This study aims to assess the impacts that cryptocurrencies and blockchain have on financial accounting and auditing through eight interviews. The results allowed the authors to conclude that cryptocurrencies are considered cryptoassets, which can be classified in different ways, but above all, as inventories and intangible assets. Respondents believe that cryptocurrencies will have an impact on auditing, triggering a dematerialization of paper in its various stages. Therefore, the auditor will spend less time collecting and verifying information, focusing on activities with greater risk and complexity. It will also be possible to carry out an audit in real time and on the entire population. Regarding audit risk, accounting and auditing standards respond indirectly to the topic, yet the risk is considered high for most auditors, as they did not deepen their knowledge on the topic. Despite limitations, such as sample size, this study contributes to understanding the impact of cryptocurrencies on financial accounting and auditing in Portugal, being a pioneer in this field.
Abstract The burgeoning interest in decentralized applications (Dapps), spurred by advancements in blockchain technology, underscores the critical role of smart contracts. However, many Dapp users, often without deep knowledge of smart contracts, face financial risks due to hidden vulnerabilities. Traditional methods for detecting these vulnerabilities, including manual inspections and automated static analysis, are plagued by issues such as high rates of false positives and overlooked security flaws. To combat this, the article introduces an innovative approach using the bidirectional encoder representations from transformers (BERT)‐ATT‐BiLSTM model for identifying potential weaknesses in smart contracts. This method leverages the BERT pre‐trained model to discern semantic features from contract opcodes, which are then refined using a Bidirectional Long Short‐Term Memory Network (BiLSTM) and augmented by an attention mechanism that prioritizes critical features. The goal is to improve the model's generalization ability and enhance detection accuracy. Experiments on various publicly available smart contract datasets confirm the model's superior performance, outperforming previous methods in key metrics like accuracy, F1‐score, and recall. This research not only offers a powerful tool to bolster smart contract security, mitigating financial risks for average users, but also serves as a valuable reference for advancements in natural language processing and deep learning.
T. Niedermayer, Pietro Saggese, Bernhard Haslhofer
The integration of bots in Distributed Ledger Technologies (DLTs) fosters efficiency and automation. However, their use is also associated with predatory trading and market manipulation, and can pose threats to system integrity. It is therefore essential to understand the extent of bot deployment in DLTs; despite this, current detection systems are predominantly rule-based and lack flexibility. In this study, we present a novel approach that utilizes machine learning for the detection of financial bots on the Ethereum platform. First, we systematize existing scientific literature and collect anecdotal evidence to establish a taxonomy for financial bots, comprising 7 categories and 24 subcategories. Next, we create a ground-truth dataset consisting of 133 human and 137 bot addresses. Third, we employ both unsupervised and supervised machine learning algorithms to detect bots deployed on Ethereum. The highest-performing clustering algorithm is a Gaussian Mixture Model with an average cluster purity of 82.6%, while the highest-performing model for binary classification is a Random Forest with an accuracy of 83%. Our machine learning-based detection mechanism contributes to understanding the Ethereum ecosystem dynamics by providing additional insights into the current bot landscape.
