Cryptocurrencies have revolutionized digital finance, offering decentralization, anonymity, and cross-border transactions. However, these very attributes have also facilitated money laundering, posing significant challenges for regulators. This paper examines the risks associated with cryptocurrency in relation to money laundering, emphasizing Indiaâs legal and regulatory framework. It discusses the role of the Prevention of Money Laundering Act (PMLA), the Reserve Bank of India (RBI) directives, and recent policy developments concerning digital assets. Additionally, the paper explores international regulatory frameworks and suggests policy measures to strengthen anti-money laundering (AML) mechanisms in India.
Cryptocurrencies and blockchain technology have revolutionized the financial sector, offering decentralized, secure, and efficient transaction mechanisms. However, these innovations have also introduced new challenges, particularly in the realm of financial crimes such as money laundering, illicit trade, and fraud. This paper explores the dual-use nature of cryptocurrencies, examining their potential for both financial innovation and criminal exploitation, with over $20 billion in illicit transactions recorded in 2023 (Chainalysis, 2023). By reviewing case studies, regulatory responses, and technological solutions, this paper provides a comprehensive analysis of the risks and opportunities presented by cryptocurrencies and blockchain technology. Current regulatory frameworks, such as the EUâs MiCA Regulation (2023) and FATF recommendations and guidelines, have significantly influenced cryptocurrency adoption by balancing innovation with risk mitigation. The paper concludes with actionable recommendations for enhancing regulatory frameworks, fostering international cooperation, leveraging AI and other technological advancements, and creating educational initiatives to mitigate financial crimes in the digital age.
JosÊ Juan de León, Cenchuan Zhang, Christos - Spyridon Koulouris, Francesca Medda ¡ 5 authors
The growing interest in decentralized finance (DeFi), driven by advancements in blockchain technologies such as Ethereum, highlights the crucial role of smart contracts. However, the inherent openness of blockchains creates an extensive attack surface, exposing participantsâ funds to undetected security flaws. In this work we investigated the use of deep reinforcement learning techniques, specifically Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), for detecting and classifying vulnerabilities in smart contracts. This approach utilizes control flow graphs (CFGs) generated through EtherSolve to capture the semantic features of contract bytecode, enabling the reinforcement learning models to recognize patterns and make more accurate predictions. Experimental results from extensive public datasets of smart contracts revealed that the PPO model performs better than DQN and demonstrates effectiveness in identifying unchecked-call vulnerability. The PPO model exhibits more stable and consistent learning patterns and achieves higher overall rewards. This research introduces a machine learning method for enhancing smart contract security, reducing financial risks for users, and contributing to future developments in reinforcement learning applications.
Chun-Wing Poon, James T. Kwok, Calvin Chow, Jieun Choi
Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight MVGNN module, which performs two-way message passing between nodes in a transaction graph. Additionally, LineMVGNN incorporates a line graph view of the original transaction graph to enhance the propagation of transaction information. We conduct experiments on two real-world account-based transaction datasets: the Ethereum phishing transaction network dataset and a financial payment transaction dataset from one of our industry partners. The results show that our proposed method outperforms state-of-the-art methods, reflecting the effectiveness of money laundering detection with line-graph-assisted multi-view graph learning. We also discuss scalability, adversarial robustness, and regulatory considerations of our proposed method.
This paper explores decentralized finance (DeFi), a fast-growing area powered by blockchain technology that offers a new alternative to traditional financial systems. DeFi removes the need for intermediaries like banks, making transactions more transparent, accessible, and often cheaper. This shift not only reduces costs but also helps improve financial access, particularly for people who are underserved by traditional banking systems. Key elements of DeFi, such as smart contracts and oracles, play a central role in automating processes and enabling peer-to-peer exchanges without needing middlemen. Despite its advantages, DeFi faces several challenges. Smart contracts can have security vulnerabilities, oracles may not always provide accurate data, and there is little consumer protection in place, which raises risks for users. Furthermore, DeFi's decentralized and often anonymous structure creates regulatory difficulties, especially when it comes to complying with anti-money laundering (AML) and know-your-customer (KYC) standards, which are crucial for ensuring financial safety and preventing illegal activities. This paper examines these issues and proposes potential solutions, such as decentralized oracle networks, regulatory tools embedded within DeFi platforms, and improved scalability techniques. These solutions aim to enhance DeFi's security while maintaining its core decentralized benefits. The paper concludes by discussing the future of DeFi, stressing the importance of balanced regulations that protect users without stifling innovation. Ultimately, DeFi holds the potential to reshape global finance, making it more inclusive, efficient, and accessible.
