The trustless nature of permissionless blockchains renders overcollateralization a key safety component relied upon by decentralized finance (DeFi) protocols. Nonetheless, factors such as price volatility may undermine this mechanism. In order to protect protocols from suffering losses, undercollateralized positions can be liquidated. In this paper, we present the first in-depth empirical analysis of liquidations on protocols for loanable funds (PLFs). We examine Compound, one of the most widely used PLFs, for a period starting from its conception to September 2020. We analyze participants' behavior and risk-appetite in particular, to elucidate recent developments in the dynamics of the protocol. Furthermore, we assess how this has changed with a modification in Compound's incentive structure and show that variations of only 3% in an asset's dollar price can result in over 10m USD becoming liquidable. To further understand the implications of this, we investigate the efficiency of liquidators. We find that liquidators' efficiency has improved significantly over time, with currently over 70% of liquidable positions being immediately liquidated. Lastly, we provide a discussion on how a false sense of security fostered by a misconception of the stability of non-custodial stablecoins, increases the overall liquidation risk faced by Compound participants.
Ashish Rajendra Sai, Jim Buckley, Brian Fitzgerald, Andrew Le Gear
Bitcoin introduced delegation of control over a monetary system from a select few to all who participate in that system. This delegation is known as the decentralization of controlling power and is a powerful security mechanism for the ecosystem. After the introduction of Bitcoin, the field of cryptocurrency has seen widespread attention from industry and academia, so much so that the original novel contribution of Bitcoin, i.e., decentralization, may be overlooked, due to decentralizationsâ assumed fundamental existence for the functioning of such crypto-assets. However, recent studies have observed a trend of increased centralization in cryptocurrencies such as Bitcoin and Ethereum. As this increased centralization has an impact the security of the blockchain, it is crucial that it is measured, towards adequate control. This research derives an initial taxonomy of centralization present in decentralized blockchains through rigorous synthesis using a systematic literature review. This is followed by iterative refinement through expert interviews. We systematically analyzed 89 research papers published between 2009 and 2019. Our study contributes to the existing body of knowledge by highlighting the multiple definitions and measurements of centralization in the literature. We identify different aspects of centralization and propose an encompassing taxonomy of centralization concerns. This taxonomy is based on empirically observable and measurable characteristics. It consists of 13 aspects of centralization, classified over six architectural layers: Governance, Network, Consensus, Incentive, Operational, and Application. We also discuss how the implications of centralization can vary depending on the aspects studied. We believe that this review and taxonomy provides a comprehensive overview of centralization in decentralized blockchains involving various conceptualizations and measures.
The success of blockchain-based solutions, not only on a large scale, but since the beginning of the pilot phase, depends on a set of key factors such as performance, efficiency, usability, scalability. In particular, security, privacy and trust are fundamental, as the use of personal data represents, ultimately, immeasurable impacts on peopleâs lives. This paper presents the results of risk analysis on a blockchain-based self-sovereign identity solution (SSI), focused on the aspects of privacy, security, protection, resilience and reliability. FINID is a blockchain-based SSI solution that aims to create a unique and portable identity that is enriched and used by Braziliansâ financial institutions.
Blockchain technology presents benefits that change the way business partners interact. This new way of establishing democratic trust encourages business owners to think differently. Disaster relief and aid industries are built on the power of collaborating participants. A very high number of participants in different hierarchies, including donors, charities, disaster victims, insurance companies and government agencies interact under extraordinary circumstances of a disaster and hard times. Establishing a new way of trust brings forward a better disaster recovery. In this paper, we propose a blockchain-based ecosystem. The blockchain-based disaster recovery not only would enhance the basic processes around disaster relief, but also promote the willingness of help by transparency and potential fraud prevention. This new blockchain system introduces an opportunity to be more resilient, to react rapidly, to communicate transparently, and to include new contributors such as IoT.
The immutability of blockchains and the transparency of their transaction records would appear to limit the benefit of exploiting them for criminal activity. However, blockchains also offer a high degree of anonymity, similar to fiat paper currency; the technology was intended to facilitate trustless transactions. Coupled with a global, borderless reach, blockchains have become an enabler of cybercrime. They are a new class of assets that, like all other assets, possess security risks and become potential targets of attack. In particular, cryptocurrencies, which depend on blockchain technology, provide significant incentives for attack because of their value. The goals of this chapter are to identify and classify blockchain-based cybercrimes and to explore the avenues for protecting against them at individual, organizational, and policy levels.
