Marijana Joksimović, Marija Paunović, Stevica Dedjanski
<p>This paper examines the growing issue of money laundering through cryptocurrencies on a global scale. Criminals use digital assets to launder illicitly obtained funds, converting them into cryptocurrencies to obscure the origins of the money. Unlike traditional financial systems, decentralized finance (DeFi) platforms lack mechanisms to freeze or block funds from suspicious sources, presenting a unique challenge for law enforcement. However, the blockchain underlying cryptocurrencies allows for the tracking of transactions across DeFi protocols, making it possible to trace asset movement, albeit with difficulty due to complex methods criminals use to mix and transfer funds across multiple wallets. The study employs official data from financial institutions between 2019 and 2023, using time-series analysis to forecast money laundering trends under both optimistic and pessimistic scenarios. The paper concludes by highlighting the ongoing efforts by regulatory bodies to strengthen measures aimed at preventing cryptocurrency-related money laundering.</p> <p>In order to draw adequate conclusions, the data used in the paper are official data from financial institutions relevant to money laundering. The time series used in the paper includes data related to the period from 2016 to 2023 and the forecast model based on optimistic and pessimistic scenarios is constructed.</p>
The use of cryptocurrencies in transnational criminal activities has grown in recent years. The scholarly literature on cryptocurrencies recognizes this trend. Yet, there has been comparatively little attention paid to the degree to which cryptocurrencies pose a direct threat to U.S. homeland security interests. This article fills a gap in the scholarly literature on cryptocurrencies by presenting evidence that cryptocurrencies are a threat to U.S. homeland security interests, specifically because of their uses for financing terrorism, enabling human and drug trafficking, and evading international financial sanctions.
CrimRxiv, our flagship project, is dedicated to increasing the quantity, quality, and usage of "open criminology" resources.It's a hub and repository.For example, we publish and reshare open-access articles, books, data, code, and instructional resources.This initiative enhances the scientific rigor and impact of criminological research by ensuring free access for everyone. MOTIVATION & GOALSOpen access to criminological research can significantly improve public safety and justice systems globally.With this proposal, we aim to advance open criminology by onboarding our Members into DeFi, leveraging blockchain technology to enhance transparency and efficiency in research funding and dissemination.
Cryptocurrency investments are rapidly developing worldwide, including in Indonesia. Behind its profit potential, digital assets also open opportunities for criminals to commit money laundering offenses. The anonymity, pseudonymity, and decentralization of blockchain technology underlying cryptocurrencies create challenges for law enforcement in tracking illegal activities that exploit these assets. This study aims to examine the role of existing regulations in preventing the use of digital assets as a means of money laundering and to identify the challenges faced by law enforcement in enforcing rules against suspected cryptocurrency transactions. The research will analyze the extent to which the existing regulations, both at the national and international levels, are effective in preventing the use of cryptocurrencies for money laundering crimes. The second subtitle will explore various technical and legal constraints faced by law enforcement, including the lack of international cooperation, limitations of monitoring technology, and the low level of technical expertise among law enforcement officials.
Purpose This paper aims to comprehensively examine the intricate relationship between cryptocurrency and criminal enterprises, shedding light on the methods, mechanisms and implications of cryptocurrency use in various financial crimes. Design/methodology/approach This paper conducts a thematic analysis of 51 cryptocurrency-related financial crime cases to identify criminogenic features of cryptocurrencies, key tools and features of cryptocurrency-related financial crimes. Based on the case study, policy-changing recommendations and big-data analytics tools are proposed for law enforcement agencies to combat cryptocurrency crimes. Findings This study found that decentralisation, pseudo-anonymity and borderless nature of cryptocurrencies enable cross-border financial flows and make transaction tracking a complex challenge. Popularity and market capitalisation used to play a dominant role in criminals’ choice of cryptocurrency as Bitcoin was associated with most cryptocurrency-related crimes before 2019. An increasing number of other cryptocurrencies, such as privacy coins and stablecoins, have been utilised for financial crimes recently. Cryptocurrency has also become an essential source of terrorist financing. Research limitations/implications Due to the open access to cryptocurrency transaction data, powerful big-data analytics tools should be developed to help law enforcement departments proactively detect cryptocurrency-related crimes. Practical implications It is important and urgent for cryptocurrency exchange companies, mixing services and wallet providers to implement strict anti-money laundering and know-your-customer measures to help combat cryptocurrency-related crimes and create a sustainable future for cryptocurrencies. Originality/value There is a lack of studies to reveal the current trend of cryptocurrency crimes; the relationship between criminogenic features of cryptocurrency and types of financial crimes; as well as the approaches used to facilitate different types of cryptocurrency-related financial crimes. These gaps will be addressed through thematic analysis on 51 crypto crime cases.
