According to previous research, cryptocurrency is a driver of money laundering and is associated with several risks (Fletcher, Larkin, & Corbet, 2021; Teichmann & Falker, 2020; Tsuchiya & Hiramoto, 2021). As a result, the purpose of this paper is to concentrate on empirical research in the accounting and finance fields that deal with the impact of cryptocurrencies on the phenomenon of money laundering. To identify relevant literature, we use the following keywords including âcryptocurrency or digital moneyâ and âbitcoin and money launderingâ. We identify 28 research papers published between 2011 and 2021. The findings of the studies that were reviewed emphasized the importance of developing a legal framework for digital currencies. Furthermore, it was revealed that all stakeholders play an important role in lowering the risk of money laundering and illicit activities. The findings highlight the critical role that banks, regulators, and all stakeholders play in reducing money laundering risks. These findings may have policy implications for governments aiming to improve cryptocurrency laws and regulations by enforcing financial security standards and laws and monitoring individualsâ and firmsâ compliance with them. The review identifies some of the literatureâs limitations and suggests future research directions
In recent years, the application of virtual currency has become a part of people's life. The decentralization and anonymity of Bitcoin have made it a favorite tool for many criminals. Therefore, how to trace illegal activities in Bitcoin transactions has become one of the most important research areas. This paper systematically collects 25 research results in this field since 2018, and divides them into three areas, i.e., supervised learning, unsupervised learning, and topological analysis. The supervised learning method based on machine learning is the current mainstream in this research field. However, we believe that the model can achieve more accurate results after combining unsupervised learning and topological analysis features. Moreover, topology analysis can help to observe the entire or specific part of the Bitcoin trading network from a macro perspective so as to discover the hidden illegal activities. In addition, data visualization techniques can provide structural insights to understand the Bitcoin trading network.
Arzu Ăzkan, Umutcan Korkmaz, Cemal Dak, Enis Karaarslan
Disaster and emergency management are under the responsibility of many organizations and there are serious coordination problems in post-disaster crisis management. This paper proposes a decentralized non-governmental organization resource management system for disasters (NGO-RMSD / STK-AKYS). This system is based on blockchain technology and it will enable the non-governmental organizations (NGO) and public institutions to manage and coordinate the resources in a trusted environment in the case of disasters. A proof of concept implementation is developed by using the Quorum blockchain framework which is more energy-efficient than crypto currency-based blockchain solutions. Smart contracts are developed for the autonomous working of the system. These smart contacts are used for the verification of the needs of the one who is in need, delivering resources to the right people, and identifying the urgent needs. The system aims to reach more disaster victims in a more timely manner. NGO-RMSD is designed according to the needs of the NGOs in the field. The application is shared with the free software license and further development with the community is aimed.
PURPOSE: The main purpose of this paper was to identify the current scope of research on cryptocurrencies as a subject of fraud. Detailed research questions related to the determination of contemporary trends of the conducted research and the definition of potential opportunities for further investigation of this topic. One of the questions also concerned identifying the most common crimes committed using cryptocurrencies. METHODOLOGY: The study is based on a systematic literature review (SLR) of 57 publications available on the Scopus database. A bibliometric and descriptive analysis of selected literature items was carried out. Then, vital thematic clusters were separated, and an in-depth content analysis was performed. FINDINGS: The detailed bibliometric and descriptive analysis showed that cryptocurrencies as a subject of financial fraud are generally a new area of scientific research, although it is developing quite intensively. The relatively small number of publications, compared to other similar areas, also indicates that this topic has not yet been explored widely by scientists, and many different research trends can be created in it. Ultimately, the following key research areas were identified: types of cryptocurrency fraud, crime detection methods, risks related to blockchain technology, money laundering, and legal regulations regarding cryptocurrencies. It was also possible to identify that money laundering is currently the most common fraud. However, it has been pointed out that the second most frequent fraud is financial pyramids based on the Ponzi scheme. IMPLICATIONS: The paper clearly presents the main research trends on using cryptocurrencies in criminal activities. At the same time, it was emphasized that, compared to other research areas, this topic is relatively new. Therefore, there is a wide possibility of exploring not only existing but also undiscovered research trends. In addition, key types of fraud in economic practice have been identified, which is particularly important for financial market participants. It was clearly indicated which transactions bear the highest risk. It is also worth paying attention to the critical timeliness of the topic, as the scale of crimes involving cryptocurrencies has recently been growing rapidly. The study confirms the insufficient scope of legal regulations, which are not able to strengthen the security of economic transactions adequately. Therefore, it can be a clear indication for the governments of individual countries or international institutions for further efficient changes to the law. ORIGINALITY AND VALUE: The contribution of this study is threefold. It is one of the first research papers showing the results of a systematic literature review (SLR) combined with a bibliographic and in-depth analysis of the content of publications in this field. During the work, the VOSviewer software was also used, which enabled objective identification of the main thematic clusters based on the occurrences and link strength of keywords included in the publications. Secondly, the key types of fraud have been identified that, at the same time, cause the most significant financial loss. This allowed for the establishing of directions for further research, which have profound practical implications for market participants. Some of them relate to the need to develop and implement modern computer applications, allowing for the detection of a wider range of emerging abuses.
