Cryptocurrency has emerged as a viable alternative to conventional payment systems, offering its users notable advantages such as cost efficiency and rapid transaction processing. However, cryptocurrencies’ decentralized nature, anonymity, and susceptibility to cyber threats introduce the potential for their exploitation in illicit activities, notably money laundering and terrorism financing (ML/TF). This scholarly investigation seeks to investigate the roles played by international organizations actively combating such criminal activities. In this academic context, a meticulous consideration of the inherent risks associated with the aforementioned criminal endeavors is undertaken, aligning with the guidelines and recommendations set forth by the Financial Action Task Force (FATF). The study underscores a significant finding that cryptocurrency accounts can be opened anonymously, with no centralized registry to monitor cryptocurrency ownership. This characteristic poses a formidable challenge in confiscating funds linked to terrorist activities. Even in instances where cryptocurrency transactions may be traced, accessing this critical data necessitates the involvement of third-party entities, given that cryptocurrency transactions are recorded in decentralized ledgers spanning multiple jurisdictions. Furthermore, the study underscores the imperative of coordination and information exchange as indispensable elements in the ongoing battle against organized and transnational criminal activities, specifically ML/TF. Moreover, the study elucidates the incongruence between cryptocurrency and FATF regulations governing electronic fund transfers, as cryptocurrencies can make transactions through opaque and unregulated conduits, such as the deep web.
This paper explores a monetary experiment, the adoption of Bitcoin as legal tender in El Salvador in 2021, to analyse the impact of digital currencies on international capital flows. Using a difference-in-differences approach, we find that, instead of making transfers easier, El Salvador’s official cross-border financial activity has decreased after the monetary change. This finding may reflect an increase in uncertainty. However, it is also in line with findings that link digital assets to illegal activity as previously officially recorded financial transfers may have been replaced by unrecorded activities.
Under the dual impact of the COVID-19 pandemic and the Russian-Ukrainian conflict, the excessive stimulation of monetary policy continuously pushes up global inflation (INF). Therefore, this article explores whether Bitcoin can serve as a safe haven for INF. We apply the rolling-window Granger causality test to solve the issue of parameter instability in vector autoregression (VAR) systems and investigate the time-varying interaction between INF and Bitcoin price (BP). The negative influence of INF on BP means a high inflation shock causes BP to decline, indicating that Bitcoin cannot be a safe asset against INF. This is because investors have decreased their willingness to hold Bitcoin under the high INF expectations and cause BP to fall. This finding is not supported by the Intertemporal Capital Asset Pricing Model, emphasising that INF positively impacts BP. Conversely, BP has positive and negative impacts on INF. The positive effect highlights the effectiveness of Bitcoin in predicting INF fluctuations, but economic factors could undermine this effectiveness. In the context of economic stagnation and market turmoil, investors can adjust their portfolio investments based on Bitcoin. The government should utilise the trend of BP to regulate the dynamics of INF to reduce uncertainty in the financial system. First published online 30 August 2024
Algimantas Venčkauskas, Šarūnas Grigaliūnas, Linas Pocius, Rasa Brūzgienė · 5 authors
Layering through cryptocurrency transactions represents a sophisticated mechanism for laundering money within cybercrime circles. This process methodically merges illegal funds into the legitimate financial system. Blockchain technology plays a crucial role in this integration by facilitating the quick and automated dispersal of assets across various digital wallets and exchanges. Machine learning emerges as a powerful tool for analyzing and identifying illicit transactions within Blockchain networks; however, a significant challenge remains in the form of a gap in advanced pattern recognition algorithms. This paper introduces a novel machine learning-based approach called Value-driven-Transactional tracking Analytics for Crypto compliance (VTAC) for the detection of illegal crypto transactions via Blockchain. The approach combines machine learning algorithms with a pre-training process, normalization, model training, and a de-anonymization process to analyze and identify illicit transactions effectively. Experimental evaluations show VTAC’s capability to detect illegal transactions with a 97.5% accuracy using the XG Boost model, outperforming existing methods with an accuracy of up to 95.9%. Key performance metrics, including precision, recall, and F1-score, consistently exceeded 95%, highlighting VTAC’s enhanced precision and reliability. The proposed solution will serve as an advisory framework to help financial crime investigators enhance the detection and reporting of suspicious cryptocurrency transactions in cyberspace.
