This paper proposes a dynamic, decentralized social trust model to meet Know Your Customer (KYC) and Anti-Money Laundering (AML) compliance requirements, especially for individuals lacking formal documentation. Building on previous work, we present a conceptual model for dynamically computing trust and risk values to support financial transaction approvals through innovative recognition schemes. Agent-based simulations across four scenarios demonstrate that dynamic trust mechanisms enhance transaction approval rates and payment volumes, provided malicious agents are controlled. The model is flexible, adaptable to diverse populations, and promotes financial inclusion without compromising compliance requirements. It also offers a foundation for future research on mitigating risks from malicious actors. Furthermore, it is particularly suited for enabling compliant and inclusive decentralized finance (DeFi) applications.
Dramatic price swings and the possibility of extreme returns have made Bitcoin a hot topic of interest for investors and researchers alike. With the help of advanced neural network models including CNN, RCNN, and LSTM networks, this paper has delved deep into the intricacies of Bitcoin price behavior. We will study different time intervals—close-to-close, close-to-open, open-to-close, and day-to-day—to find a pattern that we can use to develop an investment strategy. The average volatility over a year, six months, and three months is compared with the predictive power of volatility versus a traditional buy-and-hold strategy. Our findings point out the strengths and weaknesses of each neural network model and provide useful insights into optimizing cryptocurrency portfolios. This study contributes to the literature on the price prediction and volatility analysis of cryptocurrencies, thus providing useful information to both researchers and investors to execute strategic steps within the volatile cryptocurrency market.
The use of inside information has produced greater controversy. Unlike insider trading, market manipulation is a very broad category of conduct extending beyond securities to other markets such as commodities, foreign exchange, etc. Artificial intelligence (AI) algorithmic trading exposes markets to new and emerging risks, embracing novel types of market manipulation. Moreover, insider trading, wash trading, and price manipulation are utilized in the Non-Fungible Tokens (NFT) market. Money laundering undercuts the integrity of financial institutions and undermines control over national economic policies. AI assists financial compliance, which means that automated systems scrutinize transactions for their compliance with the laws of Know Your Customer (KYC) and Anti-Money Laundering (AML).
This article examines how the design features of retail central bank digital currencies (CBDCs) influence the detection and prevention of crypto-enabled money laundering. Drawing on a comparative analysis of Russia, the European Union, the United States, and Malta, it evaluates the effectiveness of CBDC-integrated anti-money laundering (AML) mechanisms in addressing the three key stages of illicit finance: placement, layering, and integration. Using primary sources, including pilot program data, legislative texts, and policy consultations, alongside secondary academic literature, the Study explores how design elements such as ledger visibility, programmable transaction limits, sanctions screening, and tiered privacy structures can be embedded into CBDC infrastructure. The findings reveal significant variation in enforcement capacity, privacy protection, and governance transparency across jurisdictions, shaped by political economy, legal traditions, and technological architectures. The article argues that while CBDCs offer unprecedented opportunities to embed compliance at the core of payment systems, their legitimacy and adoption depend on the careful balancing of enforcement effectiveness with constitutional safeguards, civil liberties, and public trust. Policy recommendations emphasize jurisdiction-specific typology mapping, programmable safeguards, stakeholder engagement, tiered anonymity, cross-border interoperability, and independent oversight. The analysis concludes that CBDCs, if responsibly designed, can modernize AML frameworks and strengthen financial integrity without undermining democratic principles.
This study delves into the differences between traditional financial markets, as proxied by their corresponding future contracts, and the cryptocurrency market, focusing on Bitcoin, during major global events: the COVID-19 pandemic, the Russia-Ukraine war, and the Israel–Palestine conflict. It reveals Bitcoin’s increased trading volume post-COVID-19, highlighting its appeal as a digital safe haven. This trend persists during subsequent crises, suggesting a strategic shift towards cryptocurrencies as diversification tools. Despite volume fluctuations, Bitcoin’s price stability reflects investor confidence in its long-term viability. The significant change in EuroStoxx 50 returns during the Israel–Palestine conflict, highlights localized geopolitical influences on markets. The study underscores the importance of considering both global and regional factors in investment decisions. It emphasizes cryptocurrencies’ growing significance in the global financial market, particularly during crises, and suggests further exploration into investor behavior and regulatory effects. Understanding these dynamics is crucial for navigating the evolving financial landscape.