Thomas Vinther Daugaard, Jakob Bisgaard Jensen, Robert J. Kauffman, Kwansoo Kim
Banks can reduce resources spent on anti-money laundering (AML) compliance with block-chain-based transaction infrastructure. We consider AML compliance as a superset of know-your-customer (KYC) and transaction monitoring capabilities. We conducted an instrumental case study with Danske Bank and Concordium, using internal documents and interviews that served as empirical data. We show how storing digital representations of verified IDs with a blockchain can automate tasks and reduce redundant verification in KYC onboarding. Blockchain transparency also improves identifying counterparties, determining funds sources, and creating alerts in transaction monitoring. These reduce time and labor costs for AML compliance, which may lead to smaller banks. When more banks commit to layer-1 blockchain technology, the benefits of blockchain-based AML will increase. We implemented an instrumental case study approach in this theory-based qualitative research and encourage ECRA readers to recognize that the emerging technology innovations we study in this article have not yet been widely adopted and implemented by financial services firms. We also include a theoretical model with study hypotheses to make the main constructs that we investigate easily understood by non-technical ECRA readers. The findings we have developed are consistent with early-stage exploration in our research context and are intended to encourage more well-developed empirical results as the passage of time permits such work to be undertaken. (213 words)
Purpose This paper aims to investigate technological innovations within the crypto space that have engendered novel financial crime risks and their potential utilization amidst geopolitical conflicts. Design/methodology/approach The theoretical paper uses an analysis of recent geopolitical events, with a key focus on using cryptocurrencies to undertake illicit activities. Findings The study found that cryptocurrencies and the innovations made within the crypto domain are used for both legitimate and illicit purposes, including money laundering, terrorism financing and sanction evasion. Originality/value This research contributes to understanding the critical role cryptocurrencies play amidst geopolitical conflicts and emphasizes the need for regulatory considerations to prevent their misuse. To the best of the authors’ knowledge, this paper is the first scholarly contribution that considers the evolving mechanisms afforded by cryptocurrencies amidst geopolitical conflicts in undertaking illicit activities.
We consider the execution of smart contracts on Bitcoin. There, every contract step corresponds to appending to the blockchain a new transaction that spends the output representing the old contract state, creating a new one for the updated state. This standard procedure requires the contract participants to pay transaction fees for every execution step. In this paper, we introduce a protocol that moves most of the execution of a Bitcoin contract off-chain. When all participants follow this protocol, they are able to save on transaction fees, drastically reducing them. By contrast, whenever adversaries try to disrupt the off-chain execution, any honest participant is still able to enforce the correct contract behaviour, by continuing its execution on-chain.
Munir Ahmad, Muhammad Idrees, Muhammad Saleem Qazi
Cryptocurrencies have become a preferred mode of terror financing. it is imperative to analyze the role of cryptocurrencies in empowering terrorist organizations especially in developing states. It takes on a more serious form in developing countries like Pakistan, which is fighting a battle against terrorism for the last few decades. Now digital financing has become an undeniable reality of the modern world, it is essential to devise a comprehensive strategy for breaking the nexus between terrorism and cryptocurrencies. This article intends to explore the answers to the questions that why cryptocurrencies are becoming a preferred mode for terror financing. It argues that the anonymity, decentralization and operational ease of cryptocurrencies make them a preferred medium for terrorist organizations. Therefore, enhancing institutional capacity by training human resources and making dedicated efforts at the national level will prove to be instrumental in delinking terrorism and cryptocurrencies. Moreover, close collaboration between international watchdogs and national institutions is essential in mitigating the threat of the use of cryptocurrencies for terrorism.
Jamal Wiwoho, Irwan Trinugroho, Dona Budi Kharisma, Pujiyono Pujiyono
This article analyzes the negative impact of cryptocurrency mining activities on the environment and analyzes the urgency of cryptocurrency mining policy to protect the environment.This analysis was carried out by conducting a comparative study of regulations in countries open to the development of cryptocurrency, including Indonesia, the United States, China, and Iran.The results of this comparison are used as material for constructing environmentally friendly cryptocurrency mining regulations to be implemented in various countries.This type of research is legal research.The research approaches are the statutory, comparative, and case approaches.Data was collected using the literature study method, and technical data analysis was carried out using a qualitative juridical method.The results of this research are how to prevent and combat the negative impacts of cryptocurrency mining activities on the environment, including implementing several policies including minimizing greenhouse gas emissions, ensuring reliable energy, encouraging transparency and increasing environmental performance, data search to understand, monitor, and reduce impact, enactment of energy efficiency standards, besides that it is necessary to implement transaction fees and carbon taxes for cryptocurrency mining.
