Search Results (82)

This paper examines how scam compounds, money mules and crypto-assets operate as interdependent elements of contemporary money-laundering chains. It assesses whether existing anti-money laundering (AML) and crypto-asset regulatory frameworks are capable of disrupting these chains holistically, rather than addressing individual components in isolation, with particular reference to scam-compound activity in Southeast Asia. The study adopts a qualitative comparative case-study methodology grounded in legal and regulatory analysis. Four empirically grounded cases are examined: two Southeast Asian scam-compound enforcement cases (Cambodia and Myanmar) and two European crypto-asset seizure cases (Ireland and Italy). Judicial decisions, enforcement actions and regulatory instruments are analysed through a chain-based analytical framework aligned with Financial Action Task Force (FATF) standards, the EU Markets in Crypto-Assets Regulation (MiCA) and the Anti-Money Laundering Authority (AMLA) framework. The analysis reveals a structural divergence in enforcement strategies: Southeast Asian responses increasingly prioritise network- and infrastructure-level disruption of scam compounds, whereas European approaches remain largely centred on post-offence crypto-asset seizure through traditional proceeds-of-crime mechanisms. Across all jurisdictions, money mules emerge as a critical yet systematically under-regulated intermediary layer enabling the resilience of crypto-laundering operations. The paper advances existing AML typologies by conceptualising scam compounds, money mules and crypto-assets as interconnected components of a single crypto-laundering chain. This chain-based perspective offers a novel analytical and regulatory lens for understanding organised crypto-enabled fraud. The study is based on a qualitative, case-based design and does not aim for statistical generalisation. However, the analytical framework developed is transferable to other jurisdictions experiencing similar scam-compound and crypto-laundering dynamics. The findings suggest that effective AML enforcement requires coordinated intervention across multiple nodes of the laundering chain, including scam compound infrastructure and money mule networks, alongside traditional asset-seizure mechanisms and CASP supervision. By highlighting the structural links between scam compounds, coercive labour and crypto-laundering mechanisms, the paper underscores the broader social harms of crypto-enabled fraud and the need for integrated regulatory responses that address both financial crime and human exploitation. Full article

This paper examines how emerging economies, with a focus on India, transition from being primarily recipients of capital to becoming outward investors. It investigates whether domestic institutional governance, rather than rapid liberalization or extensive investment treaty networks, accounts for the sustained growth of both inward FDI and outward ODI. The study combines a detailed timeline of institutional developments with structural break tests, vector autoregression (VAR), and dynamic panel GMM analysis. This approach tracks the timing, spread, and longevity of reforms like the shift from FERA to FEMA and the digitalization of administration, examining their effect on capital flow patterns. Results show that major turning points in India’s FDI and ODI movements correspond with key governance reforms, such as replacing the Foreign Exchange Regulation Act with the Foreign Exchange Management Act, unifying investment policies, digitizing administration, and renegotiating treaties post-2016. Improvements in governance have a more significant and enduring impact on FDI than macroeconomic factors, while clearer regulation and stronger institutions are vital for boosting ODI. Once domestic institutional capacity is taken into account, the number of investment treaties does not significantly influence capital movements. The paper introduces a “transferability matrix” that highlights effective, low-cost reforms, such as civil penalty systems and digital governance, which other emerging economies can implement. It stresses that integrating into global capital markets depends more on developing solid domestic regulations than on rapid deregulation. The study also advances previous research by (1) combining FDI and ODI within a single institutional framework explaining both flows; (2) moving beyond static, perception-based measures to develop a comprehensive timeline showing how regulatory credibility is built over three decades; and (3) providing empirical proof that credible domestic institutions can replace large treaty networks in ensuring capital mobility. Full article

Financial fraud and money laundering continue to challenge financial stability and regulatory oversight, motivating the widespread adoption of machine learning models for transaction monitoring. Although ensemble models such as Random Forest and XGBoost achieve strong predictive performance, their deployment in high-stakes financial environments is constrained by limited interpretability, overconfident predictions, and the absence of principled mechanisms for expressing decision uncertainty. Emerging regulatory expectations increasingly emphasise transparency, accountability, and operational reliability, underscoring the need for evaluation frameworks that extend beyond predictive accuracy. This study proposes the Integrated Transparency and Confidence Framework (ITCF), a deployment-oriented approach that unifies model explainability, statistically valid uncertainty quantification, and operational decision support for fraud detection. ITCF combines instance-level explanations generated via Local Interpretable Model-Agnostic Explanations (LIME) with distribution-free uncertainty estimation using split conformal prediction. The framework incorporates selective explainability, abstention-based routing, and uncertainty-driven triage to support human-in-the-loop review. Using the PaySim dataset of 6,362,620 mobile-money transactions, Random Forest and XGBoost models are evaluated under extreme class imbalance using F1-score, AUC–ROC, and Matthews Correlation Coefficient (MCC). At a target coverage level of 90% ($\alpha =0.1$), both models achieve empirical coverage close to the target level, with XGBoost producing smaller prediction sets and superior recall, MCC, and latency. ITCF provides transaction-level explanations for uncertain cases and specifies an auditable workflow that is intended to support transparency, traceability, and risk-aware human review, thereby enabling defensible human decision-making in regulated environments. Overall, this study illustrates how explainability and uncertainty quantification can be combined in a deployment-oriented evaluation workflow while noting that real-world validation remains a future endeavour. Full article