Jie Cai, Bin Li, Jiale Zhang, Xiaobing Sun ¡ 5 authors
Smart contract security has drawn extensive attention in recent years because of the enormous economic losses caused by vulnerabilities. Even worse, fixing bugs in a deployed smart contract is difficult, so developers must detect security vulnerabilities in a smart contract before deployment. Existing smart contract vulnerability detection efforts heavily rely on fixed rules defined by experts, which are inefficient and inflexible.To overcome the limitations of existing vulnerability detection approaches, we propose a GNN based approach for smart contract vulnerability detection. First, we construct a graph representation for a smart contract function with syntactic and semantic features by combining abstract syntax tree (AST), control flow graph (CFG), and program dependency graph (PDG). To further strengthen the presentation ability of our approach, we perform program slicing to normalize the graph and eliminate the redundant information unrelated to vulnerabilities. Then, we use a Bidirectional Gated Graph Neural-Network model with hybrid attention pooling to identify potential vulnerabilities in smart contract functions.
Fernando Ălvarez, David Argente, Diana Van Patten
A currency's essential feature is to be a medium of exchange. We leverage a quasi-natural experiment-El Salvador as the rst country to make bitcoin legal tender-to study a cryptocurrency's potential to be used in daily transactions. The government also launched and provided incentives to download and use a digital wallet named Chivo, which shares features with Central Bank Digital Currencies (CBDCs) and allows users to trade bitcoin and dollars. Were Chivo Wallet and bitcoin actually adopted after this "big push"? Conducting a representative face-to-face survey and relying on blockchain data to obtain all Chivo transactions, we document how usage of digital payments and bitcoin is low, concentrated, and has been decreasing over time. We nd that privacy concerns are key barriers to adoption, which speaks to a policy debate on crypto and CBDCs that has had anonymity at its core. We also estimate the technology's adoption cost and its network externalities.
Babajide Oluwaseun Olaogun, Adaobu Amini-Philips, Ahmed K. Ibrahim
The rapid evolution of digital currencies and blockchain technologies has created opportunities and challenges in the context of global trade. Traditional fiat payment systems, while widely adopted and regulated, often face limitations in cross-border transactions, including high fees, delayed settlements, and limited transparency. Conversely, cryptocurrency and stablecoin-based platforms offer faster and more transparent mechanisms but remain fragmented and lack integration with established financial infrastructures. This proposes an interoperability concept for connecting fiat and crypto payment platforms to facilitate efficient, secure, and cost-effective international trade settlements. The conceptual framework emphasizes a hybrid architecture that bridges fiat processors, crypto wallets, and exchange gateways through an interoperability layer. This layer employs technical mechanisms such as atomic swaps, cross-chain settlement protocols, and smart contract automation to enable seamless conversion, routing, and reconciliation of transactions. Operational considerations, including transaction monitoring, liquidity management, and real-time reporting, are integrated to ensure reliability and mitigate operational risk. Regulatory alignment is central to the framework, addressing anti-money laundering (AML), know-your-customer (KYC), tax compliance, and cross-jurisdictional legal requirements. The model further incorporates risk assessment mechanisms to manage foreign exchange volatility, crypto price fluctuations, and system-level vulnerabilities. Evaluation metrics focus on efficiency, cost-effectiveness, transparency, and transaction reliability, providing actionable insights for stakeholders including financial institutions, multinational corporations, and payment service providers. Additionally, the framework lays the groundwork for future research involving AI-driven predictive analytics, multi-chain decentralized finance (DeFi) integration, and standardized protocols for global regulatory harmonization. By establishing a structured approach to fiat-crypto interoperability, this concept enables faster, more transparent, and resilient international payments. It facilitates strategic decision-making, reduces transactional friction, and supports the evolution of a unified, hybrid global payment ecosystem that aligns technological innovation with operational and regulatory requirements.