Since its inception in 2009, Bitcoin has been mired in controversies for providing a haven for illegal activities. Several types of illicit users hide behind the blanket of anonymity. Uncovering these entities is key for forensic investigations. Current methods utilize machine learning for identifying these illicit entities. However, the existing approaches only focus on a limited category of illicit users. The current paper proposes to address the issue by implementing an ensemble of decision trees for supervised learning. More parameters allow the ensemble model to learn discriminating features that can categorize multiple groups of illicit users from licit users. To evaluate the model, a dataset of 2059 real-life entities on Bitcoin was extracted from the Blockchain. Nine features were engineered to train the model for segregating 28 different licit-illicit categories of users. The proposed model provided a reliable tool for forensic study. Empirical evaluation of the proposed model vis-a-vis three existing benchmark models was performed to highlight its efficacy. Experiments showed that the specificity and sensitivity of the proposed model were comparable to other models.
Purpose The purpose of this study is to describe the opportunities and limitations of cryptocurrencies as a tool for money laundering through six currently available âopen doorsâ (exchange mechanisms). The authors link the regulatory dialectic paradigm to know your customer and anti-money laundering evasion techniques, highlight six tactics to launder funds with virtual assets and investigate potential law enforcement and regulatory alternates used to reduce the incidence of money laundering with digital coins. Design/methodology/approach The methodology used is the analysis of significant recent events and the availability of âfintechâ crime-fighting tools and a literature review focusing on the application of the regulatory dialectic to innovations in existing crypto-asset markets that make them compelling to money launderers. Findings The authors examine the illicit use of cryptocurrency through Kaneâs regulatory dialectic paradigm, identify a number of avenues for crypto to fiat exchange that are still available for those seeking to launder money using digital coins, review recently âclosed doorsâ and make recommendations regarding the regulation of crypto-related markets that may assist in making them less desirable for potential criminals. Research limitations/implications The research is constrained by the state of the market for crypto to fiat exchange as of time of writing; the technology and products to launder money using these open doors is continually changing (as predicted by the regulatory dialectic). Social implications The regulatory dialectic predicts that regulatory response is reactive and often increasingly burdensome or oppressive. There is continuous innovation in the cryptocurrency market, which seeks to preserve privacy and anonymity with which regulators seek to keep up. From a social perspective, the response of bank regulators worldwide to existing open doors for crypto to fiat exchange used for money laundering may prove costly to individuals engaging in legitimate transactions, as well as financial criminals and may also erode the ability of individuals to maintain privacy regarding their financial information. Originality/value To the authorsâ knowledge, there are yet no broad overview regarding the feasibility of money laundering across crypto-related assets within the paradigm of the regulatory dialectic.
Blockocracies are a coherent, distinctive and novel organizational form bound by a collective ledger and a cryptocurrency. We frame our analysis of blockocracies against Weberâs enduring description of bureaucracy, identifying those features of Weberian bureaucracies that are present, absent or marginalized in blockocracies. In contrast to bureaucracyâs monocratic authority structure, authority in blockocracies is centered on four distinct layers. In each layer, there is governance of the code and governance by the code, and in the latter we distinguish between endogenous and exogenous rules. We also compare the âblockocratâ with Weberâs depiction of the bureaucrat.
Financial Intelligence Units (FIUs) hold a central position in the chain of actors responsible for the monitoring of money movements in the European Union. In support of their role, which is to receive, analyse and disseminate suspicious transaction reports, they have been furnished with significant information processing powers. At present, FIUs feature prominently in the EUâs anti-money laundering and counterterrorist financing agendas and plans to further enhance their powers of information exchange are underway. At the same time, however, the legal challenges that arise from their constant empowerment, particularly for the protection of personal data, are being overlooked. This article focuses on the cooperation between FIUs in the EU and argues that the latter takes place under a complex legal framework, which raises significant challenges for data protection. In particular, it highlights the present-day uncertainty over the data protection framework that governs their operations and discusses whether FIUs should be subject to the General Data Protection Regulation or to its law enforcement counterpart, the Police Data Protection Directive. The remaining of the article focuses on the â FIU.net â â the decentralized network for information exchanges between EU FIUs â and on the data protection challenges that emerged from the recent integration of this network into Europol.
While blockchain was designed as a ledger for cryptocurrency transactions, it can record transactions of anything of value. Blockchain is increasingly used to prove the integrity of commodities, tracing their supply chain journey from the source to the end user. Yet, transferring this technology from a cryptocurrency context to a supply chain setting is not without difficulties. This article explores the implications for multinational and transnational companies in using blockchain as a means to address modern slavery. The research identifies five challenges: verification, inclusion, trust, privacy, and normativity.
