Este artículo explora los desafíos de la gobernanza algorítmica utilizando el estudio de caso de The DAO, una efímera tentativa de crear una organización autónoma descentralizada en la plataforma de blockchain Ethereum. A pesar de su breve existencia y la significativa pérdida de inversión debido a una explotación de seguridad, The DAO ofrece ideas críticas sobre las formas emergentes de autoridad algorítmica, la gobernanza práctica de sistemas autónomos y descentralizados, y las posibles fallas en el diseño de incentivos y la modelización de acciones. El artículo también profundiza en el problema de agencia en economía y la gobernanza corporativa, ilustrando cómo estos conceptos se entrelazan con la gobernanza algorítmica.
Dimitris Kafteranis, Hüseyin Ünözkan, Umut Türkşen
Non-Fungible Tokens (NFTs) is a new virtual asset phenomenon the trade of which has spread quickly without any regulationas no legislation has been adopted in the EU, USA or the UK where the majority of NFT trading takes place. Concerns have been raised about NFTs and their relation to fraud and money launderingasanonymity and price volatility of NFTs create a unique and profitable asset for criminals. This paper addressestwomain issues: (1) trading statistics on NFTs,their analysis,and if and to what extent NFTs are used for financial crimes purposes; and (2) the legal challenges posed by the misuse of NFTs for fraud and other economic crimes. The final section of this paper provides feasible regulatory and business solutions that can help businesses to mitigate risks emanating from NFTs. It is argued that legal scholars, businesses and/or regulators cannot solve the challenges and risks posed by NFTs on their own, requiringmultidisciplinary research from academia and knowledge exchange between private and public stakeholders to close this gap.
In order for tokenized asset networks to be accountable as Web3 marketplaces for token-related transactions, identity verification must be conducted by gateways into those token networks. This includes the identity validation and legal status verification of the originators and beneficiaries, the gateway owners/operators, and other relevant service providers. The classic identity provider model could be enhanced to support anti-money laundering regulations, notably the Travel Rule. A privacy-preserving IdP model in combination with a legal service provider is explored where the IdP becomes the issuer of a blinded attestation regarding the user attribute, and where the legal representative with attorney-client privilege becomes the first point of contact for requests for the disclosure of the blinded attestations.
Decentralized Finance (DeFi) heralds a transformative moment in the realm of finance, challenging traditional intermediaries with a blockchain-centric blueprint. As DeFi burgeons, the intricate dance between its evolution and security emerges as an area of pivotal significance. This workshop navigates the multifaceted landscape of DeFi, where inherent challenges intertwine with new vulnerabilities, emphasizing the necessity for vigilant evaluations and adaptive measures to ensure the integrity of the ecosystem. It further delves into the ripple effects of regulatory scrutiny and its subsequent influence on DeFi's security matrix. As we stand on the cusp of uncharted territories, the workshop aims to provide a comprehensive discourse on DeFi's security challenges, fortified by interdisciplinary expertise, inviting participants to explore, ideate, and collaboratively forge a path towards a robust and secure DeFi paradigm.
Cryptomarkets are increasingly requiring users to purchase products with Monero (a ‘privacy coin’) to further obfuscate the digital trail of money compared to conventional cryptocurrencies (e.g. Bitcoin). This is the first study to explore how cryptomarket communities are used to facilitate norms and behaviours to expedite these emerging cryptocurrency practices. Through a qualitative analysis of Monero threads in a Reddit cryptomarket community (3451 total posts), this research illustrates how online communities often underpin the adoption of new technologies in cryptomarkets. The findings reveal: how the online community functions, adapts, and fails to support cryptocurrency transitions; the appraisal and contestation of cryptocurrency risks; and the ideological drivers and symbolic resources used to align community practices to adopt Monero. This research contributes to an understanding of the processes that underpin the constant evolution of online illicit markets as human and non-human elements are constantly re-assembled.
This study presented a literature review on the main issue associated with the impact of Bitcoin on the financial market and the new technological development that has reformed market interaction within society. Currently, the usefulness of digital currency has expanded financial trading. Bitcoin is considered the most significant global cryptocurrency due to its significant market recognition and technological nature. Although various studies have explained the merits and demerits of Bitcoin, most studies emphasised its influence and relationship with the financial market. Hence, this study also addressed this gap. An instrument and concepts were provided to comprehend the dynamics of Bitcoin and determine its function in the financial market. Moreover, the global meaning and function of Bitcoin were examined. Virtual and previous literature reviews highlighted a strong relationship between Bitcoin and the financial market with other cryptocurrencies. Summarily, Bitcoin is still in the early stage and needs to be developed through technological growth.