Abstract Anti-money laundering has been an issue in our society from the beginning of time. It simply refers to certain regulations and laws set by the government to uncover illegal money, which is passed as legal income. Now, with the emergence of cryptocurrency, it ensures pseudonymity for users. Cryptocurrency is a type of currency that is not authorized by the government and does not exist physically but only on paper. This provides a better platform for criminals for their illicit transactions. New algorithms have been proposed to detect illicit transactions. Machine learning and deep learning algorithms give us hope in identifying these anomalies in transactions. We have selected the Elliptic Bitcoin Dataset. This data set is a graph data set generated from an anonymous blockchain. Each transaction is mapped to real entities with two categories: licit and illicit. Some of them are not labeled. We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). GCN is of special interest in our case. Different evaluation parameters such as accuracy, ROC and F1 score are analyzed for different models. Our experimental results show that the proposed GCN model gives the accuracy $$98.5\%$$ , the AUC 0.9444 and the RMSE 0.1123, which concludes that our GCN is better than the existing models, in particular with the model proposed in Weber et al. (Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics, 2019. http://arxiv.org/abs/1908.02591 ).
The modern global financial environment faces a complex combination of requirements associated with ensuring systemic solvency while preventing the use of banks as conduits for illegal financial transactions. The current paper focuses on evaluating the capacity of modern regulatory standards for addressing these interconnected challenges. While modern legislation and regulatory approaches have reached a new level of sophistication and standardization, the dynamic nature of innovations in the field of decentralized finance integrate specific examples of Explainable AI (XAI) tools like SHAP values or Grad-CAM that regulators are currently using to improve transparency in decentralized finance. A qualitative-comparative methodology is employed for exploring the impact of strict enforcement of financial standards on the sustainability of the banking sector. Using case studies drawn from some of the world's largest economies, such as the EU, the US, and India, the study finds that despite the positive impact of regulations on the core of the global economy (e.g., through enhancing the financial cushioning of banks), there is evidence that the displacement effect has occurred, which means that risks and illegal activities continue to be relocated to the shadow economy. From the policy implications, a shift from a response-oriented and rule-based approach to one that is proactive and intelligence-based, emphasizing globalization and integration, becomes evident. For future regulation, there is a need for the coverage to be extended to non-bank financial institutions as well as dealing with the paradox of compliance whereby escalating costs have not yet translated into less global money laundering.
Abstract Smart contracts and blockchain technology have revolutionized our transactions and interactions with digital systems, yet their vulnerabilities can lead to devastating consequences such as financial losses, data breaches, and compromised system integrity. Existing detection methods, including static analysis, dynamic analysis, and machine learning-based approaches, have their limitations, such as requiring large amounts of labeled data or being computationally expensive. To address these limitations, we propose a novel approach that leverages a One-Class Variational Autoencoder (VAE) with CodeBERT for data pre-processing to detect vulnerabilities in smart contracts. Our approach achieved a higher F1 score (88.93%) compared to the baselines evaluated, even when labeled data is limited. This paper contributes to the development of effective and efficient vulnerability detection methods, ultimately enhancing the security and reliability of smart contracts and blockchain-based systems. By demonstrating superior performance in imbalanced data scenarios, our method offers a practical solution for real-world applications in blockchain security.
We discover a novel flight-to-safety (FTS) effect from cryptocurrency markets to stock markets, triggered by a series of hacking attacks on cryptocurrency exchanges. This phenomenon is driven by heightened uncertainty, which increases investorsâ risk awareness and prompts asset reallocation in favour of safer stock markets over riskier cryptocurrency markets. We conduct an extensive global examination of this effect across 39 countries and confirm this novelty. This effect is amplified by frequent attacks when investorsâ risk awareness is strengthened. Notably, social media sentiment surrounding these attacks serves as both a timely warning indicator for upcoming hacking events and a measure of the FTS pressure following such attacks. We conclude that the collapsed investor confidence and increased risk aversion are the primary cause of such an effect. We further substantiate the FTS hypothesis by offering evidence of significant abnormal fund flows into US mutual funds following these hacking events. As such, through the lens of cyber attacks, we document how a shock in cryptocurrency markets is transmitted into stock markets via investorsâ FTS behaviour. ⢠We discover a flight-to-safety (FTS) effect from cryptocurrency to stock markets. ⢠The FTS effect is amplified by more frequent cyberattacks. ⢠Social media sentiment can warn upcoming hacking events and measure FTS pressure. ⢠The FTS is driven by collapsing investor confidence and heightened risk aversion. ⢠Evidence from US mutual fund supports our novel FTS effect.