ABSTRACT This study aims at bridging critical gaps in the existing cryptocurrency research by exploring combinations of technological, macroeconomic and behavioural factors, namely, economic agents' expectations and the size of influence that each of them has on the Bitcoin price movements. In contrast to the existing studies that focused on individual determinants and estimated aggregate effects thereof, in this study, fuzzy‐set qualitative comparative analysis (fsQCA) is applied to determine configurations of drivers to determine the Bitcoin price and used necessary condition analysis (NCA) to quantify the magnitude of the effects using the monthly data between 2011 and 2022. Findings show that economic agents' expectations such as OECD's Business Confidence Index, Consumer Confidence Index and Composite Leading Indicator emerge as influential variables of Bitcoin, surpassing traditional drivers like Gold and Financial Stress Index. Among these, Business Confidence Index and Composite Leading Indicator exhibit a very large effect on Bitcoin prices, and from the technology variable group, Average Block Size exhibits a very large effect on Bitcoin prices. fsQCA indicates that nine distinct configurations contribute to high Bitcoin prices and eight configurations lead to low Bitcoin prices, thus depicting equifinality in Bitcoin price determination. These insights can provide policymakers and investors with a better understanding of the Bitcoin price dynamic by finding out necessary variables and equifinal pathways towards either high or low prices, thus promoting better risk management activities, as well as regulatory approaches to this highly dynamic asset class.
The rise in illicit financial activities across the South Africa–Zimbabwe corridor, with an estimated annual loss of $3.1 billion demands advanced AI solutions to augment traditional detection methods. This study introduces FALCON, a groundbreaking hybrid transformer–GNN model that integrates temporal transaction analysis (TimeGAN) and graph-based entity mapping (GraphSAGE) to detect illicit financial flows with unprecedented precision. By leveraging data from South Africa’s FIC, Zimbabwe’s RBZ, and SWIFT, FALCON achieved 98.7%, surpassing Random Forest (72.1%) and human auditors (64.5%), while reducing false positives to 1.2% (AUC-ROC: 0.992). Tested on 1.8 million transactions, including falsified CTRs, STRs, and Ethereum blockchain data, FALCON uncovered $450 million laundered by 23 shell companies with a cross-border detection precision of 94%, directly mitigating illicit financial flows in Southern Africa. For regulators, FALCON met FAFT standards, yielding 92% court admissibility, and its GDPR-compliant design (ε = 1.2 differential privacy) met stringent legal standards. Deployed on AWS Graviton3, FALCON processed 2 million transactions/second at $0.002 per 1000 transactions, demonstrating real-time scalability, making it cost-effective for financial institutions in emerging markets. As the first AI framework tailored for Southern Africa’s financial ecosystems, FALCON sets a new benchmark for ethical AML solutions in emerging economies with immediate applicability to CBDC supervision. The transparent validation of publicly available data underscores its potential to transform global financial crime detection.
Jeffrey Chu, Stephen Chan, Yuanyuan Zhang, Nicholas Lord
This study examines the short-term impact of the Russia-Ukraine war on the high frequency digital<br/>asset markets. We apply an event study approach, focusing on the initial months of the war and<br/>analyse hourly returns of cryptocurrencies, DeFi tokens, and metaverse tokens. We find that<br/>negative war-related events have both an immediate and sustained impact on cryptocurrencies<br/>and DeFi tokens, likely due to a series of negative events leading to positive returns. In contrast to<br/>stocks and commodities like gold, cryptocurrencies and DeFi tokens exhibit positive and significant<br/>cumulative returns following negative war-related events. This suggests that these assets could<br/>serve as diversifiers or hedges against such events, similar to the ‘political property’ observed for<br/>oil. Importantly, these findings provide preliminary insights into the ongoing Russia-Ukraine<br/>conflict and help to understand the impact of military conflict on cryptocurrency markets more<br/>broadly.