Abstract The vulnerability in smart contracts (SCs) on the blockchain system may lead to severe security compromises. The SC can be invoked from an externally owned account (EOA) or a contract account (CA). The account a user creates to receive or send ether is an EOA. A CA contains codes that can interact with SCs. In Solidity SC, some vulnerabilities can only be exploited by the interactions between CAs and vulnerable SCs, which can be named external‐risky vulnerabilities . Most state‐of‐the‐art (SOTA) detectors detect external‐risky vulnerabilities by executing contract codes as an EOA user, thus reporting many unexploitable vulnerabilities. Therefore, we propose a CA‐triggering method to identify exploitable external‐risky vulnerabilities in Solidity SCs. We first designed agent contracts to simulate CAs' interactions with the target SCs in the real blockchain environment. We then detect vulnerability exploitation by analyzing transaction logs between agent contracts and target SCs and identifying successful exploits. We implemented the CA‐triggering method in a tool named SoliTester and evaluated it using three benchmark datasets, which contain three types of external‐risky vulnerabilities, namely, Reentancy (RE), Unchecked Call (UcC), and TxOrigin (TO). The results show that SoliTester can efficiently detect exploitable external‐risky vulnerabilities with significantly better precisions and recalls than SOTA detectors.
This paper provides a contextual analysis of the adoption of Bitcoin as legal tender in El Salvador. First, we outline the historical context and the political situation of the period 2019–24 that serve as context for the passage and implementation of the Bitcoin law (Decree No. 57). We identify the institutional and political context and the main areas of contention. Next, we delve into the macroeconomic context of El Salvador, outlining the fundamental features of its economy and highlighting how they relate to currency issues. Our analysis reveals that the adoption of Bitcoin cannot be understood without factoring in the mounting strains surrounding dollarization, remittances, and foreign debt. We conclude by putting forth a set of hypotheses regarding the potential dynamics and future of Bitcoin as legal tender in El Salvador that point beyond Bukele’s tactics.
In the face of increasingly sophisticated cybercrime threats, large companies now need innovative solutions to protect their data and interests. Blockchain technology, which is based on the principles of decentralization and strong encryption, offers great potential in improving corporate cybersecurity. This research investigates the implementation of blockchain technology in the context of enterprise security by developing a blockchain-based dynamic system model. These findings make an important contribution in changing the way audits and general accounting operations are carried out, presenting fundamental changes in the profession. This new approach integrates blockchain technology into various aspects of cybersecurity, embracing innovation and creativity in the face of current challenges. By creating accurate computer models, this research brings a breakthrough in understanding system responses to employee fraud in corporate environments that adopt blockchain technology. This research aims to explore the potential of blockchain technology in improving corporate cybersecurity by identifying security gaps and designing effective updates, creating a safe and trustworthy digital environment for companies in this digital era. The findings of this research highlight the importance of integrating blockchain technology in auditing and general accounting operations, creating a foundation for the development of robust cybersecurity systems. In the context of companies using blockchain technology, this research reveals improved system responsiveness to employee fraud, indicating positive potential in mitigating security risks. This research provides a solid foundation for further development in the field of enterprise cybersecurity, inspiring innovation in protecting businesses and digital assets in a rapidly evolving cyberspace.
Abstract This chapter assesses the risks of money laundering and financing of terrorism (MLFT) via the Metaverse. This chapter starts in Sect. 4.2 by discussing the three traditional stages of MLFT in relation to the Metaverse. These stages are placement, layering and integration. In addition to the traditional stages of MLFT, the Metaverse poses additional risks to MLFT. The first additional risk identified in this book is the possibility of entering and creating realities fully anonymously. The threats of anonymity in the Metaverse will be discussed in Sect. 4.3.1. The second additional risk is that of the lack of jurisdiction in the Metaverse. This will be further explored in Sect. 4.3.2. The third and final additional risk relating to the Metaverse is that of the use of Non-Fungible Tokens (NFTs). These will be discussed in Sect. 4.3.3.
