Blockchain Papers

Rajan Kumar

The evolution of organised crime in India and its transforming relations between power and criminality increasingly reflect the logic of decentralisation, digital technologies and networked connections. The gangs headed by dons, which rely on sharp hierarchical authoritybased structures and are punctuated by layers of absentees serving only their personal interests, are giving way to a quasi-invisible architecture of fluidity, cell-based interactions and technological apparatus. Our paper places the Lawrence Bishnoi Gang at the centre of the inquiry of a contemporary interstate gang network in the digital era, exploring the dynamics of how new digital appreciation, reconnaissance, social media, encrypted exchanges and prison forays emerge to provide new reach and resiliency. Networks beg to differ; if we are to extend networkable theory to include also the study of criminality, then the Bishnoi network is the proof in the pudding. Making a special place for the records of recent homemaking, we examine how they view high visibility, dalliance in extortion, recruitment, targeted killings and crossborder desiring playing host to an archipelago in constancy even as the leaders are locked away in prisons away from home. Features like the need for hub and spoke models and high visibility required need to be varied in digital amplification, conspiring to leverage symbolic power, fear and reputation that take over physicality and locomotion for effective control over regions. Under considerable stake in the legal framework, the duration study examines most existing laws in the Indian Constitution that deal with gangsterism, such as the Indian Penal Code, better coupled with the Unlawful Activities (Prevention) Act and new media protocols dilemmas seen in the context of jurisdictions, lax borders and differential standards in prosecuting loosely accreted actors. Themes, such as suggestions, Honda variants like focus on interstate security units in combating the malady of cyber threats are safeguarded, and digital surveillance, though with care for the future.

Andrii Svintsytskyi

The expansion of Decentralized Finance (DeFi) and Anonymity-Enhancing Technologies (AETs) has complicated the tracking of illicit financial flows. This article analyzes three distinct AETs—Tornado Cash, Monero, and Zcash—to assess how specific protocol mechanisms degrade transaction‑graph attribution and obstruct compliance. Synthesizing technical literature, AML/CFT frameworks, and recent judicial documentation, the study traces how design choices translate into investigative challenges. The analysis yields three key findings. First, “decentralization” rarely eliminates control; instead, it shifts choke points to infrastructure layers such as bridges and RPC providers. Second, while AETs significantly raise attribution costs, their effectiveness is often conditional and dependent on usage patterns. Third, the Tornado Cash enforcement saga illustrates the limitations of applying traditional sanctions to autonomous code. The paper concludes by proposing a mitigation agenda focused on measurable risk reduction at entry/exit points without compromising legitimate privacy.

Alex Chen

Decentralized Autonomous Organizations (DAOs) represent a novel organizational paradigm operating across multiple regulatory jurisdictions without traditional legal personhood, exposing participants to significant liability and enforcement risk. This study constructs a comprehensive quantitative taxonomy of regulatory frameworks applicable to DAOs, analyzing 72 operational entities across seven jurisdictional models and examining enforcement actions from fiscal years 2024-2025. We formalize the regulatory compliance burden as a multi-dimensional optimization problem, model liability distribution as a function of governance participation and token holdings, and derive metrics for securities classification risk and anti-money laundering exposure. Data aggregated from Wyoming DAOLLC/DUNA implementations, UK Limited Liability Partnership proposals, Malta ITAS certifications, Swiss Foundation structures, and the emergent Harmony Framework reveal that DAOs without legal wrappers exhibit 3.2× higher expected liability costs and face 4.7× greater regulatory enforcement probability. The proposed four-tier classification system-Unregistered Protocol DAOs, Operational Wrappers, Foundation Structures, and Hybrid Multi-Jurisdictional Entities-accounts for 94% of observed variance in regulatory outcomes. Regression analysis indicates that legal personhood recognition reduces member-level risk exposure by 68% while imposing median compliance costs of $127,000 annually. Securities enforcement data from 2024-2025 demonstrate that DAOs distributing governance tokens without exemption frameworks face prosecution rates of 23%, compared to 2.8% for legally structured entities. This framework provides a tractable model for jurisdictional selection, compliance architecture design, and governance mechanism optimization under regulatory uncertainty.

Uri Volovelsky, Sivan Shlomo Agon

Decentralized Autonomous Organizations (DAOs) are blockchain-based entities that operate without centralized management or shareholders, enabling worldwide token holders the option of participating in their governance through self-executing smart contracts. With approximately fifty thousand DAOs controlling over $30 billion in assets, these organizations offer unprecedented efficiency and global collaboration, enabling stakeholders to participate and contribute to the operation of DAOs regardless of their jurisdiction or physical presence. DAOs, however, also present significant legal and regulatory challenges, particularly concerning liability, contractual enforcement, tax obligations, and oversight. Their decentralized and fluid structure makes it substantively difficult for any single country—including powerful actors such as the United States and the European Union—to assert jurisdiction or exercise regulatory authority over such organizations. In addition to governance considerations, the decentralized, pseudonymous, and borderless structure of DAOs may be exploited for unlawful purposes, most notably money laundering. This Article examines how DAOs, particularly within the decentralized finance sector, facilitate anonymous cross-border transactions that pose novel and significant money laundering risks. By analyzing existing regulatory responses in major jurisdictions including the United States and the European Union, as well as efforts by key international organizations such as the Financial Action Task Force, the International Monetary Fund, and the United Nations, the Article demonstrates that prevailing regulatory frameworks and enforcement models cannot adequately respond to the distinct challenges presented by DAOs. This regulatory vacuum poses significant risks to global financial stability, the integrity of the financial systems, and core national-security interests, including the prevention of sanctions evasion, counterterrorism and proliferation financing, and the deduction and disruption of state-sponsored, cyber-enabled illicit finance. Accordingly, the Article proposes a novel, modular, risk-based, global anti-money laundering framework tailored to DAOs’ unique operational realities. The proposed framework aligns with principles of functional equivalence, technological neutrality, and transnational cooperation, offering a more effective means of addressing DAO-related, anti-money laundering risks while preserving space for innovation.