In recent years, Decentralized Finance (DeFi) has grown rapidly due to the development of blockchain technology and smart contracts. As of March 2023, the estimated global cryptocurrency market cap has reached approximately $949 billion. However, security incidents continue to plague the DeFi ecosystem, and one of the most notorious examples is the “Rug Pull” scam. This type of cryptocurrency scam occurs when the developer of a particular token project intentionally abandons the project and disappears with investors’ funds. Despite only emerging in recent years, Rug Pull events have already caused significant financial losses. In this work, we manually collected and analyzed 103 real-world rug pull events, categorizing them based on their scam methods. Two primary categories were identified:Contract-relatedRug Pull (through malicious functions in smart contracts) andTransaction-relatedRug Pull (through cryptocurrency trading without utilizing malicious functions). Based on the analysis of rug pull events, we propose CRPWarner (short forContract-relatedRugPull RiskWarner) to identify malicious functions in smart contracts and issue warnings regarding potential rug pulls. We evaluated CRPWarner on 69 open-source smart contracts related to rug pull events and achieved a 91.8% precision, 85.9% recall, and 88.7% F1-score. Additionally, when evaluating CRPWarner on 13,484 real-world token contracts on Ethereum, it successfully detected 4168 smart contracts with malicious functions, including zero-day examples. The precision of large-scale experiments reaches 84.9%.
The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address-transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others.
Aziz N. Berdiev, Rajeev K. Goel, James W. Saunoris
Abstract The recent prevalence of digital currencies has challenged policymakers as they try to control the supply of money and rein in clandestine activities. Corruption and shadow economy are widely prevalent illegal/unobserved activities that have been hard to eliminate worldwide. These longstanding and entrenched activities have possibly found a new avenue to thrive and evade detection/punishment. So disentangling the nexus between corruption, shadow economy, and digital currencies is important. Using recent cross‐country data, this paper analyzes the interrelationships between corruption, shadow economy, and cryptocurrencies. We argue that a large underground sector in a nation provides a mechanism through which corrupt government officials use cryptocurrencies to conceal their unauthorized earnings. Employing formal mediation analysis, our results show that the positive nexus between corruption and cryptocurrency adoption is mediated by the shadow sector. Quantitatively speaking, three‐fourths of the correlation between corruption and cryptocurrency usage is mediated by the shadow economy. The primary implication of our findings is that effective monitoring of cryptocurrencies should pay attention to policies to control both corruption and the shadow economy.
In the introductory part of the paper, the author briefly explores the emergence of the first cryptocurrency (Bitcoin), which was initially devised for the purpose of securing easier transactions without intermediaries. Criminals soon realised that cryptocurrencies, due to their inherent characteristics, could provide them with anonymity. As other cryptocurrencies (altcoins) emerged, it was necessary to define their conceptual framework. While cryptocurrencies were initially used in illegal sales of narcotics, their application soon spread to a number of other criminal activities. In that context, the author first presents the reasons that led criminals to turn to cryptocurrencies in their financial transactions, and then explains the possible uses of cryptocurrencies in the commission of crime. The central part of the paper provides examples of criminal activities committed by using cryptocurrencies. It is reasonable to expect that, in the future, the use of cryptocurrencies will extend to other criminal activities, which are still unaffected by the trend that has existed for the last ten years.
Clement Daah, Amna Qureshi, Irfan Awan, Savas Konur
As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity and access management (IAM), data protection, and device and network security and introduces trust through blockchain technology. This study provides a literature review of existing Zero Trust paradigms and contrasts them with cybersecurity solutions currently relevant to financial settings. The research adopts a mixed methods approach, combining extensive qualitative analysis through a literature review and assessment of security assumptions, threat modelling, and implementation strategies with quantitative evaluation using a prototype banking application for vulnerability scanning, security testing, and performance testing. The IAM component ensures robust authentication and authorisation processes, while device and network security measures protect against both internal and external threats. Data protection mechanisms maintain the confidentiality and integrity of sensitive information. Additionally, the blockchain-based trust component serves as an innovative layer to enhance security measures, offering both tamper-proof verification and increased integrity. Through analysis of potential threats and experimental evaluation of the Zero Trust model’s performance, the proposed framework offers financial institutions a comprehensive security architecture capable of effectively mitigating cyber threats and fostering enhanced consumer trust.