This study explores the application of deep learning and machine learning technologies in the field of Anti-Money Laundering (AML) for cryptocurrencies.With the rapid growth of cryptocurrency markets, the associated money laundering activities have increasingly become a focal point for governments and financial institutions worldwide.Traditional AML measures face challenges in the digital realm, particularly in identifying and preventing illicit transactions involving cryptocurrencies.To address this, the study designs various algorithms including Deep Neural Networks (DNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Naive Bayes (NB) to enhance the detection capabilities of suspicious transactions within the Bitcoin Elliptic dataset.Cryptocurrencies involve using cryptographic security measures for financial transactions, yet their anonymity and transnational nature make them susceptible to money laundering activities.By evaluating the performance of different machine learning models on the Bitcoin Elliptic dataset, this research analyzes their effectiveness in identifying illicit transactions.The results indicate that the Random Forest model performs best, achieving an overall accuracy of 95%, effectively distinguishing between most illegal and legal transactions while mitigating overfitting risks.Through these technological approaches, the study aims to enhance AML monitoring capabilities in cryptocurrency markets, providing reliable decision support for financial institutions and regulatory bodies.Future research directions may include exploring more complex deep learning models or ensemble learning methods to further improve classification accuracy across diverse datasets and enable real-time monitoring of emerging money laundering patterns.The integration of these technologies holds promise for strengthening the global AML framework, addressing the increasingly complex challenges posed by digital finance and illicit financial activities (
Aim: As a new asset class, Bitcoin and other cryptocurren-cies can be interesting for investors in the context of return stabilization, especially in times of crisis. We aimed to anal-yse whether Bitcoin can serve as a safe haven for investors in times of crisis. Methods: The data covers the period from September 17, 2014, to April 29, 2021, with 382 observations. Yahoo! Finance served as the source for the Bitcoin prices and Investing.com for the values of the Standard & Poor’s 500 (S&P500) Index. We used the maximum likelihood method to estimate the dynamic conditional correlation model. Results: Due to the high volatility during the analysed peri-od, Bitcoin achieved a higher risk-adjusted return compared to the S&P500 Index. The DCC model showed a positive cor-relation between the returns of the S&P500 and Bitcoin during the analysed period. Conclusions: Our results suggest that Bitcoin may not serve as a safe haven for investors in times of crisis. However, its role in this context should be further evaluated by examin-ing its relationship with other traditional asset classes (gold, commodities) and other types of cryptocurrencies such as stablecoins.
Decentralized Finance (DeFi), a pivotal component of the emerging Web3 landscape, is gaining popularity but remains vulnerable to market manipulations, such as wash trading. Wash trading is an illegal practice, where traders buy and sell assets to themselves within cryptocurrency exchanges to artificially inflate trading volumes and distort market perceptions. However, current research primarily focuses on traditional exchanges based on the Order-book mechanism (similar to stock markets), while ignoring the Automated Market Maker (AMM) exchanges, which dominate over 75% of the market and represent a significant innovation within the DeFi. This study utilizes entity recognition technology to detect wash trading on AMM exchanges within Ethereum-like systems, based on the understanding that colluding addresses (perceived as the same entity) must use ETH for transaction fees and exhibit direct or indirect ETH transfer links. We identify wash trading when addresses with transfer connections almost simultaneously buy and sell assets while their total asset holdings remain nearly constant. This comprehensive blockchain network analysis, compared to focusing solely on transactions within exchanges, unveils covert wash trading activities. Our detection method achieves a 95.9% recall and a 96.7% true negative rate in identifying pools affected by wash trading, demonstrating its superiority over existing methods. Furthermore, we apply our method to 98,945 pools from Uniswap V2 & V3 (the most popular AMM exchanges on Ethereum) and identify 1,070,626 abnormal transactions, totaling $27.51 billion in trading volume. Analysis of these transactions uncovers insights into wash traders’ behaviors, including the utilization of multiple addresses and the dual roles of certain addresses as wash traders and liquidity providers. These insights are crucial for developing more effective strategies to combat fraudulent activities in the DeFi ecosystem and enhance financial scrutiny.
