Search Results (31)

Purpose—This study examines the interaction of economic openness, governance, and money laundering. The paper’s main objective is to analyze how trade openness, foreign direct investment, and anti-corruption measures influence the risk of money laundering in specific economic blocs. Design/methodology/approach—This study analyzes these economic blocs (EU, G20, BRICS, and CIVETS) using annual data from the Basel Institute on Governance and World Bank statistics for 2012–2021. A panel-corrected standard errors (PCSE) estimator is employed to examine the relationships among the variables, accounting for cross-sectional dependence and ensuring robust parameter estimation. The corruption control index is a proxy for governance effectiveness, though it does not directly measure regulatory strength. Future research should incorporate more specific variables to evaluate the regulatory impact. Findings—This study reveals significant variations in money laundering risks by a country’s income category and economic bloc influenced by economic openness and governance structures. Economic growth and foreign direct investment (FDI) inflows exhibit contrasting effects on money-laundering risks; they tend to exacerbate risks in middle-income countries, while high-income nations demonstrated a lower risk of money laundering, likely due to more robust governance structures. Trade openness and anti-corruption measures generally reduced risks in wealthier countries, highlighting the importance of strong governance frameworks. These insights suggest that anti-money-laundering policies should be tailored to fit different regions’ unique economic and institutional contexts for enhanced effectiveness. Originality—This study employs a structured approach to analyzing a decade of panel data from key economic blocs, providing insights into the intricate relationships between governance, economic openness, and money laundering risks. Bridging the gap between theoretical research and practical, actionable strategies serves as a valuable resource for improving the effectiveness of anti-money-laundering (AML) measures on a global scale. Full article

Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight MVGNN module, which performs two-way message passing between nodes in a transaction graph. Additionally, LineMVGNN incorporates a line graph view of the original transaction graph to enhance the propagation of transaction information. We conduct experiments on two real-world account-based transaction datasets: the Ethereum phishing transaction network dataset and a financial payment transaction dataset from one of our industry partners. The results show that our proposed method outperforms state-of-the-art methods, reflecting the effectiveness of money laundering detection with line-graph-assisted multi-view graph learning. We also discuss scalability, adversarial robustness, and regulatory considerations of our proposed method. Full article

Current Environmental, Social, and Governance (ESG) indices are flawed because the data are incomplete and not reported consistently, and some measured factors may be irrelevant to the industry. Regulators in the financial services industry emphasize reporting CO₂ emissions (environmental factor), yet the key resources leveraged for production are rented offices, and internet–governance issues like money laundering, corruption, and unethical behavior would be more relevant. To investigate this problem, we sampled the finance and insurance industry firms in the USA with the greatest economic impact, i.e., those managing at least USD 1 trillion in assets. We used artificial intelligence to collect data about undisclosed legal decisions against firms to measure the ESG anti-corruption governance factor GRI 206-1, defined by the Global Reporting Institute (GRI) for global sustainable development goals (SDGs), which correspond to the United Nations’ SDGs. We applied Bayesian correlation with bootstrapping to test our hypotheses, followed by root cause analysis. We found that ESG ratings from providers did not reflect legal cases decided against firms; the Bayesian BF₊₀ odds ratio was 3005 (99% confidence intervals were 0.617, 0.965). Also, misconduct fines and arbitration legal case counts were significantly related for the same firm (the Vovk-Selke maximum p-ratio was 4411), but most ESG scores were significantly different for the same firm. We found three other studies in the literature that corroborated some of our findings that specific firms in our sample were considered to be unethical. We propose deeper study of the implications related to our findings based on public interest and stakeholder theory. Full article

This study employs a bibliometric analysis of emerging trends in money laundering for terrorism financing (ML/TF) to provide direction for future research. The authors used VOSviewer and analyzed 2577 published documents retrieved from the SCOPUS database using the PRISMA methodology. The findings reveal a growing research interest in understanding the complex interplay between money laundering and terrorism financing. This research emphasizes the significance of ML/TF for economic stability, as understanding terrorism financing mechanisms allows authorities to trace and block funds going to terrorist groups, which is crucial for national security. Critical insights for policymakers underscore the need for robust legislative frameworks, effective Financial Intelligence Units (FIUs), and international collaboration to combat these global threats. This analysis offers a foundation for future research, mapping the evolving knowledge landscape in ML/TF. Full article