The aim of this chapter is to situate Decentralized Autonomous Organizations (DAOs) within the philosophical scope of cryptoanarchism. The chapter proceeds by identifying key aspects of cryptoanarchist thought that resonate with DAOs, and argues that the consonance/dissonance of DAOs with cryptoanarchism helps elucidate several interesting facets of DAOs as they have emerged thus far, including: their design, organization, vulnerability, and efficiency. The findings of the chapter suggest that, although DAOs do not square entirely with cryptoanarchist precepts, both are in oeurionevolution, and thus may find several alternate paths of expression as DAOs mature.
ABSTRACT Bitcoin, launched in 2009, has gone through inconspicuous years, halcyon years, and now, the chaos years. Writers predicted that bitcoin would replace fiat currencies and the underlying technology, blockchain, would significantly reduce the need for auditors. However, there have been numerous major cryptocurrency crimes and related bankruptcies. Called the “heist of the century,” in the 2016 Bitfinex hack, 119,754 bitcoins were illegally transferred from over 2,000 Bitfinex accounts to an external wallet. Starting in January 2017, about 25,118 of those bitcoins were transferred to other exchanges. Several money laundering techniques were employed; however, the perpetrators made mistakes and were arrested in 2022. They were identified through analyzing the bitcoin blockchain, geolocating the IP addresses used for communications, and identifying individuals when bitcoin was used for “real world” transactions. The bitcoin blockchain is publicly available and would be a great resource for Big Data, data analytics, and forensics classes. JEL Classifications: K42.
Dimitris Karakostas, Aggelos Kiayias, Thomas Zacharias
We analyze bribing attacks in Proof-of-Stake distributed ledgers from a game theoretic perspective. In bribing attacks, an adversary offers participants a reward in exchange for instructing them how to behave, with the goal of attacking the protocol's properties. Specifically, our work focuses on adversaries that target blockchain safety. We consider two types of bribing, depending on how the bribes are awarded: i) guided bribing, where the bribe is given as long as the bribed party behaves as instructed; ii) effective bribing, where bribes are conditional on the attack's success, w.r.t. well-defined metrics. We analyze each type of attack in a game theoretic setting and identify relevant equilibria. In guided bribing, we show that the protocol is not an equilibrium and then describe good equilibria, where the attack is unsuccessful, and a negative one, where all parties are bribed such that the attack succeeds. In effective bribing, we show that both the protocol and the "all bribed" setting are equilibria. Using the identified equilibria, we then compute bounds on the Prices of Stability and Anarchy. Our results indicate that additional mitigations are needed for guided bribing, so our analysis concludes with incentive-based mitigation techniques, namely slashing and dilution. Here, we present two positive results, that both render the protocol an equilibrium and achieve maximal welfare for all parties, and a negative result, wherein an attack becomes more plausible if it severely affects the ledger's token's market price.
Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent years, causing massive financial losses. Rule-based detection approaches rely on pre-defined rules with limited capabilities and domain knowledge dependency. Additionally, using static information like opcodes and transactions for machine learning models fails to effectively characterize the Ponzi contracts, resulting in poor reliability and interpretability.