Due to its anonymity and decentralization, Bitcoin has long been a haven for various illegal activities. Cyber-criminals generally legalize illicit funds by Bitcoin mixing services. Therefore, it is critical to investigate the mixing services in cryptocurrency anti-money laundering. Existing studies treat different mixing services as a class of suspicious Bitcoin entities. Furthermore, they are limited by relying on expert experience or needing to deal with large-scale networks. So far, multi-class mixing service identification has not been explored yet. It is challenging since mixing services share a similar procedure, presenting no sharp distinctions. However, mixing service identification facilitates the healthy development of Bitcoin, supports financial forensics for cryptocurrency regulation and legislation, and provides technical means for fine-grained blockchain supervision. This paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification (BMSI-GC) model. First, BMSI-GC constructs 2-hop ego networks (2-egonets) of mixing services based on their historical transactions. Second, it applies graph2vec, a graph classification model mainly used to calculate the similarity between graphs, to automatically extract address features from the constructed 2-egonets. Finally, it trains a multilayer perceptron classifier to perform classification based on the extracted features. BMSI-GC is flexible without handling the full-size network and handcrafting address features. Moreover, the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for identification. Our experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification, achieving an average identification F1-score of 95.08%.
Mohammad Nur Bobby Putra Yusra, Arthur Josias Simon Runturambi, Bondan Widiawan
In Indonesia, Cryptocurrency, on the one hand, is not recognized as a legal tender, so it does not have a legal umbrella, and the risk of its use is borne by the user himself. On the other hand, cryptocurrencies are included in the list of commodities that can be used as the subject of Futures Contracts traded on the Futures Exchange because, from their use, it is expected to make a positive contribution to futures trading in Indonesia. These two contradictory things of cryptocurrency regulation are then faced with a phenomenon called cryptocurrency-based TPPU. This study uses a descriptive method combined with a qualitative approach. The data in this study is primary data sourced from the results of interviews and data from the Metro Jaya Police, and the secondary data used to support the research is literature in the form of books, research journals and online scientific journal data. The data that has been collected is analyzed by data reduction methods and triangulation techniques. The location of the research is the Metro Jaya Police and the University of Indonesia Library. From this study, it is known that in Indonesia, there are no special rules governing this cryptocurrency-based anti-corruption, and there has been no cooperation between law enforcement for its prevention.
The coming of cryptocurrencies has, amazingly, changed the face of the financial space. It has opened up opportunities and challenges in the field of money laundering. The deep impact of cryptocurrencies on the practice of money laundering becomes a detailed study. Since digital currencies are embedded with inherent characteristics, such as anonymity, decentralization, and the ease of performing cross-border transfers, criminals have now found new ways to conceal their illicit financial activities. The paper critically reviews how cryptocurrencies are used in money laundering schemes, evaluates the effectiveness of current legal provisions and anti-money laundering measures, and reviews case studies that exemplify real-world applications and challenges to regulatory bodies. Moreover, it offers recommendations on the use of new technologies, like blockchain analytics, toward better detection and prevention of money laundering through cryptocurrency. The paper thus provides a range of useful insights, associated with recommendations for the strengthening of the global regulatory framework in dealing with the increased threat of cryptocurrency money laundering, through a synthesis of the literature review, case analysis, and expert interviews. The paper contributes to this debate by providing insight into the challenges that regulatory authorities face and making recommendations to improve anti-money laundering efforts in the cryptocurrency space. This is done through an in-depth review of recent cases and legislation in this area. The findings were that, though cryptocurrencies pose a great challenge, innovative technology solutions coupled with international cooperation can play a vital role in mitigating the risks associated with cryptocurrency-based money laundering.