The emergence of the Bitcoin cryptocurrency marked a new era of illegal transactions. Cryptocurrency provides some level of anonymity allowing its users to create an unlimited number of wallets with alias addresses, which makes it challenging to identify the actual user. This is used by criminals for the purpose of making illegal transactions. At the same time, Bitcoin stores and provides information about all committed transactions, which opens up opportunities for identifying suspicious behavior patterns in this network using data mining. The problem of detecting suspicious activity in the Bitcoin network can be solved with sufficiently high accuracy using machine learning methods. The paper provides a comparative study of various machine learning methods to solve the mentioned problem: logistic regression, decision tree, random forest, gradient boosting.. Selecting hyper parameters, rebalancing the dataset, and active learning are particularly important. The most important hyperparameters of the algorithms are described. Metrics show that the gradient boosting looks the most promising. In total 38 features of bitcoin addresses were identified. The top features are presented in the paper.
Delegated-Proof-of-Stake (DPoS) blockchains, such as EOSIO, Steem and TRON, are governed by a committee of block producers elected via a coin-based voting system. We recently witnessed the first de facto blockchain takeover that happened between Steem and TRON. Within one hour of this incident, TRON founder took over the entire Steem committee, forcing the original Steem community to leave the blockchain that they maintained for years. This is a historical event in the evolution of blockchains and Web 3.0. Despite its significant disruptive impact, little is known about how vulnerable DPoS blockchains are in general to takeovers and the ways in which we can improve their resistance to takeovers. In this paper, we demonstrate that the resistance of a DPoS blockchain to takeovers is governed by both the theoretical design and the actual use of its underlying coin-based voting governance system. When voters actively cooperate to resist potential takeovers, our theoretical analysis reveals that the current active resistance of DPoS blockchains is far below the theoretical upper bound. However in practice, voter preferences could be significantly different. This paper presents the first large-scale empirical study of the passive takeover resistance of EOSIO, Steem and TRON. Our study identifies the diversity in voter preferences and characterizes the impact of this diversity on takeover resistance. Through both theoretical and empirical analyses, our study provides novel insights into the security of coin-based voting governance and suggests potential ways to improve the takeover resistance of any blockchain that implements this governance model.
In recent years, blockchain technology has witnessed rapid development and received considerable attention. However, its decentralized and pseudonymous nature has also attracted many criminal activities. Among them, Ponzi schemes, a classic form of financial fraud, also hide their true face in smart contracts, causing huge losses to blockchain users. Although numerous methods have been proposed to detect Ponzi contracts, these methods still have limitations in terms of generalization and feature learning. To address this issue, we conduct research on Ethereum, the currently largest blockchain platform enabling smart contracts, and propose a novel contrastive learning-based smart Ponzi scheme detection method named ContraPonzi. This method first extracts control flow graph information from bytecodes and models it as attribute graphs that preserve both semantic and structural information. Next, by augmenting the bytecode data of multi-version compilers and maximizing the graph representation similarity of multi-version bytecodes of the same contract, a pre-training graph encoder is obtained and then can be used in Ponzi contract detection. Experimental results on real-world data demonstrate that ContraPonzi is significantly superior to the state-of-the-art in Ethereum Ponzi scheme detection.
El Bitcoin es una criptomoneda creada en 2009 por su seudónimo creador Satoshi Nakamoto, es una moneda virtual e intangible, no la podemos tocar como a los billetes y monedas que se utilizan en el día a día, pero el Bitocoin también puede ser utilizado como un método de pago al igual que estos. Por medio de esta investigación se pretende dar a conocer los conceptos básicos sobre el Bitcoin, para que el público en general los conozca y ya no exista una desconfianza o desconocimiento hacia esta criptomoneda y hacia las demás criptomonedas que existen.
