terms, however, renders the terms ineffective, leading to a potential regulatory overreach. The paper recommends administrators need internal policies and procedures that would give clear guidance on the possible meanings of those legislative terms to achieve the balance desired between privacy and national security requirements in Australia when deploying CBDCs. It is suggested that internal policies are to base suspicion on factual basis for decision-makers to have reasonable grounds for contravening privacy legislations. This may be achieved through requiring decision-makers to justify their decisions and consider alternative options before infringing on privacy, thus enhancing accountability. Furthermore, it is suggested that the transparency and traceability provided by distributed ledger technologies will compel decision-makers to assess the benefits of privacy violations against their costs, promoting a balanced approach to surveillance and personal data disclosure. Originality The originality of the paper lies in its seminal analysis of the interaction of privacy an anti-money laundering laws in the context of the introduction of a central bank digital currency.

Purpose This paper aims to illuminate actions to be executed by the bank’s financial crime compliance department on suspected money mules. Design/methodology/approach This paper outlines the actions by collecting facts from existing literature review, reports and news on money mule. Findings Money mule phenomenon is a threat to the financial system. The irresponsible actions by money mules allow illicit funds to pass through the banks, resulting in money trail dissemblance and prevent detection. The banks must not extend their services to money mules, and actions of suspicious transaction report filing, exit relationship, watchlisting, police report and interbank intelligence exchange should be initiated. Originality/value This paper presents mandatory actions to be taken by financial crime compliance department after completion of money mule investigation by anti-money laundering analyst. This study will be beneficial for future money mule researchers, enforcement agencies and practitioners in the banking industry.

Purpose This study aims to examine a range of factors − encompassing both micro- and macro-level perspectives, as well as institution- and industry-specific characteristics − that may account for the persistent exposure of banks to money laundering risk. This vulnerability persists despite ongoing efforts to curtail the exploitation of the banking sector as a conduit for illicit financial activities. Drawing on routine activity theory, this study analyzes how the convergence of motivated offenders, suitable targets and the absence of capable guardianship within the banking sector facilitates the continued occurrence of money laundering. Design/methodology/approach This study uses a qualitative research methodology, with a primary focus on the review and analysis of secondary data obtained from reports, official documents and relevant academic literature. Findings Numerous instances of money laundering demonstrate that, despite the implementation of robust safeguards, banks continue to knowingly or unknowingly internalize money laundering risk within their institutions. Although various risk management strategies have been adopted by banks, alongside regulatory interventions by authorities to shield the banking sector from such risk, complete immunity remains elusive. Accordingly, proactive and adaptive measures are essential to strengthen anti-money laundering efforts and more effectively combat the occurrence of money laundering activities. Practical implications The findings of this study suggest that banks remain vulnerable to money laundering risk. Consequently, they must continuously strengthen their efforts to assess pervasive threats and refine strategic approaches to effectively mitigate this risk. Originality/value This study offers a valuable contribution to the literature on money laundering by providing a contemporary analysis of the pervasive threats that banks continue to face.

Purpose This study aims to conduct a systematic literature review on illicit financial flows (IFFs), with a focus on uncovering the institutional and behavioral factors that shape such flows at global, national and corporate levels. Design/methodology/approach Following the PRISMA protocol, 25 highly cited peer-reviewed articles (2005–2023) were retrieved from Scopus. Manual thematic coding and deductive synthesis were applied to categorize evidence related to enforcement failures, regulatory arbitrage, institutional loopholes and the dual role of technology. A new conceptual framework links IFF dynamics to governance and cross-border accountability. Findings Four key themes emerged: definitional ambiguities and inconsistent metrics, regulatory arbitrage through trade misinvoicing and capital shifting, opaque intermediaries and shell structures and digital enablement via fintech and unregulated platforms. These patterns reflect a broader enforcement–enablement dynamic, with limited institutional perspectives and weak cross-jurisdictional regulatory coordination. Research limitations/implications This review is limited to English-language journal publications indexed in Scopus, potentially omitting gray literature and policy-based regional insights, particularly from low-income jurisdictions. Originality/value This study offers a theory-informed synthesis of IFFs by reframing them as outcomes of systemic governance failures rather than isolated financial crimes. It integrates governance, compliance and deterrence perspectives into the enforcement–enablement framework and aligns findings with Sustainable Development Goals 16.4 and 17.