This article offers a clear and approachable introduction to the evolving landscape of money and the frictions developing between traditional government control and decentralized finance (DeFi). Tailored for readers with a basic awareness of cryptocurrency but limited familiarity with its broader implications, the article demystifies DeFi by explaining its core concepts including blockchain, Centralized Bank Digital Currencies (CBDCs), and the historical role of government regulation of money through central banking. Against this backdrop, it examines the transformative potential of DeFi, emphasizing the growing tension between the centralized authority of governments and the decentralized ideals driving this new financial model. While governments seek to maintain stability and control, individuals increasingly gravitate toward the more affordable, efficient, and inclusive solutions promised by DeFi. Designed to empower readers with a better grasp of the forces shaping the future of finance, this article underscores the importance of understanding the delicate interplay between governmental oversight and decentralized innovation. As the digital economy expands, this dynamic struggle will influence not only economic policies but also person-al financial choices and access to resources.
This study examines why Bitcoin consistently traded at a discount in Colombia compared to the US market between April 2020 and July 2023. We introduced the “Bitcoin yield gap,” representing the difference between Bitcoin’s trading price in Colombia and its global price. Using robust ordinary least squares regression, we found a positive relationship between foreign exchange (FX) convenience yields and the yield gap, while Bitcoin convenience yields did not show a significant relationship. Additionally, Bitcoin network demand was significantly associated with the yield gap without affecting other regression parameters. These findings highlight that the yield gap is predominantly driven by dynamics in the domestic FX market, illustrating the interplay between traditional FX markets and the digital cryptocurrency landscape in Colombia.
Jie Song, Sijia Zhang, Pengyi Zhang, Junghoon Park · 6 authors
In recent years, blockchain anonymity has led to more illicit accounts participating in various money laundering transactions. Existing studies typically detect money laundering transactions, known as AML (Anti-money Laundering), through learning transaction features on transaction graphs of transactional blockchains. However, transaction graphs fail to represent the accounts’ social features within transactional organizations. Account graphs reveal such features well, and detecting illicit accounts on account graphs provides a new perspective on AML. For example, it helps uncover illegal transactions whose transaction features are not distinct in transaction graphs, with a loose assumption that illicit accounts are likely involved in illegal transactions. In this paper, we propose a Social Attention Graph Neural Network ($\textsf {SGNN}$) on account graphs converted from transaction graphs. To detect illicit accounts,$\textsf {SGNN}$learns the social features on two sub-graphs, a heterogeneous graph and a hypergraph, extracted from the account graph, and fuses these features into account attribute vectors through attention. The experimental results on the Elliptic++ dataset demonstrate$\textsf {SGNN}$’s advances. It outperforms the best baseline by 14.18% in precision, 7.37% in F1 score, 0.96% in accuracy, and 0.64% in recall when detecting illicit accounts on account graphs, as well as detects 20.3% more recall of illegal transactions through these illicit accounts than state-of-the-art methods based on transaction graphs when the mappings between illegal transactions and illicit accounts are provided. Moreover, thanks to social features,$\textsf {SGNN}$has a novel capability that works under many account scales and activity degrees. We release our code onhttps://github.com/CloudLab-NEU/SGNN.