Cryptocurrencies, like Bitcoin and Ethereum, have garnered global attention in recent years as digital alternatives to traditional fiat currencies. This paper explores the complex landscape of cryptocurrency adoption, consumer behavior, and perceptions. Beginning with the origin of cryptocurrencies and the dominance of Bitcoin with its USD 1.23 trillion market capitalization, the paper highlights popular online platforms facilitating Bitcoin trading. It also examines the varying legal statuses and regulations across different countries, with a notable divide between Eastern and Western nations, attributed to factors like wealth, risk tolerance, and government restrictions. The role of blockchain technology as the foundation of cryptocurrencies is explained, emphasizing its role in ensuring secure and transparent transactions. The paper delves into the processes involved in handling cryptocurrencies, including the blockchain, exchanges, wallets, and mining. Consumer behavior and the factors influencing cryptocurrency usage are analyzed, with a focus on speculation, algorithm trust, spending power, and demographics. Survey findings and case studies from diverse geographical areas reveal patterns of adoption and local consumer perceptions. The paper concludes by discussing the cryptocurrency market’s inherent volatility and sensitivity to regulatory changes, as well as the different types of cryptocurrencies and online exchanges shaping this evolving financial landscape. Overall, it offers insights into the complex dynamics surrounding cryptocurrency adoption and its potential impact on global finance.
Natural or man-made disasters pose significant challenges for delivering critical relief to affected populations due to disruptions in critical infrastructures and logistics networks. Unmanned aerial vehicles (UAVs)-aided disaster relief networks (UDRNs) leverage UAVs to assist existing ground relief networks by swiftly assessing affected areas and timely delivering lifesaving supplies. To meet the growing demands for collaborative, trust-free, and transparent UDRN services, blockchain-based UDRNs emerge as a promising approach through immutable ledgers and distributed smart contracts. However, several efficiency and security challenges hinder the deployment of blockchain-based UDRNs, including the lack of cooperation between smart contracts, lack of dynamic audit for smart contract vulnerabilities, and low forensics robustness against transaction malleability attacks. Towards efficient and secure blockchain-based UDRNs, this paper presents potential solutions: (i) a series of collaborative smart contracts for coordinated relief management, (ii) a dynamic contract audit mechanism to prevent known/unknown contract vulnerabilities; and (iii) a robust transaction forensics strategy with on/off-chain cooperation to resist transaction malleability attacks. Our prototype implementation and experimental results demonstrate the feasibility and effectiveness of our approach. Lastly, we outline key open research issues crucial to advancing this emerging field.
Pedro Henrique Resende Ribeiro, Pedro Leale, Ivan da Silva Sendin
Over recent decades, the global financial paradigm has experienced significant transformations, notably the emergence and adoption of cryptocurrencies. The escalating prominence of assets like Bitcoin has inadvertently catalysed a surge in illicit activities associated with the currency. Consequently, the forensic examination of transactions within blockchains becomes imperative for the detection and surveillance of malevolent undertakings. This research delineates a preliminary pipeline for a Bitcoin forensic analysis tool. Moving forward, the ambition is to conceptualize and empirically validate this tool utilizing data procured from blockchain and ancillary sources. The methodology will harness Open-Source Intelligence (OSINT), clustering of Bitcoin addresses, and an exhaustive financial analysis. Upon finalizing the pipeline, the implementation of an open-source instrument is envisioned, poised to confer substantial advantages to the broader cryptocurrency milieu.
Catherine Carpentier-Desjardins, Masarah Paquet-Clouston, Stefan Kitzler, Bernhard Haslhofer
*PLEASE REFER TO THE SECOND VERSION UPLOADED IN JANUARY 2025. THIS VERSION CONTAINS A FEW DUPLICATES. VERSION 2 IS AVAILABLE FOR DOWNLOAD HERE: https://zenodo.org/records/14706760 README - Crime Events Dataset This document provides a detailed overview of the structure of the dataset for the paper: "Mapping the DeFi crime landscape: An Evidence-based Picture". The following fields are included, each representing different aspects of the events collected. Data Fields 1. unique_key Description: A unique number assigned to identify each event in the dataset. 2. Agregators Description: The sources where the event is listed. Aggregators include: - De.Fi REKT - SlowMist - CryptoSec (rebranded to ChainSec as of February 2023) 3. DeFi actor involved Description: The name of the DeFi actor involved in the event (target, perpetrator, or intermediary). Sources: - On De.Fi REKT: Found as the "Title" of the event’s listing. - On SlowMist: Found under the “Hacked target” title. - On CryptoSec: Found in the "Title" of the event’s listing with the date. 4. REKT URL Description: The URL to the event's listing on De.Fi REKT. Process: Found by searching for the DeFi actor involved in the REKT Database: https://de.fi/rekt-database 5. SlowMist URL Description: The URL to the event's listing on SlowMist. Process: Available via https://hacked.slowmist.io/search/. Note that searching the actor's name will lead to the event but without an individualized URL. 6. CryptoSec URL Description: The URL to the event's listing on CryptoSec. Process: Found at https://chainsec.io/defi-hacks/. Events are listed on a single page; use traditional keyboard search to locate specific events. 