Purpose This study aims to explore how perpetrators of illegal financial practices, especially money laundering, construct narratives, euphemisms and rationalizations to justify their actions. The main focus is directed at the ways in which they engineer language and meaning to obscure the criminal dimensions of their actions in digital spaces and online communities. Design/methodology/approach This study uses an in-depth passive observation approach through social media searches such as YouTube and Twitter related to money laundering cases. Data is examined using a combination of language framing analysis to identify language patterns and euphemisms used by perpetrators, and visual narrative analysis to understand visual and symbolic representations that strengthen the justification narrative. This analysis is complemented by a language semiotic approach to interpret the hidden meanings built into the online communication practices. Findings The study found that financial crime perpetrators consistently used a variety of rationalization strategies to justify their illegal actions. These strategies included neutralization of responsibility, moral justification and the use of euphemisms to obscure the criminal dimension of their behavior. These findings suggest that cognitive dissonance plays a significant role in maintaining the psychological comfort of perpetrators, allowing them to maintain a positive self-image even though their actions clearly violate legal norms and social ethics. Research limitations/implications The results of this study provide important contributions to the literature on financial crime and moral psychology by revealing that money laundering practices are not solely driven by economic motives, but also influenced by complex psychological mechanisms, such as moral rationalization and cognitive dissonance. Practical implications The findings of this study indicate that money laundering prevention efforts need to be designed more holistically. In addition to strengthening regulatory and law enforcement aspects, prevention strategies must also include value-based ethics education, as well as psychological interventions and strategic communication aimed at reducing the cognitive rationalization used by perpetrators. Originality/value This study offers an alternative perspective in understanding the narrative and psychological dynamics behind money laundering practices, by emphasizing how perpetrators reframe illegal acts through language strategies and moral justifications. Through an online ethnographic (netnographic) approach and narrative analysis rooted in cognitive dissonance and linguistic euphemism theories, this study contributes to the development of more comprehensive anti-money laundering policies, considering psychosocial aspects in addition to legal-formal aspects.

Purpose This study explores the application of machine learning (ML) algorithms to enhance the detection and reporting of Suspicious Activity Reports (SARs) in California’s financial sector. This research aims to improve anti-money laundering (AML) compliance by evaluating the effectiveness of advanced ML techniques, specifically CatBoost and Decision Tree algorithms, in identifying suspicious financial transactions. Design/methodology/approach This research uses a comprehensive methodological framework involving the analysis of 45,000 SAR filings from financial institutions and regulatory agencies in California, dating back to 2018. Various ML algorithms, including linear regression, random forest, decision tree and CatBoost, are used to analyze SAR filing patterns and predict suspicious transactions. Findings The findings reveal that CatBoost outperforms other models, offering a better fit to the data and higher predictive accuracy with a low RMSE and high cross-validation scores. The Decision Tree algorithm also demonstrates significant promise but is slightly less effective than CatBoost. This study confirms that ML algorithms, particularly CatBoost, significantly improve the detection and reporting of suspicious financial activities, thereby enhancing AML compliance. Originality/value This research contributes to the literature by integrating advanced ML techniques into AML compliance, moving beyond traditional statistical approaches. The findings provide practical implications for financial institutions, highlighting the potential of ML algorithms to enhance the effectiveness of SAR filings and bolster regulatory efforts in mitigating financial crime. This study underscores the value of ML in developing targeted policies to curb illicit financial activities and advance AML analytical capabilities.