Abstract Since its introduction as a decentralized digital currency for peer-to-peer transactions, Bitcoinâs role in financial markets has undergone significant evolution. We employ bibliometric analysis to explore research trends in Bitcoin, identifying two primary perspectives in the recent financial economic literature: Bitcoin as a speculative asset and as a safe-haven asset. The speculative nature of Bitcoin is evident through its high volatility and frequent price jumps, largely influenced by rapid shifts in investor sentiment and attention, which create both risks and opportunities for traders. Conversely, Bitcoin exhibits characteristics of a safe-haven asset due to its asymmetric tail dependence and negative correlation within certain asset classes.
The increasing adoption of blockchain technology has led to a surge in financial fraud, including money laundering, Ponzi schemes, and illicit fund transfers. Traditional fraud detection techniques, such as rule-based systems and supervised machine learning models, struggle to handle the high-volume, high-velocity, and dynamically evolving nature of blockchain transactions. These limitations necessitate a scalable and adaptive approach to detect fraudulent activities efficiently. This study introduces a Spatial-Temporal Graph Neural Network (STGNN)-based fraud detection framework, specifically designed for scalable anomaly detection in large-scale blockchain networks. By modeling blockchain transactions as a spatial-temporal graph, the proposed system captures structural dependencies between wallets and temporal patterns of fund movements. The STGNN model employs graph convolutional networks (GCN) or graph attention networks (GAT) for spatial feature extraction and gated recurrent units (GRU) or temporal convolutional networks (TCN) for sequential fraud pattern recognition. Additionally, to ensure scalability, the framework incorporates graph partitioning techniques, parallelized mini-batch training, and distributed processing, enabling real-time fraud detection across high-throughput blockchain networks. Extensive experiments conducted on Bitcoin and Ethereum transaction datasets demonstrate that the STGNN model achieves higher accuracy, lower false positive rates, and improved computational efficiency compared to rule-based fraud detection systems, supervised ML models, and static GNNs. Case studies further confirm the modelâs effectiveness in detecting large-scale fraud schemes, such as DeFi exploits, cross-chain laundering, and coordinated illicit transactions. This research highlights the potential of graph-based deep learning techniques in blockchain security, providing a foundation for future advancements in scalable fraud detection, cross-chain anomaly detection, and decentralized financial security monitoring.
Blockchain technology, originating from the Bitcoin system, is a prominent notion in both practical applications and scholarly discourse. Numerous subtopics may be seen, including the definition of blockchain, its historical significance in the evolution of currency, its durability, and its magnitude of influence within the literature. In other words, sufficient study on blockchain exists in the literature. Likewise, several studies exist about auditing, particularly concerning accounting and taxation within the setting of the Turkish economy. An examination of official declarations and legislation in Turkey reveals that the state's view on the bitcoin industry lacks definiteness. The perspectives are transitioning from negative to positive. Nevertheless, contradicting remarks have also been seen. Upon assessing the existing circumstances, the strategic plans of nations with comparable developmental stages and active cryptocurrency markets are identified. The most appropriate stance for Turkey is neither entirely liberal nor entirely restrictive. The market requires active management and oversight. This control includes accounting and taxation. Turkey should transition from a passive observation approach to one that incorporates a definitive hybrid therapy. This hybrid encryption encompasses the fundamental components of the cryptocurrency system and the corresponding regulation of pertinent regulations.
Ben Berger, Edward W. Felten, Akaki Mamageishvili, Benny Sudakov
Optimistic rollups rely on fraud proofs -- interactive protocols executed on Ethereum to resolve conflicting claims about the rollup's state -- to scale Ethereum securely. To mitigate against potential censorship of protocol moves, fraud proofs grant participants a significant time window, known as the challenge period, to ensure their moves are processed on chain. Major optimistic rollups today set this period at roughly one week, mainly to guard against strong censorship that undermines Ethereum's own crypto-economic security. However, other forms of censorship are possible, and their implication on optimistic rollup security is not well understood. This paper considers economic censorship attacks, where an attacker censors the defender's transactions by bribing block proposers. At each step, the attacker can either censor the defender -- depleting the defender's time allowance at the cost of the bribe -- or allow the current transaction through while conserving funds for future censorship. We analyze three game theoretic models of these dynamics and determine the challenge period length required to ensure the defender's success, as a function of the number of required protocol moves and the players' available budgets.