7. Aggregator Summary Description: A summary of the event provided by the aggregator. Sources: - On De.Fi REKT: Found under "Quick Summary" and "Details of the Exploit". - On SlowMist: Under "Description of the event". - On CryptoSec: Below the title in quotation marks. 8. Aggregator sources URL Description: The URLs of references linked by the aggregator in the event’s listing. Sources: - On De.Fi REKT: Found at the bottom by clicking "Source" or "Archived link". - On SlowMist: Found by clicking "View Reference Sources". - On CryptoSec: Available by clicking the source’s name at the end of the summary. 9. Event date Description: The date the event occurred. Sources: - On De.Fi REKT: Listed under the "Date" field. - On SlowMist: At the top right of the listing. - On CryptoSec: Listed in parentheses behind the actor’s name. 10. Event year Description: The year the event occurred, extracted from the Event date. 11. Stolen amount USD Description: The total amount stolen, converted to USD. Sources: - On De.Fi REKT: Found under "Funds lost". - On SlowMist: Under the title “Amount of loss”. - On CryptoSec: Behind the title "Amount stolen". Note: If needed, conversions were manually performed using CoinMarketCap’s historical data as explained in the paper. 12. Implication of actor Description: Indicates whether the DeFi actor was a target, perpetrator, or intermediary in the event. Manually coded after reviewing the aggregator’s summary and linked sources. 13. Strategy Description: The main approach used to steal funds. Six categories are possible: Technical vulnerability, Human risks, Undetermined, Malicious use of contract, Misappropriation of funds, and Imitation. This was manually coded from the event summary and sources. 14. General tactic Description: The common techniques or methods used by malicious actors. Eleven categories are possible, defined in the appendix. Manually coded after reviewing the summary and linked sources. 15. Specific tactic Description: The precise technique used to commit the crime. Thirty-seven categories are possible, defined in the appendix. This was manually coded based on the event summary and sources. 16. Paper category Description: The main area of operation of the involved DeFi actor. Twelve categories are possible: Blockchain, Bridge, DApp, Derivatives, Exchange, Fungible Token (FT), Non-Fungible Token (NFT), Oracle, Yield, Staking, and Others. This was determined by the event summary and research on the actor. 17. Stack category Description: The technical layer of the DeFi Stack Reference (DSR) model corresponding to the paper category. Five categories are possible: DeFi Compositions (CP), DeFi Protocols (P), Cryptoassets (CA), Distributed Ledger Technology (DLT), and Interfaces (INT). --- For more detailed information on the tactics, strategies, or categories used, please refer to the appendix of the dataset or the associated documentation.
In the aftermath of the 2008 global financial crisis, the foundation was developed for the protocol we know as blockchain. Blockchain is a system through which money can be sent from one person to another without using any financial service providers. It has been argued that the security of blockchain technology could be the answer to people’s mistrust and lost confidence in the financial market. However, blockchain technology and cryptocurrencies go much further than a mere transfer of money. New forms of value exchange have been created, in addition to providing access to financial services in locations where only limited services are offered by traditional banks. As the use of such innovation gains popularity, the legislator is slowly catching up, but how much is covered? This paper studies FinTech and cryptocurrencies in respect of money laundering. It is found that the current regulatory landscape has several weaknesses that can be attractive for those wishing to exploit them.
W. D. Li, Zhun Wang, Chenyu Li, H. F. Chen · 8 authors
The rapid growth and adoption of decentralized finance (DeFi) systems have been accompanied by various threats, notably those emerging from vulnerabilities in their intricate design. In our work, we introduce and define an attack strategy termed as Role-Play Attack, in which the attacker acts as multiple roles concurrently to exploit the DeFi system and cause substantial financial losses. We provide a formal definition of this strategy and demonstrate its potential impacts by revealing the total loss of \$435.1M caused by 14 historical attacks with applying this pattern. Besides, we mathematically analyzed the attacks with top 2 losses and retrofitted the corresponding attack pattern by concrete execution, indicating that this strategy could increase the potential profit for original attacks by \$3.34M (51.4%) and \$3.76M (12.0%), respectively.