Purpose The requirement for Member States to maintain National Central Registers is a part of a broader strategy aimed at ensuring economic security for citizens and businesses operating within the European Union (EU) by combating financial crime. Unfortunately, in terms of the definition of beneficial owner and the maintenance of National Central Registers, the regulatory landscape has for several years been plagued by a critical lack of harmonisation. Although two EU Directives, 2015/849 and 2018/843, have come into force in national law, individual Member States have been using different criteria regarding the term beneficial owner. The purpose of this paper is to analyse the functioning of the beneficial ownership registers in the EU and to assess the reform initiated in this area by the European Commission in 2021 – both in terms of the differences that exist and the projects aimed at harmonising regulations. Design/methodology/approach To verify the issue of beneficial owners in the EU, a basic research question was formulated – what are the reasons for the lack of harmonisation in the maintenance of National Central Registers? The hypothesis adopted is that the differences in how the beneficial ownership registers function can be eliminated only when the new Anti-Money Laundering (AML) Directive 2024/1640 is correctly implemented and Regulation 2024/1624 is correctly used by Member States. Two research methods were used in this study. The primary one was an institutional-legal analysis. By verifying the directives and regulations published by EU bodies, the current model for defining and registering beneficial owners was analysed. The results obtained were compared against the ideas included in the package of legislative proposals announced in 2021. On this basis, critical comments were formulated on the directions of reforms that have been adopted. The second method was a comparative analysis of the functioning of the National Ownership Registers in all Member States – especially as concerns personal data protection. Findings The research findings revealed that ambiguity in the definition of beneficial owner has given rise to numerous problems. A large part of the data entered in the registers in EU countries is questioned by obliged entities and public institutions. The lack of harmonisation of personal data protection is also a problem. These issues reduce the value of the registers as a tool to effectively combat money laundering. This study ultimately shows that the key to harmonisation is not merely adopting the new AML Directive, but ensuring that Member States implement it correctly. Originality/value This paper discusses the important issue of regulatory requirements introduced under EU law for private sector and public institutions. This research presents a number of risks related to the proposed reforms and critical reflections on the functioning of beneficial ownership registers. In addition, to the best of the author’s knowledge, this is one of the first scholarly texts to identify and systematise the key challenges related to the registers’ functioning in EU Member States.

Purpose The purpose of this paper is to systematically review and evaluate recent anti-money laundering (AML) research, focusing on methodological shifts toward machine learning and network analysis, and identify key challenges and future directions for effective and ethical AML. Design/methodology/approach This is a systematic review that follows Preferred Reporting Items for Systematic Reviews and PRISMA guidelines. An analysis of 45 studies (2017–2024) was conducted via Google Scholar using structured content analysis with a bi-dimensional framework (methodology and contextual applicability). Findings AML research shows a paradigm shift from statistics to machine learning and network analysis. Mixed methods are increasingly important. Key challenges include cryptocurrencies, balancing detection with privacy and model interpretability/scalability. The literature shows significant variation in methods and results across operational contexts, but few studies offer direct comparisons of their relative effectiveness. Network analysis effectiveness depends on regulatory context and data sharing. The reviewed studies reveal ongoing discussion and varied approaches regarding model complexity versus practical applicability in diverse settings. Similarly, a debate on the factors influencing network analysis effectiveness emerges, frequently pointing to the critical roles of regulatory frameworks and data-sharing capabilities, though without a unified consensus on optimal implementation across all contexts. Research limitations/implications This study reveals the need for research into adaptable models, context-specific solutions, privacy-preserving analytics and the interplay between AML evolution and criminal adaptation. Practical implications This study recommends layered analytics, data-sharing frameworks and interpretability for FIs; graduated compliance, sandboxes and outcome metrics for regulators; and interoperable, tailored and simplified solutions for tech providers. Social implications The broader implications of this research extend beyond immediate technical considerations to encompass significant societal impacts. The findings of this study reveal complex interactions between surveillance capabilities, privacy considerations and regulatory effectiveness. The analysis suggests that successful AML frameworks must balance competing demands: the need for comprehensive monitoring systems against legitimate privacy concerns and the requirement for transparent processes against the complexity of modern financial networks. These considerations have substantial implications for policy development and regulatory frameworks. Originality/value This review applies a unique bi-dimensional framework for analysis. It peaks at the contextual variance influencing network analysis effectiveness, questions a straightforward “complexity equals superiority” assumption by pointing to practical implementation factors and notes the emerging potential of hybrid models. Additionally, this study identifies critical gaps, such as the co-evolution of methods and counter-methods, and various implementation barriers.