In 2021, El Salvador declared bitcoin legal tender. According to President Nayib Bukele, the measure was intended to expand access to financial services in a country with a high proportion of unbanked people and to cheapen and ease remittance flows for migrants and their families. In this article, we inquire about the use of bitcoin as a tool for financial inclusion and contend that this policy needs to be seen in the broader context of democratic backsliding. We show that bitcoin has not translated into financial inclusion, but instead, the bitcoin law serves as a public relations tool to capture new support from like-minded constituencies, build closer relations with them, and empower international âcrypto-bros.â On the other hand, this is a tool to benefit a close circle close to the president with the use of public funds, as part of a broader historical shift of elites in El Salvador.
Stablecoins serve as the backbone of many decentralized finance (DeFi) ecosystems, offering price stability in an otherwise volatile cryptocurrency market. This paper analyzes the economic design of stablecoins- both algorithmic (un- or under-collateralized) and asset-backed (collateralized)- and employs game-theoretic models to examine their susceptibility to speculative attacks. We present mathematical frameworks illustrating peg- maintenance mechanisms, discuss equilibrium conditions for stable pegging, and use real-world examples of USDC, DAI, and Terra-Luna to highlight the key success and failure factors. Policy and protocol design recommendations are provided to help mitigate risks of de-pegging and bank-run dynamics.
This comprehensive article explores the transformative impact of blockchain technology on financial operations, focusing on its architectural foundations, security mechanisms, and practical applications. The article explores how blockchain's distributed ledger technology revolutionizes transaction processing and verification through advanced cryptographic protocols and consensus mechanisms. The article encompasses the evolution of smart contracts, their role in automating financial agreements, and the implementation of robust security frameworks. The article also investigates blockchain's contribution to regulatory compliance and audit capabilities, while addressing future developments in the technology's integration with artificial intelligence and cross-chain interoperability protocols. The article demonstrates blockchain's significant potential in creating more efficient, transparent, and secure financial systems while highlighting the importance of balanced implementation strategies that consider both performance optimization and security requirements.
The emergence of blockchain and cryptocurrency technologies has transformed digital ecosystems, introducing opportunities for innovation and efficiency alongside profound ethical challenges. This paper explores key ethical considerations in cryptocurrency and blockchain, including the decentralization of financial systems, the balance between privacy and transparency, the use of blockchain for surveillance, and the socio-economic impacts on vulnerable populations. The authors delve into the contrasting emphasis on ethical considerations for financial solutions deployed in developed and developing countries. The borderless nature of blockchain and cryptocurrencies enables decentralised international transactions while simultaneously introducing specific challenges regarding the definition of applicable law and other jurisdictional legal matters. Through a combination of literature analysis and illustrative case studies, the authors examine the complex ethical dilemmas that accompany these technologies in combination with their actual and perceived links to crime. The findings aim to provide actionable insights for policymakers, industry leaders, and researchers, fostering the responsible and equitable adoption of blockchain and cryptocurrency technologies.
Africa is one of the fastest-growing crypto markets in the world, with its crypto transactions peaking at $20 billion per month in mid-2021, with Nigeria contributing to the worldâs third largest bitcoin-holding. Evidence continues to grow, showcasing criminals who seek to use cryptocurrencies for illegal activities like money laundering which could subsequently give rise to the event of an unregulated economy and global financial instability. As a result of the challenges of these unconventional currencies and transaction methods, the Financial Action Task Force (FATF) updated its recommendations (particularly with the inclusion of Recommendation 15 on New Technologies) to address the various money laundering and terrorism financing risks associated with virtual assets (including cryptocurrencies) and Virtual Asset Service Providers (VASPs) as an attempt to encourage member statesâ regulatory actions against such risks. As there has been considerable work carried out by the Intergovernmental Action Group Against Money Laundering in West Africa (GIABA) with regards to the implementation of Recommendation 15 in West African member states (and particularly Nigeria for the purpose of this research), this paper therefore investigates and evaluates the role of GIABA as an FRSB in monitoring Money Laundering in West Africa and implementing FATF Standards in the 21st-century era of virtual assets and other cryptocurrencies. Particularly, it provides an in-depth assessment of GIABAâs work in monitoring the implementation of Recommendation 15, especially with regards to cryptocurrency-based money laundering amidst the growth of cryptocurrency exchange and trading services in West Africa.