Erdinç Akyıldırım, Shaen Corbet, Douglas J. Cumming, Brian M. Lucey · 5 authors
Cryptocurrencies have been broadly scrutinised in recent times for a host of concerning regulatory and cybercriminality issues. Although steps have been taken to promote regulatory sufficiency in the near future, we examine the avenues through which this extremely high-risk industry can derive potentially devastating contagion channels, influencing both unwilling and unsuspecting investors. We focus this research on the expressions of interest by publicly traded companies across the world to utilise cryptocurrency and blockchain projects. We find evidence that there exists a substantial stock price premium and sustained increase in volatility in the aftermath of blockchain announcements, with emphasis on highly-speculative motives such as coin creation and corporate name changes. Changes in price discovery and information flows are found to be largely determined from cryptocurrency-based pricing sources in the aftermath of speculative announcements. We discuss the inherent ethical and legal issues, considering as to whether such announcements are simply an attempt to artificially manipulate share prices and take part in the current phase of crypto-exuberance.
Purpose The purpose of this paper is to tackle the most pressing issues confronting global anti-money laundering (AML) efforts, particularly, the implications of the Brexit from EU and the increasing association of bitcoin and cryptocurrencies with crimes. Design/methodology/approach This paper will evaluate the implications of Brexit to AML efforts and the threat that cryptocurrencies like bitcoin pose to the financial system. Findings Instead of banning trade and other transactions using BTC and other cryptocurrencies, financial experts, with the able assistance of IT and mining experts, from all over the world need to convene and tailor an effective regulatory framework. Solid cooperation among the international community, supported by unitary standards and procedures, will help boost the worlds AML/combatting the financing of terrorism (CFT) efforts. As an added bonus, effective regulation, monitoring and control can facilitate more efficient tax collection. Originality/value Recommendations were advanced about the future of AML/CFT efforts and the need for internationally holistic approaches in combatting these twin scourges on all economies.
Clients of permissionless blockchain systems, like Bitcoin, rely on an underlying peer-to-peer network to send and receive transactions. It is critical that a client is connected to at least one honest peer, as otherwise the client can be convinced to accept a maliciously forked view of the blockchain. In such an eclipse attack, the client is unable to reliably distinguish the canonical view of the blockchain from the view provided by the attacker. The consequences of this can be catastrophic if the client makes business decisions based on a distorted view of the blockchain transactions. In this paper, we investigate the design space and propose two approaches for Bitcoin clients to detect whether an eclipse attack against them is ongoing. Each approach chooses a different trade-off between average attack detection time and network load. The first scheme is based on the detection of suspicious block timestamps. The second scheme allows blockchain clients to utilize their natural connections to the Internet (i.e., standard web activity) to gossip about their blockchain views with contacted servers and their other clients. Our proposals improve upon previously proposed eclipse attack countermeasures without introducing any dedicated infrastructure or changes to the Bitcoin protocol and network, and we discuss an implementation. We demonstrate the effectiveness of the gossip-based schemes through rigorous analysis using original Internet traffic traces and real-world deployment. The results indicate that our protocol incurs a negligible overhead and detects eclipse attacks rapidly with high probability, and is well-suited for practical deployment.
Purpose The purpose of this paper is to investigate available forensic data on the Bitcoin blockchain to identify typical transaction patterns of ransomware attacks. Specifically, the authors explore how distinct these patterns are and their potential value for intelligence exploitation in support of countering ransomware attacks. Design/methodology/approach The authors created an analytic framework â the RansomwareâBitcoin IntelligenceâForensic Continuum framework â to search for transaction patterns in the blockchain records from actual ransomware attacks. Data of a number of different ransomware Bitcoin addresses was extracted to populate the framework, via the WalletExplorer.com programming interface. This data was then assembled in a representation of the target network for pattern analysis on the input (cash-in) and output (cash-out) side of the ransomware seed addresses. Different graph algorithms were applied to these networks. The results were compared to a âcontrolâ network derived from a Bitcoin charity. Findings The findings show discernible patterns in the network relating to the input and output side of the ransomware graphs. However, these patterns are not easily distinguishable from those associated with the charity Bitcoin address on the input side. Nonetheless, the collection profile over time is more volatile than with the charity Bitcoin address. On the other hand, ransomware output patterns differ from those associated charity addresses, as the attacker cash-out tactics are quite different from the way charities mobilise their donations. We further argue that an application of graph machine learning provides a basis for future analysis and data refinement possibilities. Research limitations/implications Limitations are evident in the sample size of data taken on ransomware campaigns and the âcontrolâ subject. Further analysis of additional ransomware campaigns and âcontrolâ subjects over time would help refine and validate the preliminary observations in this paper. Future research will also benefit from the application of more powerful computing resources and analytics platforms that scale with the amount of data being collected. Originality/value This research contributes to the maturity of the field by analysing ransomware-Bitcoin behaviour using the RansomwareâBitcoin IntelligenceâForensic Continuum. By combining several different techniques to discerning patterns of ransomware activity on the Bitcoin network, it provides insight into whether a ransomware attack is occurring and could be used to trigger alerts to seek additional evidence of attack, or could corroborate other information in the system.