Cryptocurrency has been subject to illicit activities probably more often than traditional financial assets due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all three critical properties of (I) early detection, (II) good interpretability, and (III) versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without interpretability and are only available for retrospective analysis of a specific illicit type. To tackle all these challenges, we propose Intention-Monitor for early malice detection in Bitcoin (BTC), where the on-chain record data for a certain address are much scarcer than other cryptocurrency platforms. We first define asset transfer paths with the Decision-Tree based feature Selection and Complement (DT-SC) to build different feature sets for different malice types. Then, the Status/Action Proposal Module (S/A-PM) and the Intention-VAE module generate the status, action, intent-snippet, and hidden intent-snippet embedding. With all these modules, our model is highly interpretable and can detect various illegal activities. Moreover, well-designed loss functions further enhance the prediction speed and model's interpretability. Extensive experiments on three real-world datasets demonstrate that our proposed algorithm outperforms the state-of-the-art methods. Furthermore, additional case studies justify our model can not only explain existing illicit patterns but can also find new suspicious characters.
Decentralized Exchanges (DEXs) are one of the most important infrastructures in the world of Decentralized Finance (DeFi) and are generally considered more reliable than centralized exchanges (CEXs). However, some well-known decentralized exchanges (e.g., Uniswap) allow the deployment of any unaudited ERC20 tokens, resulting in the creation of numerous honeypot traps designed to steal traders' assets: traders can exchange valuable assets (e.g., ETH) for fraudulent tokens in liquidity pools but are unable to exchange them back for the original assets. In this paper, we introduce honeypot traps on decentralized exchanges and provide a taxonomy for these traps according to the attack effect. For different types of traps, we design a detection scheme based on historical data analysis and transaction simulation. We randomly select 10,000 pools from Uniswap V2 & V3, and then utilize our method to check these pools. Finally, we discover 8,443 abnormal pools, which shows that honeypot traps may exist widely in exchanges like Uniswap. Furthermore, we discuss possible mitigation and defense strategies to protect traders' assets.
Duzgun Kucuk, Emre ÇAKAR, Ömer Faruk Yakut, Fatih Ertam
One of the biggest innovations brought by the digitalized world is undoubtedly the invention of crypto money, which is decentralized, anonymous and complex, and connected to blockchain technology. This relatively new technology has attracted the attention of many people with its revolutionary changes in payment systems and great price movements in the market, as well as the economic balances it has changed around the world. In addition to this interest, it also attracted the attention of crime and crime organizations in a short time, and over time it turned into a tool used by illegal organizations such as laundering the proceeds of crime. Although this structure was initially exposed to the reaction of some states at the level of nation states, on the other hand, it managed to get the support of many states. However, although the spread of blockchain-based money laundering methods is an undeniable problem for all states, a significant cooperation has not been achieved by international collaborations and organizations to prevent this situation. On the other hand, it is of great importance for law enforcement and forensic analysts to clarify this situation and to fight against these structures in order to protect national interests. In this study; In this study, an approach that will detect money laundering is tried to be presented through sample scenarios by bringing a broad perspective to crypto money-based money laundering methods, which are very difficult to trace due to their nature. In addition, it is expected that the difficulties in implementation of the proposed approach will be clearly addressed and will shed light and inspire further study.
Abstract Deanonymization is one of the major research challenges in the Bitcoin blockchain, as entities are pseudonymous and cannot be identified from the on-chain data. Various approaches exist to identify multiple addresses of the same entity, i.e., address clustering. But it is known that these approaches tend to find several clusters for the same actor. In this work, we propose to assign a fingerprint to entities based on the dynamic graph of the taint flow of money originating from them, with the idea that we could identify multiple clusters of addresses belonging to the same entity as having similar fingerprints. We experiment with different configurations to generate substructure patterns from taint flows before embedding them using representation learning models. To evaluate our method, we train classification models to identify entities from their fingerprints. Experiments show that our approach can accurately classify entities on three datasets. We compare different fingerprint strategies and show that including the temporality of transactions improves classification accuracy and that following the flow for too long impairs performance. Our work demonstrates that out-flow fingerprinting is a valid approach for recognizing multiple clusters of the same entity.