This study examines factors influencing Ghanaian banks’ compliance with anti-money laundering (AML) legislation. Drawing upon institutional, compliance, and dynamic capability theories, the study identifies the interplay of organisational, regulatory, and employee factors influencing compliance outcomes. A mixed methods approach was used to collect data from 23 universal banks, 9 local and 14 foreign, in Ghana, focusing on experienced managers and employees in risk, legal, operations, compliance, and business development departments. The findings show that employee characteristics like due diligence and moral involvement have a positive relationship with compliance with AML regulations; however, contrary to expectations, effective AML/CFT programs did not significantly impact banks’ adherence to these regulations. The association between moral engagement, an innovative culture, and AML compliance is weakened by normative power and an innovative culture acting as negative moderators. This study contributes empirical evidence to the literature on AML compliance in emerging markets and offers practical implications for policymakers, regulators, and banking professionals seeking to boost regulatory effectiveness and mitigate financial crime risks. This study provides a foundation for targeted interventions and strategic initiatives aimed at strengthening the AML regulatory landscape in Ghana and other countries. Full article

Money laundering is an illicit activity that seeks to conceal the nature and origins of criminal proceeds, posing a substantial threat to the national economy, the political order, and social stability. To scientifically and reasonably predict money laundering risks, this paper focuses on the “layering” stage of the money laundering process in the field of supervised learning for money laundering fraud prediction. A money laundering and fraud prediction model based on deep learning, referred to as MDGC-LSTM, is proposed. The model combines the use of a dynamic graph convolutional network (MDGC) and a long short-term memory (LSTM) network to efficiently identify illegal money laundering activities within financial transactions. MDGC-LSTM constructs dynamic graph snapshots with symmetrical spatiotemporal structures based on transaction information, representing transaction nodes and currency flows as graph nodes and edges, respectively, and effectively captures the relationships between temporal and spatial structures, thus achieving the dynamic prediction of fraudulent transactions. The experimental results demonstrate that compared with traditional algorithms and other deep learning models, MDGC-LSTM achieves significant advantages in comprehensive spatiotemporal feature modeling. Specifically, based on the Elliptic dataset, MDGC-LSTM improves the Macro-F1 score by 0.25 compared to that of the anti-money laundering fraud prediction model currently considered optimal. Full article

The volume of the asset investment and trading market can be expanded through the issuance and management of blockchain-based security tokens that logically divide the value of assets and guarantee ownership. This paper proposes a service model to solve a problem with the existing investment service model, identifies security threats to the service model, and specifies security requirements countering the identified security threats for privacy protection and anti-money laundering (AML) involving security tokens. The identified security threats and specified security requirements should be taken into consideration when implementing the proposed service model. The proposed service model allows users to invest in tokenized tangible and intangible assets and trade in blockchain-based security tokens. This paper discusses considerations to prevent excessive regulation and market monopoly in the issuance of and trading in security tokens when implementing the proposed service model and concludes with future works. Full article

Since 2012, researchers have explored various factors influencing Bitcoin prices. Up until the end of July 2023, more than 9100 research papers on cryptocurrencies were published and indexed in the Web of Science Clarivate platform. The objective of this paper is to analyze the impact of publications on Bitcoin prices. This study aims to uncover significant themes within these research articles, focusing on cryptocurrencies in general and Bitcoin specifically. The research employs latent Dirichlet allocation to identify key topics from the unstructured abstracts. To determine the optimal number of topics, perplexity and topic coherence metrics are calculated. Additionally, the abstracts are processed using BERT-transformers and Word2Vec and their potential to predict Bitcoin prices is assessed. Based on the results, while the research helps in understanding cryptocurrencies, the potential of academic publications to influence Bitcoin prices is not significant, demonstrating a weak connection. In other words, the movements of Bitcoin prices are not influenced by the scientific writing in this specific field. The primary topics emerging from the analysis are the blockchain, market dynamics, transactions, pricing trends, network security, and the mining process. These findings suggest that future research should pay closer attention to issues like the energy demands and environmental impacts of mining, anti-money laundering measures, and behavioral aspects related to cryptocurrencies. Full article

This study utilizes the random forest technique to investigate risk management practices and concerns in Islamic banks using survey data from 2016 to 2021. Findings reveal that larger banks provide more consistent survey responses, driven by their confidence and larger survey budgets. Moreover, a positive link is established between a country’s development, characterized by high GDPs and low inflation and interest rates, and the precision of Islamic banks’ survey responses. Analyzing risk-related concerns, the study notes a significant reduction in credit portfolio risk attributed to improved risk management practices, global economic growth, stricter regulations, and diversified asset portfolios. Concerns related to terrorism financing and cybersecurity risks have also decreased due to the better enforcement of anti-money laundering regulations and investments in cybersecurity infrastructure and education. This research enhances our understanding of risk management in Islamic banks, highlighting the impact of bank size and country development. Additionally, it emphasizes the need for ongoing analysis beyond 2021 to account for potential COVID-19 effects and evolving risk management and regulatory practices in Islamic banking. Full article

The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address–transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others. Full article