The rapid expansion of global commerce has escalated the complexity of money laundering schemes, making the detection of illicit transfers an urgent but highly challenging research problem. In operational anti-money laundering (AML) systems, the extreme rarity of illicit transactions often overwhelms compliance teams with false positives, leading to severe “alert fatigue.” To address this critical bottleneck, this paper introduces an enhanced, probability-driven risk-prioritization framework utilizing an XGBoost classifier integrated with Bayesian Optimization (BO-XGBoost). By optimizing directly for the Area Under the Precision–Recall Curve (PR-AUC), the model is specifically tailored to rank high-risk anomalies under severe class imbalance. We validate the proposed approach on a rigorously resampled transaction dataset simulating a realistic 5% laundering rate. The BO-XGBoost model demonstrates exceptional prioritization capability, achieving an ROC-AUC of 0.9686 and a PR-AUC of 0.7253. Most notably, it attains a near-perfect Precision@1%, meaning the top 1% of flagged transactions are 100% true illicit activities, entirely eliminating false positives at the highest priority tier. Comparative and SHAP-based interpretability analyses confirm that BO-XGBoost easily outperforms sequence-heavy deep learning baselines. Crucially, it matches computationally expensive stacking ensembles in peak predictive precision while significantly surpassing them in operational efficiency, indicating its immense promise for resource-optimized, real-world compliance screening. Full article

Anti-money laundering (AML) in cryptocurrency networks presents significant challenges due to complex transactional relationships, severe class imbalance, and limited labeled data, which severely constrain the scalability and label efficiency of existing AML systems. Traditional machine learning approaches treat transactions independently and fail to capture the intricate network structures inherent in money laundering schemes. To address these limitations, we propose HeteroGCL, a heterogeneous graph contrastive learning framework for scalable and sustainable cryptocurrency AML. Our approach models cryptocurrency transactions as a heterogeneous graph with multiple node and edge types and integrates a heterogeneous graph attention network with a graph contrastive learning module. By leveraging unlabeled data through topology-aware and attribute-aware graph augmentations, HeteroGCL mitigates label scarcity while enabling scalable and label-efficient AML model training while reducing reliance on costly manual annotation. Extensive experiments on the Elliptic dataset demonstrate that HeteroGCL achieves superior performance over state-of-the-art baselines, achieving an F1-score of 0.824 and an AUC of 0.912, with a 4.7% improvement in F1-score compared to the CARE-GNN baseline. The results indicate that the proposed framework effectively captures complex money laundering patterns while supporting scalable deployment of AML systems and improving the economic and operational sustainability of blockchain AML infrastructures. Full article

Sextortion has rapidly expanded into a global cyber-enabled crime that leverages anonymous digital communication and decentralized payment systems. This study examines the financial infrastructures underlying contemporary sextortion by conducting a two-phase analysis of 87 confirmed cases involving cryptocurrency payments. Using blockchain forensic tools and open-source intelligence, the research traces fund movements across perpetrator-controlled wallets, identifies laundering techniques such as mixers, peel-chain transfers, and exchange-based cash-outs, and links these behaviors to narrative patterns within victim reports. The results reveal a dual-tier ecosystem in which mass-produced, multilingual extortion scripts coexist with divergent laundering typologies that differentiate lower-value, high-volume scams from more organized and higher-yield operations. By integrating qualitative and quantitative evidence, this study provides a forensic framework for detecting illicit cryptocurrency activity, improving threat classification, and strengthening investigative and regulatory responses to sextortion and related crypto-enabled interpersonal crimes. Full article