Bitcoin has been gaining increasing attention in academia and industry.This article investigates Bitcoin's research status and evolution via bibliometrics using a dataset of 3,873 publications between 2012 and 2022 from the Web of Science Core Collection.The findings reveal a significant increase in research on Bitcoin since 2017, coinciding with the cryptocurrency bull market.The article identifies publication trends, influential contributors, collaboration networks, and topics evolution in Bitcoin research.The three Bitcoin research stages are conceptualisation and fundamentals of Bitcoin (2012-2016), cryptocurrency and market efficiency (2017)(2018), and technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets (2019-2022).The four prominent emerging areas for future Bitcoin research are decentralised finance (DeFi), non-fungible tokens (NFTs), clean energy and mining, and monetary policy.The article offers valuable insights for researchers, policymakers, and practitioners, facilitating a better understanding of the status quo of Bitcoin research.
Elohim Fonseca dos Reis, Alexander Teytelboym, Abeer ElBahrawy, Ignacio De Loizaga · 5 authors
Dark web marketplaces have been a significant outlet for illicit trade, serving millions of users worldwide for over a decade. However, not all users are the same. This paper aims to identify the key players in Bitcoin transaction networks linked to dark markets and assess their role by analysing a dataset of 40 million Bitcoin transactions involving the 31 major markets in the period 2011-2021. First, we propose an algorithm that categorizes users either as buyers or sellers, and show that a large fraction of the trading volume is concentrated in a small group of elite market participants. We find that the dominance of markets is reflected in trading properties of buyers and sellers. Then, we investigate both market star-graphs and user-to-user networks, and highlight the importance of a new class of users, namely 'multihomers', who operate on multiple marketplaces concurrently. Specifically, we show how the networks of multihomers and seller-to-seller interactions can shed light on the resilience of the dark market ecosystem against external shocks. Our findings suggest that understanding the behavior of key players in dark web marketplaces is critical to effectively disrupting illegal activities.
With the ever-increasing advancement in blockchain technology, security is a significant concern when substantial investments are involved. This paper explores known smart contract exploits used in previous and current years. The purpose of this research is to provide a point of reference for users interacting with blockchain technology or smart contract developers. The primary research gathered in this paper analyses unique smart contracts deployed on a blockchain by investigating the Solidity code involved and the transactions on the ledger linked to these contracts. A disparity was found in the techniques used in 2021 compared to 2023 after Ethereum moved from a Proof-of-Work blockchain to a Proof-of-Stake one, demonstrating that with the advancement in blockchain technology, there is also a corresponding advancement in the level of effort bad actors exert to steal funds from users. The research concludes that as users become more wary of malicious smart contracts, bad actors continue to develop more sophisticated techniques to defraud users. It is recommended that even though this paper outlines many of the currently used techniques by bad actors, users who continue to interact with smart contracts should consistently stay up to date with emerging exploitations.
Cryptocurrency and Bitcoin have gained significant attention in recent years, disrupting traditional banking systems and raising concerns about their impact on the international economy and cybersecurity. Bitcoin, the first and most well-known cryptocurrency, has seen an exponential rise in value since its inception in 2009, reaching an all-time high of over $1 trillion in market cap in 2021. So, cryptocurrency and Bitcoin have significant impacts on the international economy and cybersecurity landscape. While they offer many benefits, they also pose significant challenges and risks. As the technology continues to evolve, it will be essential for governments, financial institutions, and individuals to stay informed and take steps to ensure the security of their digital assets.
Cryptocurrency, a form of digital currency, has emerged as a disruptive force in the financial landscape. Built on the the historical context and key milestones in the development of cryptocurrencies, highlighting the release of Bit coin as the pioneering cryptocurrency. It explores the underlying technology of block chain, elucidating its decentralized nature and cryptographic security features that enable trust and accountability in transactions. Overall, this abstract offers foundations of block chain technology, cryptocurrencies offer decentralized, secure, and transparent transactions, challenging the traditional centralized financial systems. This abstract presents a comprehensive analysis of the evolution, functioning, and implications of cryptocurrencies. The study begins by examining a comprehensive overview of cryptocurrencies, providing insights into their technological foundations, economic implications, and potential future developments. It aims to contribute to the understanding of this transformative digital currency revolution and its impact on global finance and economics