Globalization has created an environment where access to advances in technology, communications, and transportation transcends international boundaries creating a rich operating space for threat networks to exploit. Threat networks around the globe leverage their geographically dispersed structures to increase reach while mitigating risks to disruption by operating in various environments and domains. Like all networked organizations, threat networks rely on financial structures that are sustainable while providing resilience when faced with inevitable disruption. Increasingly, threat networks pursue options that are decentralized and rely on local placement and access for resourcing while exploiting gaps in various environments in order to establish and maintain their financial systems. Defining the system threat networks use to finance their operations in a clear and repeatable construct supports the common understanding that leads to unified action.
Abstract Financial sanctions and trade control regulations are becoming increasingly relevant in the current global situation, characterized by multiple ongoing conflicts, sophisticated terrorist organizations, and serious tensions between international economic actors. The United Nations, under Article 41 of the UN Charter, the United States and Europe, in particular, regularly introduce and enforce a number of tools (e. g., financial restrictions, trade restrictions, arms embargoes, travel bans). Such tools can range from comprehensive, âterritory-wideâ sanctions against States to targeted sanctions on entities, including individuals, to obtain a change in policy or activity by the target country, part of the country, governments, entities, and individuals, with the ultimate aim of pursuing peace, human rights, democracy, and respect for the rule of law. Recent history proves that companies and financial institutions have adopted complex solutions to conceal transactions with sanctioned countries and entities. Blockchain technology, through the use of distributed digitalized ledgers, grants a high level of transparency, for instance providing real-time updates on the development of an export transaction, and the use of smart contracts can automatize payments, or the triggering of guarantees, etc. While blockchain technology continues to be an area of enormous promise, it is also one of risk, if not managed properly, especially in a transnational, multi-jurisdictional context. Not only might the use of blockchain-based solutions prove challenging for the enforcement of current international sanctions programs by competent authorities, but blockchain technologies developers will also need to set up solutions suitable to comply with the requirements imposed by the various sanctions in place. At the same time, businesses will need to determine which measures and due diligence practices are needed to protect against the risks of a sanctions violation, which can result in significant fines and even criminal penalties. This paper explores how blockchain solutions that may be implemented in the context of international financial and commercial transactions can interrelate (or interfere) with sanctions enforcement and sanctions compliance, highlighting the multiple legal issues that may arise in connection thereto.
Bitcoin was designed to be a decentralized global electronic payment system that does not require verification by a third-party intermediary platform and can be used by anyone originally. Due to its anonymity and globalization, bitcoin has achieved great success and attracted the attention of various illegal traders. In recent years, the number of illegal transactions of bitcoin has been increasing. Although bitcoin can support a certain amount of privacy, the bitcoin users and entity information can be linked by tracking the on-chain information of bitcoin users and combining the public off-chain information. Through bitcoin users de-anonymization, we can obtain some valuable intelligence information, which plays an important role in combating bitcoin-related crimes. In this paper, we build a visual analysis system for bitcoin transactions based on a graph database and use real-world multi-dimensional data sources to analyze the entity information of bitcoin transactions on the chain to achieve the effect of de-anonymization. Besides, we adopt a supervised learning method in our system to predict the legitimacy of unknown bitcoin transactions. Experiments and analyses show that our system can achieve good correlation analysis and de-anonymization. Finally, we put forward the future research direction of the bitcoin de-anonymization field.