The influence of political stability on financial crime remains a subject of ongoing debate. While stability is often associated with policy continuity, regulatory credibility, and more effective enforcement, an alternative view suggests that unstable democracies may outperform stable autocracies in curbing financial crime. Using panel data from 158 countries over the period 2012–2023, this study finds that political stability is associated with lower money laundering (ML) risk, even after controlling for the extent of democratic governance. With respect to moderating factors, democracy independently lowers ML risk, but its interaction with political stability is limited, suggesting that stability constrains illicit financial activity largely irrespective of regime type. Economic development emerges as a more decisive moderator: in high-income countries, political stability translates into more credible AML enforcement, whereas in low-income settings, its impact is constrained by weaker institutional capacity. Legal origin exhibits weaker moderating effects, with political stability reducing ML risk across both common law and civil law systems. Overall, the findings highlight political stability as a key institutional determinant of structural Anti-Money Laundering (AML) vulnerability, underscoring the importance of strengthening governmental capacity to enhance the effectiveness of anti-money laundering frameworks. Full article

Financial fraud has expanded rapidly with the growth of the digital economy, evolving from conventional transactional misconduct to more complex and data-intensive forms. Traditional rule-based detection methods are increasingly inadequate for addressing the scale, heterogeneity, and dynamic behavior of modern fraud. In this context, artificial intelligence (AI) has become a core tool in financial fraud detection research. This review systematically surveys AI-based financial fraud detection studies published between 2015 and 2025. It summarizes representative machine learning and deep learning approaches, including tree-based models, neural networks, and graph-based methods, and examines their applications in major fraud scenarios such as credit card fraud, loan fraud, and anti-money laundering. In addition, emerging research on cryptocurrency- and blockchain-related fraud is reviewed, highlighting the distinct challenges posed by decentralized transaction environments. Through a comparative analysis of methods, datasets, and evaluation practices, this review identifies persistent issues in the literature, including severe class imbalance, concept drift, limited access to labeled data, and trade-offs between detection performance and interpretability. Based on these findings, the paper discusses practical considerations for applied fraud detection systems and outlines future research directions from a data-centric and application-oriented perspective. This review aims to provide a structured reference for researchers and practitioners working on real-world financial fraud detection problems. Full article

Transnational organized criminal groups operate in ways that are resistant to prosecution. In response, a proposed change in domestic law would aim to ensnare front men who serve to hide the identities of criminals, enabling prosecutors to flip them via cooperation agreements, thereby unraveling transnational criminal schemes. The proposal would require the ultimate beneficial sender, and ultimate beneficial recipient, to certify beneficial ownership (as sender and recipient) when funds are transacted in amounts exceeding USD 3000; and would require foreign financial institution personnel, who handle transactions with a nexus to a party to the Rule, to collect and submit, to a central law enforcement database, certifications by the ultimate beneficial sender and recipient of covered funds that are deposited, transmitted, transferred, or paid. Analysis of the proposed change in law indicates that it may be effective in addressing the impunity that prevails when organized criminal groups operate in China and North Korea in ways that appear to be outside the reach of domestic U.S. law enforcement authorities. Full article

The rise in Blockchain-based digital assets has transformed the financial ecosystems, which has also created complex governance and taxation challenges. The pseudonymous and cross-border nature of crypto transactions undermines traditional tax enforcement, leaving regulators such as the South African Revenue Service (SARS) reliant on voluntary disclosures with limited verification mechanisms, while existing Blockchain forensic tools and regulatory technologies (RegTechs) have advanced in anti-money laundering and institutional compliance, their integration into issues related to taxpayer compliance and locally adapted solutions remains underdeveloped. Therefore, this study conducts a state-of-the-art review of Blockchain forensics, RegTech innovations, and crypto tax frameworks to identify gaps in the crypto tax compliance space. Then, this study builds on these insights and proposes a conceptual model that integrates digital forensics, cost basis automation aligned with SARS rules, wallet interaction mapping, and non-fungible tokens (NFTs) as verifiable audit anchors. The contributions of this study are threefold: theoretically, which reconceptualise the adoption of Blockchain forensics as a proactive compliance mechanism; practically, it conceptualises a locally adapted proof-of-concept for diverse transaction types, including DeFi and NFTs; and lastly, innovatively, which introduces NFTs to enhance auditability, trust, and transparency in digital tax compliance. Full article

Money laundering poses a serious threat to financial stability and requires effective national frameworks for prevention. This study investigates how the quality of legal and institutional frameworks affects the effectiveness of national anti-money laundering (AML) systems and their implications for financial risk management. We conducted an empirical analysis of 132 jurisdictions in 2024 using the Basel AML Index (AMLI) and the WJP Rule of Law Index (RLI). The Random Forest method was employed to model the relationship between rule-of-law indicators and AML risk levels. Findings reveal a significant inverse relationship between rule-of-law indicators and AML risk levels, with an overall classification accuracy of 69.6%. The model performed best for low-risk countries (precision 75%, recall 92.31%), moderately for medium-risk countries (precision 65.22%, recall 78.95%), but failed to identify high-risk jurisdictions, suggesting a legal institutional “threshold” necessary for effective AML functioning. Key predictors included protection of fundamental rights and mechanisms for civil oversight, with strong negative correlations between AML risk and criminal justice impartiality (−0.35), civil justice fairness (−0.35), and equality before the law (−0.41). These results show that legal factors strongly affect AML risk and can guide regulators in improving risk-based standards, enhancing regulatory certainty, and managing financial risk. Full article