This paper deals with ensuring the sustainability of the financial system and combating hybrid threats in relation to anti-money laundering and counter-terrorist financing (AML/CTF) measures. International cooperation in the field of combating hybrid threats is only at the beginning, and in many ways, the experience of international cooperation in the fight against money laundering and terrorist financing, which is based on many years of experience in the institutional and legislative fields, could be used. Hybrid threats are constantly changing and evolving, which means our response to them must also constantly evolve and adapt. The aim of the presented study is the analysis of the problem of the legalization of income from criminal activity and the financing of terrorism and their possible relationship with the fight against hybrid threats and maintaining the stability of the financial system. Full article

The fight against financial crimes has become increasingly challenging, and the need for sophisticated systems that can accurately identify suspicious transactions has become more pressing. The goal of the study is to develop a new feature selection method based on swarm intelligence algorithms to improve the quality of data classification. This article is about the development of an information system for the classification of transactions into legal and suspicious in an anti-money laundering sphere. The system utilizes a swarm-algorithm-based feature selection approach, specifically the ant colony optimization algorithm, which was both used and adapted for this purpose The article also presents the system’s functional–structural diagram and feature selection algorithm flowchart. The proposed feature selection method can be used to classify data from various subject areas. Full article

The digital revolution has launched myriad new technologies in the field of art and cultural heritage law, including digital art, NFTs (non-fungible tokens), artificial intelligence (AI)-generated art, virtual reality and reality augmentation, online viewing rooms and auctions, holograms, immersive experiences, and more. As a $67.8 billion industry, the art market is a global driver of innovation, international collaboration, and national economies, given its cross-border transactions. However, given the extremely rapid development of these new technologies, regulators have struggled to keep pace and implement legal measures that are fit for purpose in this field. Limited oversight has resulted in several claims that have the potential to change the legal landscape. For instance, claims over the theft/misappropriation of NFTs and the related fraud and money laundering that may ensue, as well as a recent class action copyright infringement suit against the creators of a popular AI algorithm and infringement claims over immersive installation and light technologies, demonstrate how new ways of thinking are required to assess cases involving digital property (distinguished from other types of non-tangible property). Moreover, the US Supreme Court has issued a landmark ruling on fair use within the copyright context, which will be relied upon in the future to determine whether (and to what extent) the appropriation of existing copyrighted material is permitted. This includes both the digital use of physical artworks and the use of born-digital works. Although jurisprudential decisions are made on a case-by-case basis, factual patterns involving online media, digital art, and related technologies could serve as guidance for legislators and other decision-makers when considering what limits should be imposed on Web 3.0. This article will focus on recent US-based claims and regulations and dovetail with existing art market regulations in this jurisdiction (e.g., anti-money-laundering statutes) to determine their impact on new technologies, whether directly or indirectly. Finally, the article highlights ongoing trends and preoccupations to provide an overview of the shifting legal landscape. Full article

Digital credit has gained much attention from academic researchers, practitioners, and policymakers worldwide. This study empirically evaluates the determinants of digital credit using cross-country data from 2013 to 2019. The conventional ordinary least square regression with fixed effects estimator is used to investigate the factors affecting the growth of digital credit. Our study highlights that the regulatory frameworks of anti-money laundering and terrorist financing, the economy’s innovative capacity, and financial development are significant factors affecting the development of digital credit, especially fintech credit. However, the findings indicate that only the innovation capacity is more critical to the expansion of bigtech credit. Nonetheless, our results provide some important implications for market participants and the authorities in promoting digital credit. Accordingly, this study contributes to the literature on the growth of digital credit when considering the critical roles of money laundering and terrorist financing frameworks and innovation capacity. Full article

The decentralization and anonymity of blockchain have attracted significant attention. However, in recent years, there has been a rise in blockchain money laundering incidents, and anti-money laundering efforts have become crucial within the blockchain space. Blockchain money laundering differs from traditional financial money laundering as it does not provide account information, particularly in the case of Bitcoin. This absence of information makes it challenging for researchers to detect money laundering activities based on transaction data. We propose LB-GLAT, a novel Long-Term Bi-Graph Layer Attention Convolutional Network, to effectively capture the topological structure and attribute characteristics of money laundering on the blockchain transaction graph. LB-GLAT utilizes the transaction graph and the reverse transaction graph to solve the no-loop problem that results in the inability to capture the destination of blockchain transactions and designs a long-term layer attention mechanism to alleviate the over-smoothing problem. We implemented a series of experiments to evaluate LB-GLAT, which achieved state-of-art performance compared with other methods, presenting an accuracy of 0.9776, a precision of 0.9317, a recall of 0.8494, an F1−score of 0.8887, and an AUC of 0.9806. Full article