This guest editorial argues for renewed intellectual engagement between anthropology and economics, two disciplines that became estranged during 20th‐century debates over human motivation and cooperation. With anthropologists largely rejecting economic theories following the formalist‐substantivist controversy, this ‘disciplinary divorce’ has impoverished anthropological analysis by limiting available theoretical tools. Contemporary anthropologists often mischaracterize economic arguments – particularly regarding barter theory – while remaining unaware of insights from heterodox economic schools that increasingly draw on anthropological methods and findings. Both disciplines share a fundamental concern with developing a general theory of value, making collaboration essential. The institution of money serves as an especially productive site for such interdisciplinary dialogue, functioning simultaneously as a social institution and technology that addresses the coordination problems inherent in complex societies. Bitcoin's emergence as the first natively digital, nonstate medium of exchange presents an unprecedented opportunity to examine how monetary institutions evolve and impact social relationships. By moving beyond disciplinary boundaries and engaging seriously with economic theory, anthropologists can contribute to a more comprehensive understanding of value across human societies while advancing both fields’ shared intellectual project of explaining social organization and cultural change.
The financial world is at the crossroads, and digital monies, decentralized privacy, and asset tokens recreate centuries-old constructs. Blockchain options are challenging conventional clearing houses as never before, by operating outside of the set parameters. This article examines the complex interaction of old-world clearing systems with new-fangled, crypto settlement mechanisms, deconstructs prickly issues and precious opportunities facing Central Counterparty Clearing Houses. The cryptocurrency environment has developed different settlement methods, but advanced investors are eager to have safe and regulated access to digital assets. Its essence is that blockchain promises to render bypassing middlemen through direct transactions a reality, but, in the meantime, it poses a threat to current systems and presents a new way to envision clearing. This article shows how new clearing corporations can help solve the problem of finance, and even support better market performance and transparency along with stability alongside key protections because innovative hybrid enterprise models can actually become a bridge between old-fashioned finance and digital networks and even increase their reliability, integrity, and stability in the long-term future.
Ben Wang, Yanxiang Tong, Shunhui Ji, Hai Dong · 6 authors
With the rapid development of blockchain technology, smart contract applications have become increasingly widespread. However, vulnerabilities in contracts may be exploited by attackers, causing serious financial losses. In recent years, learning-based approaches have gained prominence for their accuracy and efficiency by automatically extracting explicit syntactic or semantic features from a large number of smart contracts with minimal manual intervention. In this article, we conduct a comprehensive analysis and ultimately select 61 scientific publications to provide researchers, especially beginners, with a comprehensive understanding of the learning-based detection process and guidance on selecting appropriate code representations. We first introduce common types of vulnerabilities, detail uncovered vulnerabilities, and summarize datasets used in learning-based methods. Then, we elaborate on the general process of learning-based detection and classify existing publications based on code representations, including sequence, tree, graph, and mixed features. Finally, we summarize the progress of existing work and explore future research directions in this field.
Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the Trump (2017–2020) and Biden (2021–2024) governments. The 48-week return forecast of the Bitcoin–Gold correlation was also conducted by using the Bayesian Structural Time Series (BSTS) model. Results indicate that Bitcoin was the most volatile asset, while the U.S. Dollar remained the least volatile under both regimes. Under Trump, U.S. Dollar significantly influenced Oil and Bitcoin while Bitcoin and Gold were negatively linked to Oil and positively associated with U.S. Dollar. An inverse relationship between Bitcoin and Gold also emerged. Under Biden, Bitcoin, Gold, and U.S. Dollar all significantly affected Oil with Bitcoin showing a positive impact. Bitcoin and Gold remained negatively correlated though not significantly, and the Dollar maintained positive ties with both. Forecasts show a positive link between Bitcoin and Gold in the coming year. However, Bitcoin does not exhibit consistent characteristics of a safe-haven asset during the U.S. presidential transitions examined, largely due to its high volatility and unstable correlations with a traditional safe-haven asset, Gold. This study contributes to the understanding of shifting relationships between digital and traditional assets across political regimes.