The rise of blockchain technology and cryptocurrencies such as Bitcoin and Ethereum has created new avenues for both lawful and illicit activities, including illegal firearm transactions. This study applies a combination of graph-based analysis, functional data techniques, and machine learning to detect and classify suspicious activities related to firearm trafficking on blockchain networks. A Random Forest model, achieving a precision of 0.907 and recall of 0.786, was used to identify illicit Bitcoin addresses, while a multi-target classifier categorized these addresses by specific types of illicit activity. For Ethereum, an XGBoost model achieved a precision of 0.9864 and an accuracy of 0.9901, demonstrating robust detection of suspicious accounts. Feature engineering and a rule-based system further enhanced model performance, though challenges remain in addressing misclassifications, particularly in distinguishing subtle transaction patterns. These findings underscore the potential of machine learning in blockchain forensics, providing critical insights for law enforcement efforts to combat illegal firearm trading.
Smart Contracts (SCs) handle transactions in the Ethereum blockchain worth millions of United States dollars, making them a lucrative target for attackers seeking to exploit vulnerabilities and steal funds. The Ethereum community has developed a rich set of tools to detect vulnerabilities in SCs, including reentrancy (RE) and unhandled exceptions (UX). A dataset of SCs labeled with vulnerabilities is needed to evaluate the tools’ efficacy. Existing SC datasets with labeled vulnerabilities have limitations, such as covering only a limited range of vulnerability scenarios and containing incorrect labels. As a result, there is a lack of a standardized dataset to compare the performances of these tools. Our dataset, SCRUBD, aims to fill this gap. SCRUBD is a dataset of real-world SCs and synthesized SCs labeled with RE and UX vulnerabilities. The real-world SC dataset is labeled through crowdsourcing, followed by manual inspection by an experienced SC programmer, and covers both RE and UX vulnerabilities. On the other hand, the synthesized dataset is carefully crafted to cover various RE scenarios only. Using SCRUBD, we compared the performance of six popular vulnerability detection tools. Based on our study, we found that Slither outperforms other tools on a crowdsourced dataset in detecting RE vulnerabilities, while Sailfish outperforms other tools on a manually synthesized dataset for detecting RE. For UX vulnerabilities, Slither outperforms all other tools.
The rise of blockchain and cryptocurrency networks has fueled financial innovation, yet it also presents new opportunities for illicit activities such as fraud and money laundering. Traditional detection approaches struggle with the non-Euclidean, large-scale nature of cryptocurrency transaction networks. Graph Neural Networks offer a promising solution for capturing complex relational data. This paper proposes four fusion architectures—Triple Parallel Layer, Hierarchical Staging, Attention-Weighted Residual Fusion, and Multi-View Feature Aggregation—combining Graph Convolutional Network, Graph Attention Network, and Graph Isomorphism Network to enhance classification of illicit transactions. Experiments on the Elliptic Bitcoin dataset show that the proposed models achieve classification accuracies up to 97.17%, significantly outperforming standalone Graph Convolutional Network, Graph Attention Network, and Graph Isomorphism Network models. These results underscore the superior performance and robustness of the fusion architectures, with improvements in accuracy ranging from 1.1% to 2.9% over individual models, marking a step forward in financial crime detection within decentralized networks.
In this paper, we examine a novel category of services in the blockchain ecosystem termed Instant Cryptocurrency Exchange (ICE) services. Originally conceived to facilitate cross-chain asset transfers, ICE services have, unfortunately, been abused for money laundering activities due to two key features: the absence of a strict Know Your Customer (KYC) policy and incomplete on-chain data of user requests. As centralized and non-transparent services, ICE services pose considerable challenges in the tracing of illicit fund flows laundered through them. Our comprehensive study of ICE services begins with an analysis of their features and workflow. We classify ICE services into two distinct types: Standalone and Delegated. We then perform a measurement analysis of ICE services, paying particular attention to their usage in illicit activities. Our findings indicate that a total of 12,473,290 illegal funds have been laundered through ICE services, and 432 malicious addresses were initially funded by ICE services. Based on the insights from measurement analysis, we propose a matching algorithm designed to evaluate the effectiveness of ICE services in terms of efficiency and prevention of traceability. Our evaluation reveals that 92% of the user requests analyzed were completed in less than three minutes, underscoring the efficiency of ICE services. In addition, we demonstrate that the algorithm is effective in tracing illicit funds in situations where ICE services are used in malicious activities. To engage the community, the entire dataset used in this study is open-source.