Md. Nur Islam, Md. Golam Shakhawat Hossen, Samson P. Baidya, Md. Ahsan Ullah Emon ¡ 5 authors
Bitcoin stores its transaction details in an open distributed public ledger. In cryptocurrencies, the real identity of a user is hidden, and the only information about a user publicly available is his public address, which is not linkable to his real identity, and the transactions are sent to the public address. Among all the cryptocurrencies, bitcoin is renowned for providing the highest anonymity. This feature is exploited by the scammers and illegal users to perform their illegal transactions anonymously. To rein in such illegal activities, it is essential to expose the real identity of bitcoin users. In this paper, we propose a technique to trace the real identity of a bitcoin user. In this technique, the blockchain maintains a public ledger where every transaction of that chain is recorded and anyone within the blockchain can monitor that. The proposed platform will have a tracker for tracking a bitcoin user. A victim submits a complain to the platform giving the pubic address of the scammer. The platform continues to track the scammer. The proposed technique exposes the user identity exploiting the bitcoin and real-world currency conversion scenarios. While the existing transaction tracing techniques require the IP address and/or absence of the mixing services, the proposed technique is free of such kinds of requirements.
The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem, and our approach can act as a whistleblower that identifies scam tokens at their early stages.
Darcy W E Allen, Chris Berg, Sinclair Davidson, Jason Potts
Knowledge about property rights is a commons that facilitates market exchange and economic coordination. The governance of that shared knowledge resourceâthe various formal and informal rules that maintain ledgers of property rightsâranges from community norms to formal state registries. In this chapter we make three contributions. First, we use the lens of knowledge commons theory to argue that knowledge about property rights is a shared resource. Second, we explore how that knowledge commons is governedâparticularly relating to ârules in useââmight shift due to technological advances in distributed ledgers. Third, we argue that as blockchain augments and complements existing governance structures, it creates a more robust political economy.
Abstract Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In recent years, DeFi has gained popularity and market capitalization. However, it has also been connected to crime, particularly various types of securities violations. The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses. This study aims to determine whether this problem is suited to a machine learning approach, namely, whether we can identify DeFi projects potentially engaging in securities violations based on their tokensâ smart contract code. We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projectsâ tokensâ smart contract code (specifically, opcode-based features). Our final model was a random forest model that achieved an 80% F-1 score against a baseline of 50%. Notably, we further explored the code-based features that are the most important to our modelâs performance in more detail by analyzing tokensâ Solidity code and conducting cosine similarity analyses. We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library, although this does not account for the entirety of our features. Another contribution of our study is a new dataset, comprising (a) a verified ground truth dataset for tokens involved in securities violations and (b) a set of legitimate tokens from a reputable DeFi aggregator. This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.
In supply chains where stakeholders belong to the economically disadvantaged segment and form an important part of the supply chain distribution, the complexities grow manifold. Fisheries in developing nations are one such sector where the complexity is not only due to the produce being perishable but also due to the livelihood dependence of others in the coastal regions that belong to the section of economically disadvantaged. This paper explains the contextual challenges of fish supply chain in a developing country and describes how integrating disruptive technologies can address those challenges. Through a positive deviance approach, we show how firms can help unorganized supply chains with economically disadvantaged suppliers by carefully redesigning the supply chain through the integration of satellite imagery and blockchain technology. With COVID-19 in the backdrop, we highlight how such technologies significantly improves the supply chain resilience and at the same time contributes to the income generating opportunities of poor fisherfolks in developing nations. Our study has important implications to both developing markets and food supply chain practitioners as this paper tackles issues such as perishability, demand-supply mismatch, unfair prices, and quality related data transparency in the entire value chain.
Abstract Blockchain technology has a great potential for improving public administration â its transparency and efficiency. It is also discussed as an instrument for reducing corruption and transaction costs. This paper discusses the potential use of block-chain technology in public administration. It is based on a case-study approach focusing on real estate registration in Kazakhstan. Particular attention is paid to identifying factors hindering the development of the blockchain technology. The paper indicates that the main barriers to further use of blockchain technology in Kazakhstan are insufficient legislation and also the complexity of the technical implementation of blockchain projects and integration with existing systems.