The main purpose of this study is to provide a comprehensive assessment of the importance of cryptocurrencies in terms of money laundering risk and to provide detailed information on money laundering techniques and structures. An analysis of how cryptocurrencies impact local and international money laundering is also being explored to clarify the facts. The article attempts to provide a better understanding of this emerging problem by providing information on the use of digital currency to change the money laundering landscape. To clarify the review, the review is divided into two main parts: The first part will focus on the theoretical framework of the money laundering process, the process complexity of cryptocurrencies and the ecosystems surrounding them. The second part will examine whether virtual currencies are suitable for money laundering, the various features that make virtual currencies ideal for such activities, and the creation of new emerging technologies will also be discussed. This document is designed to provide policymakers, regulators, and law enforcement with useful information and strategic solutions to address the challenges of cryptocurrency money laundering through many of these methods.
Modern blockchains support the execution of application-level code in the form of smart contracts, allowing developers to devise complex Distributed Applications (DApps). Smart contracts are typically written in high-level languages, such as Solidity, and after deployment on the blockchain, their code is executed in a distributed way in response to transactions or calls from other smart contracts. As a common piece of software, smart contracts are susceptible to vulnerabilities, posing security threats to DApps and their users.
Cryptocurrencies, especially privacy coins, conceal the flow of money. Similarly, the dark web obscures the flow of internet traffic, increasing anonymity. In this paper, I provide evidence that secondary market trading activity in privacy coins is linked to dark web traffic, although their pricing remains mostly unaffected. This finding holds after considering various controls and comparing similar privacy and non-privacy coins. However, when disentangling dark web traffic by country of origin, I find that privacy coin prices correlate positively with traffic from China, while trading volume is mainly driven by users from Russia and Iran.
Fernando Richter Vidal, Naghmeh Ivaki, Nuno Laranjeiro
The number of applications supported by blockchain smart contracts has been greatly increasing in recent years, with smart contracts now being used across several domains, such as the music industry, finance, and retail, to name a few. Despite being used in business-critical contexts, the number of security vulnerabilities in smart contracts has also been increasing, with many of them being exploited and resulting in huge financial and reputation losses. This is despite the enormous effort that is being placed into the research and development of vulnerability detection tools and techniques, which have also greatly increased in number and type in the last few years. Motivated by the recent increase in both vulnerabilities and vulnerability detection techniques, this paper reviews the latest research in smart contract vulnerability detection, emphasizing the techniques being used, the vulnerabilities targeted, and the characteristics of the dataset used for evaluating the technique. We mapped the vulnerabilities against two common vulnerability classification schemes (DASP and SWC) and performed a consolidated analysis. We identified the current research trends and gaps in each technique and highlighted future research opportunities in the field. • A categorization of smart contract vulnerability detection techniques. • The identification of smart contract vulnerabilities that are the target of current vulnerability detection tools. • An analysis of the datasets used in smart contract vulnerability research.
Anticipatory action (AA) refers to a series of interventions executed when a hazard presents imminent danger, as indicated by forecasts, early warnings, or pre-disaster risk analyses. It involves proactive measures undertaken by individuals or organizations before an expected disaster to lessen its impact on people, assets, and infrastructure likely to be affected. An increasing array of inter-governmental bodies, governments, United Nations entities, global non-governmental organizations, and Red Cross and Red Crescent movements, have been delving into and utilizing anticipatory action. It has been heralded globally by governments, donors and organizations alike as being a more protective, practical, and cost-effective method of disaster risk management. However, ongoing pilots by the Food and Agriculture Organization of the United Nations (FAO) and partners have exposed a significant level of friction between the capacity and speed of existing systems and stakeholders to execute these processes in a manner that is synchronized and timely enough to have the intended impact. This research investigates the potential of blockchain technology to streamline AA programs. It analyzes existing academic and non-academic sources (scholarly and gray literature) to develop a framework for identifying which blockchain applications can be most beneficial. By viewing smart contracts, data oracles, and decentralized finance (DeFi) as a toolbox, the framework examines how these technologies can be used to digitize and simplify AA processes. For AA practitioners, this approach offers a practical solution to potentially increase efficiency and speed within existing program structures. Additionally, it may have the secondary benefit of simplifying cash transfer delivery. Ultimately, this piece seeks to be more thought-provoking than prescriptive; as both areas of work (AA and blockchain technology) are continuously evolving, hopefully new cross-sectoral collaborations can ensue and provide more granular insights on what AA and blockchain synergies look like in practice.