The Police Complaint Management System (PCMS) is a decentralized application template designed to modernize the processes of lodging, tracking, and resolving complaints within law enforcement systems. Leveraging the Next.js framework, Web3 technologies, and blockchain integration, the system ensures tamper-proof complaint records, real-time updates, and enhanced transparency for citizens and authorities. By utilizing Wagmi and Ethers.js for seamless wallet connections, IPFS for decentralized evidence storage, and a user-friendly interface styled with Tailwind CSS, the PCMS provides a scalable, efficient, and accessible platform. With automated processes for complaint categorization and routing, as well as immutable blockchain records, the system fosters greater accountability and trust in public services. Built with TypeScript for reliability and enhanced with modular tools for rapid deployment, the PCMS exemplifies a modern, citizen-centric approach to grievance management, ensuring data security and operational efficiency in law enforcement agencies
Flash loan attacks threaten decentralized finance (DeFi) protocols, which constitute a Total Value Locked (TVL) of more than 106 billion. These attacks exploit the atomicity property in blockchains to drain funds within a single block. Existing research overlooks the mitigation of non-price flash loan attacks, which mostly exploit zero-day vulnerabilities. These attacks are challenging to detect as they are highly time-sensitive and each instance of the attack is complex and has a unique pattern. To address this challenge, we present FlashGuard, a runtime detection and mitigation framework for non-price flash loan attacks. FlashGuard communicates directly with the miners and bypasses the public mempool, where attack transactions usually reside. We utilize the temporary time window where transactions are visible in the mempool but not yet confirmed. Once the attack is detected, FlashGuard dispatches a dusting counter-transaction for the victim contract to the miners directly within the same block to disrupt the attack's atomicity and change the smart contract state. This forces the malicious transaction to revert. FlashGuard ensures that the series of operations that are required for a non-price flash loan attack cannot be completed atomically, leading to a failure of the attack. Our evaluation using 20 historical attacks that exploited protocol vulnerabilities shows an outstanding detection rate for FlashGuard with minimal false positives, and effective attack disruption and indicates that FlashGuard could have rescued about $405.71 million in losses.
Cong Wu, Jing Chen, Ziming Zhao, Kun He · 10 authors
Decentralized finance has experienced phenomenal growth, revolutionizing the landscape of financial transactions and asset management via blockchain. Yet, this swift growth brings with it substantial challenges, notably the surge in scam tokens, imposing significant security threats on cryptocurrency investments and trading. Existing detection methods of scam token, primarily relying on analyzing contract codes or transaction patterns, struggle to catch increasingly sophisticated tactics employed by scammers. For example, contract-based analysis are unable to identify scams lacking overt malicious code, e.g., most rugpulls, while transaction-based methods generally lack the foresight to early-detect potential risks.
Abstract The proliferation of cryptocurrencies has brought significant changes in the global economic market while introducing new risks to national security. This paper explores the economic transformations driven by the rise of digital currencies, analyzing their impact on traditional financial systems, monetary policy, and international trade. While cryptocurrencies offer opportunities for innovation and economic growth, they also pose substantial challenges for regulators, particularly in addressing illicit activities such as money laundering, terrorism financing, and tax evasion. Furthermore, the decentralized nature of these digital assets presents unique vulnerabilities for national security, as they can be used to avoid financial controls and sanctions. This paper aims to provide a comprehensive analysis of the economic benefits and security risks associated with cryptocurrencies, emphasizing the need for coordinated regulatory frameworks. By examining the intersection of technological innovation, economic impact, and security concerns, this research contributes to the ongoing debate on how to handle the main challenges brought by the growth of cryptocurrencies in a globalized, digital economy.
Maria Jell‐Ojobor, Roland Russwurm, Josef Windsperger
ABSTRACT This study explores the implications of the introduction of blockchain technology for the governance of franchise networks. In light of the limited prior research on this topic, the study employs a twofold approach. It begins by identifying the central themes present in current research at the intersection of franchising and blockchain technology. Next, it examines existing instances of implementations of blockchain technology and connects them to corresponding value chain operations in the franchising context. The study takes a comprehensive approach, combining bibliometric analysis with a semisystematic literature review, to offer insights into blockchain technology's potential future impact on the franchise industry. Specifically, it shows that franchise network governance can benefit significantly from attributes of blockchain such as transparency, efficiency, and trust as well as from its various applications such as smart contracts and decentralized autonomous organizations.