With the advance of Bitcoin technology, money laundering has been incentivised as a den of Bitcoin blockchain, in which the user's identity is hidden behind a pseudonym known as address. Although this trait permits concealing in the plain sight, the public ledger of Bitcoin blockchain provides more power for investigators and allows collective intelligence for anti-money laundering and forensic analysis. This fascinating paradox arises in the strength of Bitcoin technology. Machine learning techniques have attained promising results in forensic analysis, in order to spot suspicious behaviour in Bitcoin blockchain. This paper presents a comparative analysis of the performance of classical supervised learning methods using a recently published data set derived from Bitcoin blockchain, to predict licit and illicit transactions in the network. Besides, an ensemble learning method is utilised using a combination of the given supervised learning models, which outperforms the given classical methods. This experiment is performed using a newly published data set derived from Bitcoin blockchain. Our main contribution points out that using ensemble learning approach outperforms the performance of the classical learning models used in the original paper, using Elliptic data set, a time series of Bitcoin transaction graph with node transactions and directed payments flow edges. Using the same data set, we show that we are able to predict licit/illicit transactions with an accuracy of 98.13% and F1 score equals to 83.36% using the proposed method. We discuss the variety of supervised learning methods, and their capabilities of assisting forensic analysis, and propose future work directions.
Purpose The purpose of this paper is to illustrate how cryptocurrencies are being used as a vehicle for financial crime (such as money laundering, terrorist financing and corruption) and propose a more effective international standard for regulation that uses the Liechtenstein blockchain act as a benchmark. Design/methodology/approach This paper investigates how cryptocurrencies facilitate financial crime through a qualitative study consisting of interviews with 10 presumed providers of illegal financial services and 18 international compliance experts. Findings This study shows that cryptocurrencies are a highly suitable vehicle for money laundering, terrorist financing and corruption and that current compliance efforts in the cryptocurrency sector are ineffective. Research limitations/implications The presented findings illustrate that for a more effective combat of financial crime via cryptocurrency, an international standard for blockchain and cryptocurrency regulation must be created. This paper suggests that Liechtensteinâs innovative and comprehensive blockchain act could be used as a basis for said standard. Practitioners should also consider cooperating transnationally when prosecuting financial crime via cryptocurrency. Originality/value The fact that cryptocurrencies facilitate financial crime is widely known. However, this study combines the perspectives of both compliance experts and presumed criminals to gain a comprehensive understanding of the techniques that money launderers, terrorist financiers and corrupt public officials use. This paper examines the potential for the innovative Liechtenstein blockchain act, which has, thus, far not received empirical attention, to set the benchmark for international regulations.
With the advance of Bitcoin technology, money laundering has been incentivised as a den of Bitcoin blockchain, in which the user's identity is hidden behind a pseudonym known as address. Although this trait permits concealing in the plain sight, the public ledger of Bitcoin blockchain provides more power for investigators and allows collective intelligence for anti-money laundering and forensic analysis. This fascinating paradox arises in the strength of Bitcoin technology. Machine learning techniques have attained promising results in forensic analysis, in order to spot suspicious behaviour in Bitcoin blockchain. This paper presents a comparative analysis of the performance of classical supervised learning methods using a recently published data set derived from Bitcoin blockchain, to predict licit and illicit transactions in the network. Besides, an ensemble learning method is utilised using a combination of the given supervised learning models, which outperforms the given classical methods. This experiment is performed using a newly published data set derived from Bitcoin blockchain. Our main contribution points out that using ensemble learning approach outperforms the performance of the classical learning models used in the original paper, using Elliptic data set, a time series of Bitcoin transaction graph with node transactions and directed payments flow edges. Using the same data set, we show that we are able to predict licit/illicit transactions with an accuracy of 98.13% and F1 score equals to 83.36% using the proposed method. We discuss the variety of supervised learning methods, and their capabilities of assisting forensic analysis, and propose future work directions.
Graph networks are extensively used as an essential framework to analyse the interconnections between transactions and capture illicit behaviour in Bitcoin blockchain. Due to the complexity of Bitcoin transaction graph, the prediction of illicit transactions has become a challenging problem to unveil illicit services over the network. Graph Convolutional Network, a graph neural network based spectral approach, has recently emerged and gained much attention regarding graph-structured data. Previous research has highlighted the degraded performance of the latter approach to predict illicit transactions using, a Bitcoin transaction graph, so-called Elliptic data derived from Bitcoin blockchain. Motivated by the previous work, we seek to explore graph convolutions in a novel way. For this purpose, we present a novel approach that is modelled using the existing Graph Convolutional Network intertwined with linear layers. Concisely, we concatenate node embeddings obtained from graph convolutional layers with a single hidden layer derived from the linear transformation of the node feature matrix and followed by Multi-layer Perceptron. Our approach is evaluated using Elliptic data, wherein efficient accuracy is yielded. The proposed approach outperforms the original work of same data set.