Ahmed Alteneiji, Khaled Shaalan, Suleiman Y. Yerima, Usman Butt
Blockchain networks are decentralized and offer pseudonymity, thus making illegal operations in Bitcoin very difficult to detect and stop. This work reviews recent machine learning approaches designed to spot these activities, focusing on the technical obstacles associated with class imbalance, unlabeled data, and how money laundering methods are rapidly becoming more advanced. Over a decade, reports from 2015 to 2025 were studied to analyze approaches that applied supervised learning, unsupervised clustering, and graph-based neural networks both individually and in mixed hybrid configurations. Important developments in related work are sophisticated blockchain data features, using ensembles to enhance performance, and systems for real-time automatic data processing. Analysis found that models using graph attention achieve over 40% improvement in performance compared to rule-based systems for finding unlawful activity. It further investigates methods for future privacy-preserving analytics, uncovering cross-chain criminals, and making regulations more compatible in the world of decentralized finance, to build better Anti-Money Laundering frameworks.
Kostiantyn Orobets, V. I. Shkolnikov, Tetiana Batrachenko, Тетяна Василівна Барановська · 5 authors
Introduction: The legal regime of cryptocurrency in different countries of the world is heterogeneous. In some, it is not defined at all, which leads to legal conflicts, including when qualifying crimes committed with cryptocurrency use. The situation is further complicated because such crimes can occur in the territories of several states where cryptocurrency has a different legal regime. Traditional legislation and mechanisms for combating money laundering and terrorist financing are practically ineffective in the landscape of crimes involving the use of cryptocurrency.Objectives: The aim of the study is to systematise the main patterns of crimes related to the use of cryptocurrency, as well as analyse existing vectors of their legal assessment, appropriate design and application of effective methods of combating these crimes.Methods: Based on the methods of analysis and synthesis, qualitative data analysis, using content analysis as the primary research tool, it is shown that the main problem in preventing the use of cryptocurrency in predicate crimes lies in the technical difficulty of identifying a person or group of persons who carry out cryptocurrency transactions for illegal purposes. Such goals may be aimed at legalising funds, i.e., concealing their illegal origin, making payments in a hidden network, organising various fraudulent schemes, financing terrorism, and other crimes.Results: The article argues that given the technical specifics of cryptocurrency transactions and the technical capabilities of "masking" the origin of cryptocurrency funds, it is necessary to develop methods for studying trace formation and develop an algorithm for establishing and consolidating forensically significant information for this type of crime. The results indicate that the future of law enforcement in the fight against cryptocurrency-related crime will require a multifaceted approach. Agencies must adopt a proactive approach by foreseeing emerging criminal strategies. To protect the public from crimes using digital assets, law enforcement must be flexible, progressive, and technologically savvy as cryptocurrencies continue to develop. The development of provisions on cryptocurrency also determines the theoretical significance of the work as an object and means of committing crimes, a surrogate means of payment during the commission of certain crimes.Conclusions: The practical significance of the work lies in the possibility of using its results to solve problems arising in the law-making activities of state authorities and law enforcement activities, as well as in developing recommendations for improving criminal legislation in the field of cryptocurrency-related crimes.