The deliberate fabrication of identity or information to deceive others, the unauthorised use of a credit card, debit card, or ATM, or the use of technology to transmit fraudulent information in an attempt to obtain money or valuables are all considered financial scams. There are many financial scams has been happening across the world, some of the example’s scams which happened in indie among that The Satyam Computers organizations shame was India's greatest corporate scam until 2010. The trailblazer and the heads of non mainstream based reexamining association Satyam PC organization, debased the records, expanded the proposition cost, and took enormous sums from the association a considerable amount of this was placed assets into property. This research paper covers Importance of the study, Objectives of the study, Why people should have an in depth study about Satyam computer and its various legal implication, Why people give valuable opinion and suggestion and also to create an awareness about financial scams and the stake holders, Legal compliance with fraud offense in India, Functions Of forensic accounting and it also explains about Examining financial information, bank statements, invoices, and other accounting records were all part of the forensic audit. and ends with how to prevent financial scams in India. Apparently corporate bookkeeping misrepresentation is a critical issue that is filling in both recurrence and seriousness, as confirmed by the instances of Enron, WorldCom, and Satyam. Research proof has exhibited that a rising number of false exercises have debilitated the respectability of financial reports, added to huge monetary misfortunes, and corrupted financial backers' certainty about the utility and unwavering quality of financial explanations.
The wide application of Ethereum technology has brought technological innovation to traditional industries. As one of Ethereum's core applications, smart contracts utilize diverse contract codes to meet various functional needs and have gained widespread use. However, the non-tamperability of smart contracts, coupled with vulnerabilities caused by natural flaws or human errors, has brought unprecedented challenges to blockchain security. Therefore, in order to ensure the healthy development of blockchain technology and the stability of the blockchain community, it is particularly important to study the vulnerability detection techniques for smart contracts. In this paper, we propose a Dual-view Aware Smart Contract Vulnerability Detection Framework named DVDet. The framework initially converts the source code and bytecode of smart contracts into weighted graphs and control flow sequences, capturing potential risk features from these two perspectives and integrating them for analysis, ultimately achieving effective contract vulnerability detection. Comprehensive experiments on the Ethereum dataset show that our method outperforms others in detecting vulnerabilities.
This study investigates the potential role of Bitcoin in terrorism financing by analyzing the decentralization and anonymity features of cryptocurrencies that facilitate illegal transactions. In the context of delayed regulatory capacity regarding innovations in financial technology, the study explores new opportunities for terrorism financing. International reports, such as that of the financial action task force on money laundering, highlight the growing risks of terrorism financing associated with virtual currencies like Bitcoin. While terrorist financing based on emerging technologies enables terrorist organizations to swiftly transfer funds globally, this phenomenon simultaneously increases the difficulties in combating terrorism financing at the national level. In light of this technological revolution and the expansion of terrorism, the study focuses on the interaction between Bitcoin and terrorism, exploring whether terrorist attacks significantly contribute to Bitcoin price fluctuations and to what extent the cryptocurrency may evolve into a currency used for terrorism financing.