Abstract Libertarian city projects are emerging as a new trend in capitalist urbanisation. One aspect about this trend is their location in rural Global South regions. The former raises questions about the role of these projects in the global land grab. This paper analyses the connection of libertarian city projects and land dispossession through the case of Bitcoin City in El Salvador. Data from news reports, surveys, and cadastral records show that the land dispossession associated with Bitcoin City is connected to a speculative dynamic generated by the project's intensive publicity. The media spectacle created by this publicity is intensifying preâexisting land grabbing patterns oriented toward the construction of real estate projects. I argue that this phenomenon is a result of the ruling elite's attempt to construct a strategy of economic growth around a logic of financial accumulation. To explain the rationality behind this effort and its material impacts, I introduce the concept of fictitious development .
This article delves into the transformative impact of blockchain technology on enhancing transaction quality and efficiency. Since the emergence of blockchain alongside Bitcoin in 2008, its decentralised and transparent nature has significantly improved transaction speed, security, and cost efficiency. These advancements have solidified blockchain as a foundational innovation in financial services. The paper examines critical milestones in blockchain, including Bitcoin, Ethereum, and Binance Coin (BNB), and their role in reshaping global finance by automating processes and reducing reliance on intermediaries. Additionally, the study evaluates blockchainâs impact on quality management, particularly emphasising how its immutable ledger system enhances the reliability and transparency of financial transactions. Despite challenges such as scalability, energy consumption, and regulatory hurdles, the potential for blockchain to redefine transaction quality in financial services is evident. This research contributes to the growing body of literature by integrating blockchain technology and traditional quality management systems, providing a comprehensive perspective on how the two domains influence one another. The findings underscore blockchainâs ability to drive innovation in financial services while addressing security, efficiency, and operational quality concerns.
The study aims to assess the dual (positive and negative) impact of FinTech in financial institutions. In this study, secondary data were used, and they were collected from Web of Science, Scopus, ScienceDirect, and Google Scholar. In this regard, the key FinTech technologies are identified, including blockchain and distributed ledger technology, artificial intelligence and machine learning, robo-advisors, mobile banking and digital banking, regulatory technology, and cloud computing. While major financial crimes are fraud, money laundering, insider trading, bribery and corruption, tax evasion, and cybercrime, The study shows that AI algorithms help to identify criminal activities, including credit card fraud, theft, and account takeovers, and ensure data privacy, accountability, and transparency. Blockchain is useful for trustless transactions since it creates an unchangeable and secure, transparent record of every transaction. Big data analytics help to acquire insights into customer behaviour and preferences. RegTech tracks online transactions in real time to spot anomalies in the realm of digital payments. On the other hand, FinTech is one of the most effective tools to facilitate cybercrime. Moreover, the study shows the framework FinTech has for mitigating wrongdoing, regulatory shortages, and customer threats. The article provides several implications for several stakeholders in the financial sector.
Blockchains are the backbone behind cryptocurrency networks, which have developed rapidly in the last two decades. However, this growth has brought several challenges due to the features of these networks, specifically anonymity and decentralization. One of these challenges is the fight against fraudulent activities performed in these networks, which, among other things, involve financial schemes, phishing attacks or money laundering. This article will address the problem of identifying fraud cases among a large set of transactions extracted from the Bitcoin network. More specifically, our studyâs goal was to find reliable techniques to label Bitcoin transactions, taking into account their features. The approach followed involved two kinds of Machine Learning methods. On the one hand, anomaly detection algorithms were applied to determine whether fraudulent activities tend to show anomalous behaviour without resorting to manually obtained labels. On the other hand, Heterogeneous Graph Transformers were used to leverage the heterogeneous relational nature of the cryptocurrency information. As a result, the article will provide reasonable conclusions to acknowledge that unsupervised approaches can be useful for fraud detection on blockchain networks. Furthermore, the effectiveness of supervised graph methods was revalidated, emphasizing the importance of data heterogeneity.
Abstract This study investigates the dark side of the non-fungible token (NFT) marketplace, with a focus on understanding the risks, and underlying factors driving fraud in the NFT ecosystem. Using the fraud triangle framework, this study examines pressure, opportunity, and rationalization from individual and organizational perspectives. The research provides a comprehensive understanding of the contributing factors to NFT marketplace fraud by analyzing the reasons behind fraudulent actions. A conceptual framework is developed that includes ten propositions to aid in understanding the complexity of this issue. This studyâs outcomes will assist policymakers in crafting efficient approaches to mitigate fraud within the NFT marketplace.