Abstract Cryptocurrency remittances overcome many regulatory and practical barriers, but there is little empirical research into this increasingly popular remittance medium. In response, this article explores cryptocurrency remittances from Latin America and the Caribbean into Venezuela. Cryptocurrencies as a remittance medium conveys important messages for advocates and critics. To appropriately critique cryptocurrencies, it is important to understand how they are used in the ‘every day’ rather than how their use may be characterised by ideologues. Rather than directly relying on ‘trustless’ and decentralised blockchain technology, ‘really existing’ cryptocurrency remittances are highly intermediated. Access to this medium is often hierarchical, stemming from knowledge barriers but also legal status (and by extension, economic status). The ‘need’ for trusted intermediaries prompts discussions around the relationship between ‘trustless’ blockchain technology and cryptocurrency remittances. This article shows that stablecoins (cryptocurrencies pegged to fiat currencies—usually the US dollar) are the most popular cryptocurrency remittance medium. Stablecoins challenge institutional attempts to geographically restrict currencies, yet also contribute to global processes of dollarisation. This is important to understanding how stablecoins simultaneously undermine spatial barriers to financial access yet may create new ones in the process.
This chapter explores blockchain technology, a revolutionary distributed ledger system transforming the financial sector by enabling secure, transparent, decentralized transaction recording and management. Focusing on the accounting and finance industries, the chapter examines how blockchain technology is being adopted to enhance operations and service automation. It highlights blockchain's significant benefits, such as increased transparency, faster transaction processing, cost reduction, improved accuracy, and enhanced security. The chapter comprehensively discusses blockchain's diverse applications in accounting, banking, and finance, including its role in cross-border payments, stock exchanges, identity verification, and the standardization of bookkeeping, accounting, and auditing practices. Additionally, it critically analyzes the potential risks associated with blockchain adoption, like regulatory challenges and security vulnerabilities, underlining the importance for businesses to weigh both risks and benefits in the rapidly digitalizing financial landscape.
In this paper, we analyze the global controversy surrounding the innovation of cryptocurrencies, developing an analytical framework to assess the empirical structure of arguments. By unpacking an argumentation analysis of a comprehensive set of scholarly, media, and industry publications, we identify six key dimensions of disagreement, comprising 42 distinct arguments. These dimensions include the raison d’être, environmental impact, social inclusion, susceptibility to illegal activities, economic impact, and potential for decentralization and democratization. Our findings reveal entrenched positions supported by robust scholarly research and empirical evidence. Cryptocurrencies represent a controversial innovation, for which global resolution remains elusive. While the controversy may appear unbounded, we plead for a geographical approach, emphasizing that localized institutional contexts are crucial for exploring potential trajectories of the controversy. Finally, our analysis illustrates the potential of argumentation analysis to properly disentangle complex societal disagreements, and it therefore promises to enrich the methodological pluralism in economic geography.
In order to maintain the value of the national currency and control foreign debt, central banks are vital to the management of a nation’s foreign exchange reserves. These reserves, however, are vulnerable to a variety of hazards, including as money laundering, fraud, theft, and cyberattacks. These are issues that traditional financial systems frequently face because of their vulnerabilities and inefficiency. Using modern innovations in a blockchain-based solution can help tackle these serious issues. To protect data privacy, the Microsoft SEAL library is utilized for homomorphic encryption (FHE). For the development of smart contracts, Solidity is employed within the Ethereum blockchain ecosystem. Additionally, Amazon Web Services (AWS) is leveraged to provide a scalable and powerful infrastructure to support our solution. To guarantee safe and effective transaction validation, our method incorporates a hybrid consensus process that combines Proof of Authority (PoA) with Byzantine Fault Tolerance (BFT). The administration of foreign exchange reserves by central banks is made more secure, transparent, and operationally efficient by this all-inclusive approach.