The widespread adoption of cryptocurrencies has transformed the financial landscape by enabling swift, decentralised transactions. However, the pseudonymous nature of digital currencies has also fuelled illicit activities, such as money laundering. Criminals perform money laundering to access illicitly acquired funds without detection and convert illegally obtained assets into untraceable commodities, seamlessly integrated into the financial system. Although new regulatory measures have been introduced, illicit actors continue to exploit various methods, from peer-to-peer exchanges to cryptocurrency mixing services, to obscure the origins of illegal funds. This study presents a parametric analysis of these methods, examining dimensions such as duration, number of actors, contextual requirements, operational difficulty, traceability, and costs across each stage of the money laundering process: placement, layering, and integration. The analysis indicates that, while more sophisticated techniques may provide a higher degree of anonymity, they simultaneously require specialised technical expertise and meticulous planning. Consequently, there is a trade-off between the level of privacy attainable and the operational complexity inherent to each method. By systematically comparing these strategies, this analysis aims to contribute to a deeper understanding of cryptocurrency-based money laundering techniques, providing insight for more effective prevention and mitigation measures for both regulatory authorities and the financial sector. Full article

As Albania aspires to join the EU by 2030, harmonisation of existing and future legislation and ensuring proper implementation remain the main priorities. Several working groups have been established to deal with harmonisation and enforcement. Although scepticism about Albania’s 2030 membership exists among Albanian scholars and politicians about whether public administration can address this daunting task, Albanian citizens are hopeful about finally joining the EU. This paper analyses the extent to which Albanian legislation on the prevention of money laundering and financing of terrorism aligns with the Anti-Money Laundering Directives and how it is enforced. Using both traditional legal and comparative methodologies, this paper compares whether the Albanian anti-money laundering and countering the financing of terrorism law aligns with the Anti-Money Laundering regime and assesses the level of enforcement of harmonised legislation. This paper concludes that, although the Albanian Law on anti-money laundering and terrorist financing largely aligns with the AML/FT Directive, proper implementation remains a challenge due to limited enforcement capacities, weak legal structures, and an essentially cash-based economy with a substantial informal economy. Full article

Within the structure of organised criminal groups (OCGs), women were traditionally relegated to peripheral and support roles (e.g., mothers and partners), with men primarily engaged in serious forms of criminality. However, more recent research has highlighted the varied roles women occupy within OCGs, including as traffickers, recruiters, and strategic advisors. Within this growing field of research, the present study sought to explore the role of female offenders in OCGs within Australia through a gynocentric and intersectional lens. Drawing on a content analysis of 84 court judgement transcripts involving convicted female offenders between 2010 and 2024, this study centres women’s experiences within OCGs. The findings reveal that women occupy a spectrum of roles across a range of offences such as drug trafficking, money laundering, and fraud. Many offenders had histories of trauma, mental illness, and economic precarity, reflecting structural inequalities that shape pathways into criminality. The findings provide a preliminary understanding of female involvement within OCGs in Australia, including relational, survival-based, professional, entrepreneurial, and subordinate offenders. This study affirms the need for a gender-sensitive criminological framework that accounts for agency, coercion, and structural constraint. By focusing on the experience of women, the study contributes to a growing body of literature seeking to highlight the complexity and centrality of women’s roles within OCGs, while providing the groundwork for future studies. Full article

Financial fraud represents a critical global challenge with substantial economic and social consequences. This comprehensive review synthesizes the current knowledge on machine learning approaches for financial fraud detection, examining their effectiveness across diverse fraud scenarios. We analyze various fraud types, including credit card fraud, financial statement fraud, insurance fraud, and money laundering, along with their specific detection challenges. The review outlines supervised, unsupervised, and hybrid learning approaches, discussing their applications and performance in different fraud detection contexts. We examine commonly used datasets in fraud detection research and evaluate performance metrics for assessing these systems. The review is further grounded by two case studies applying supervised models to real-world banking data, illustrating the practical challenges of implementing fraud detection systems in operational environments. Through our analysis of the recent literature, we identify persistent challenges, including data imbalance, concept drift, and privacy concerns, while highlighting the emerging trends in deep learning and ensemble methods. This review provides valuable insights for researchers, financial institutions, and practitioners working to develop more effective, adaptive, and interpretable fraud detection systems capable of operating within real-world financial environments. Full article