This paper addresses the problem of detecting money laundering in the Bitcoin network. Money laundering is the process of handling the proceeds of crime to conceal their illegal source, these illicit transactions have complex features, similar to those of legal transactions. It is well known that transactions can be represented as topological graph structure data, and many GCN-based methods have been developed for Anti-Money Laundering (AML) tasks. However, existing methods have not performed as well in dynamically assigning weights to neighboring nodes and extracting information from global nodes in the Bitcoin network. Therefore, we identify three major challenges: Firstly, GCNs can be misled by concealed illegal transactions due to uniform node representation weights. Secondly, current node-level GCNs cannot handle varied methods of concealing illegal transactions because they fail to extract global information. Thirdly, the costliness of data labelling necessitates the effective use of limited but rich domain-specific labelled data. To address these challenges, we propose the Transformer-enhanced Graph Attention Network (TFGAT) with a Global-Local Attention Mechanism (GLATM) that uses Transformers to extract global information and selectively focus on local information from connected nodes. Due to the limited availability of labelled data from expensive data labelling processes, we introduce a Deep Cyclic Pseudo-Label Updating Mechanism (DCPLU) to enhance data distribution and model robustness, which does not rely on manifold structure or Euclidean distance assumptions. DCPLU can enhance model performance while preserving the model's existing parameters, enabling it to maintain its current faster response time in the application scenario. Experimental results show that our methods outperform existing models across various metrics.
El Salvador's adoption of Bitcoin as legal tender in 2021 represented a bold but ultimately flawed experiment in national cryptocurrency integration.This paper critically examines the key challenges that led to the policy's failure, including public rejection, technological deficiencies, market volatility, and international financial pressure.Despite government incentives, Bitcoin adoption remained minimal, with security issues in the Chivo wallet further eroding trust.The collapse of cryptocurrency markets in 2022 exposed the country's economic vulnerability, forcing El Salvador to scale back its Bitcoin strategy under IMF loan conditions.The findings highlight critical lessons for other nations considering similar policies, emphasizing the need for phased implementation, robust financial infrastructure, and regulatory clarity.Future research should explore alternative digital currency models, particularly Central Bank Digital Currencies (CBDCs), and the socio-economic implications of large-scale cryptocurrency adoption.
The rapid adoption of Non-Fungible Tokens (NFTs) has revolutionised the digital art and collectibles markets. NFTs present novel opportunities for creators and investors alike. However, with such opportunities also comes the risk of money laundering through NFTs. The South African digital art market has not been spared from the rising phenomenon of NFTs. This rising phenomenon has brought with it questions regarding whether the South African anti-money laundering (AML) regime can adequately counter the challenge of money laundering through NFTs. This is particularly so if one considers that, generally, the AML regulatory framework for NFTs is still nascent, not only in South Africa, but also globally. Thus, this contribution comparatively examines the AML regulation of NFTs in South Africa to establish the adequacy and efficacy of the country’s AML regime. The paper concludes that while NFTs are still new, they can be dealt with under the blanket regulation for crypto assets and in specific use cases, AML regulations may be applied to them.
Rug pulls present a critical threat in Decentralized Finance (DeFi), causing substantial financial losses and eroding ecosystem trust. Despite research advances, effective detection remains hampered by fragmented taxonomies, limited datasets, and inadequate tool evaluations. Through systematic analysis of academic and industry sources, we develop a comprehensive taxonomy of 35 distinct rug pull types, including 9 previously undocumented variants. Our analysis reveals significant detection gaps: existing datasets cover only 20% of known types, leading us to create an enhanced dataset of 2,391 instances that increases coverage to 82.9%. Evaluation of 13 detection tools shows substantial capability variation (25.7% to 62.9%), with 9 types completely undetectable. Most critically, tool performance degrades significantly when facing complex attacks, with maximum detection rates dropping from 55.6% for single-vector cases to 31.3% for compound scenarios. These findings provide essential insights for developing more robust security testing approaches for smart contract vulnerabilities in decentralized systems.
In the interconnected world of finance today, fighting money laundering is a paramount issue for institutions globally. This paper presents a new graph-based data architecture to enable multi-institutional pattern identification in anti-money laundering (AML) activities using the capabilities of distributed ledger analytics. With a real-world dataset of transaction records and suspicious activity reports, our solution builds complex networks that uncover hidden connections among distant financial institutions. The architecture facilitates dynamic visualization and anomaly detection on intricate transaction graphs but also leverages the intrinsic security and transparency of distributed ledger technology to provide data integrity and collaborative insights. Through intensive testing, the framework demonstrates improved detection accuracy and reduced false positives, offering an efficient and scalable way for regulators and financial institutions striving to detect and prevent financial crime threats in real time.