Purpose The purpose of this paper is to estimate the implications of illicit market use for the value of Bitcoin in an event studies framework. Design/methodology/approach This study uses a data set of 58 state-level marijuana decriminalisation and legalisation bills and referenda in the USA in 2010–2022. Findings Decriminalisation is associated with a strong and consistent positive Bitcoin price response around the event, recreational legalisation induces a more ambiguous reaction and medical legalisation is found to have a negative albeit small impact on Bitcoin value. This suggests decriminalisation enhances shadow economy use value of Bitcoin, whereas recreational and medical legalisation are not consistently reducing illicit drug cryptomarket activity. The effects are robust to various estimation windows, in subsamples, and also when outliers, heavy tails, conditional heteroskedasticity and state size are accounted for. Originality/value New to the literature, the choice of US marijuana bills, specifically as sample events, is based on both theoretical and empirical grounds.
This paper presents a case study of a cryptocurrency scam that utilized coordinated and inauthentic behavior on Twitter. In 2020, 143 accounts sold by an underground merchant were used to orchestrate a fake giveaway. Tweets pointing to a fake blog post lured victims into sending Uniswap tokens (UNI) to designated addresses on the Ethereum blockchain, with the false promise of receiving more tokens in return. Using one of the scammer's addresses and leveraging the transparency and immutability of the Ethereum blockchain, we traced the flow of stolen funds through various addresses, revealing the tactics adopted to obfuscate traceability. The final destination of the funds involved two deposit addresses. The first, managed by a well-known cryptocurrency exchange, was likely associated with the scammer's own account on that platform and saw deposits exceeding $3.5 million. The second address was linked to a popular cryptocurrency swap service. These findings highlight the critical need for more stringent measures to verify the source of funds and prevent illicit activities.
Dr.Abdul Basit Khan, Hira Farman, Saif Hassan, Moomal Seelro · 5 authors
Bitcoin is the most successful cryptocurrency with the highest market capitalization of up to 53%. Due to its pseudonymous mechanism, bitcoin is being utilized in a variety of illicit activities. It is noticed, around US$72 billions of unlawful activities per year involve Bitcoin. In this study, systematic literature review is conducted on the illicit use of bitcoin, and the measures required to counter the illicit activities using Bitcon. In this work, authors have managed to select 45 research articles published during 2018-2022. The synthesis of selected articles revealed that bitcoin is proliferating in darknet markets. It is used to make payments for criminal activities such as drug trafficking, money laundering, human trafficking, pornography, ransomware, and other criminal activities like contract killers, Ponzi schemes, and terrorism financing. By the findings from this study, out of45 research articles 24.4% articles claim that bitcoin has been used in drug trafficking whereas 17.7% believe that people use bitcoin for money laundering. Moreover, Blockchain identity flexibility, dissociative anonymity, and a lack of deterrence encourage users to perform illegal activities. At present, the research community is actively involved in proposing and designing innovative approaches tocounter the illicit use of bitcoin. However, these solutions are unable to stop the misuse of bitcoin.
In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively.
This comprehensive analysis delves into the intricate impact of cryptocurrency adoption on traditional banking systems. As cryptocurrencies achieve widespread acceptance, they challenge established norms of centralized control in financial transactions, necessitating a reconsideration of the resilience and adaptability of traditional banking models. The disruptive potential, particularly embodied in blockchain technology, prompts a critical evaluation of the ongoing transformation. The article scrutinizes the positive influence of cryptocurrencies on financial inclusion and accessibility, unlocking banking services for previously underserved populations. However, the decentralized and pseudonymous nature of cryptocurrencies introduces regulatory challenges, demanding a nuanced equilibrium between fostering innovation and ensuring compliance with anti-money laundering and know your customer regulations. Traditional banks respond by embracing blockchain technology, entering collaborative endeavors with cryptocurrency projects to augment operational efficiency and transparency. Nevertheless, the inherent volatility of cryptocurrencies poses systemic risks, necessitating adept navigation by traditional banking systems. The imperative for a delicate equilibrium between innovation and regulation emerges as pivotal for the harmonious coexistence of traditional banking and the ever-evolving cryptocurrency ecosystem. As the financial landscape undergoes profound changes, this analysis underscores the necessity for adaptability and a strategic alignment between traditional and innovative financial paradigms.