Qian'ang Mao, Jiaxin Wang, Liu Ya, Li Zhu · 6 authors

The decentralized architecture of Web3 technologies creates fundamental challenges for Anti-Money Laundering and Counter-Financing of Terrorism compliance. Traditional regulatory technology solutions designed for centralized financial systems prove inadequate for blockchain's transparent yet pseudonymous networks. This systematization examines how blockchain-native RegTech solutions leverage distributed ledger properties to enable novel compliance capabilities. We develop three taxonomies organizing the Web3 RegTech domain: a regulatory paradigm evolution framework across ten dimensions, a compliance protocol taxonomy encompassing five verification layers, and a RegTech lifecycle framework spanning preventive, real-time, and investigative phases. Through analysis of 41 operational commercial platforms and 28 academic prototypes selected from systematic literature review (2015-2025), we demonstrate that Web3 RegTech enables transaction graph analysis, real-time risk assessment, cross-chain analytics, and privacy-preserving verification approaches that are difficult to achieve or less commonly deployed in traditional centralized systems. Our analysis reveals critical gaps between academic innovation and industry deployment, alongside persistent challenges in cross-chain tracking, DeFi interaction analysis, privacy protocol monitoring, and scalability. We synthesize architectural best practices and identify research directions addressing these gaps while respecting Web3's core principles of decentralization, transparency, and user sovereignty.

Hazik Mohamed

The rapid rise of Decentralized Finance (DeFi) and anonymity-focused cryptocurrencies has transformed financial systems by eliminating intermediaries and enabling peer-to-peer transactions. While these innovations offer numerous benefits, they also present unprecedented challenges for crime prevention and regulatory enforcement. This paper examines how DeFi and privacy-enhanced cryptocurrencies, such as Monero and Zcash, facilitate financial crimes, including money laundering, ransomware attacks, and fraud. By applying criminological theories—Strain Theory, Routine Activity Theory, and Rational Choice Theory—this study reinterprets traditional crime models in the context of blockchain-based financial ecosystems. Law enforcement agencies face significant hurdles in investigating and prosecuting crypto-enabled financial crimes due to jurisdictional limitations, privacy-enhancing technologies, and decentralized governance. This paper explores how blockchain analytics, artificial intelligence-driven risk assessment, and cross-border regulatory collaborations, such as the Financial Action Task Force (FATF) Travel Rule and the EU’s Markets in Crypto-Assets (MiCA) regulation, are being developed to counter these emerging threats. Additionally, it assesses the institutional limitations of law enforcement agencies, the role of DeFi governance communities in mitigating financial crimes, and the potential impact of central bank digital currencies (CBDCs) on reducing illicit transactions. To enhance regulatory effectiveness, this study recommends strengthening international cooperation, improving forensic capabilities for tracking illicit blockchain transactions, and implementing ethical frameworks that balance financial privacy with security. The findings contribute to criminology, financial regulation, and cybersecurity by offering insights into evolving digital crimes and proposing solutions to mitigate their risks. Future research should explore the role of artificial intelligence in DeFi crime detection and the impact of regulatory advancements on illicit financial flows in decentralized ecosystems.

Liang Zhao

Amid the rapid evolution of digital currencies and the decentralized finance (DeFi) ecosystem, technology-driven, anonymous, and cross-border financial crimes pose systemic challenges to traditional regulatory frameworks. Grounded in three core theories of criminal psychology—Rational Choice Theory, Routine Activity Theory, and Techniques of Neutralization—and integrating the “technology–society co-construction” perspective from the sociology of technology, this study constructs a three-dimensional analytical framework encompassing “technological ecology, social cognition, and individual psychology.” It systematically elucidates the psychological formation logic and evolutionary pathways of financial crimes within the DeFi domain. The research reveals that the technical features of DeFi—anonymity, decentralization, and code autonomy—collectively create a “structural opportunity space” characterized by low accountability costs and weakened moral constraints. Subcultural communities further supply “morally neutralizing scripts” through narratives of crypto-libertarianism and the myth of “code as law.” Under these dual influences, individual psychology undergoes transformation, manifesting as complex motivations, distorted risk perceptions, and heightened moral disengagement, ultimately leading to a rationalization mechanism for criminal acts veiled behind “technological neutrality.”

Yosita Saikhwan, Khwanmanee Maneechot, Kanokphol Nikornpest

This research undertakes a comparative analysis of Thailand’s anti-money laundering (“AML”) regulatory framework in relation to the most recent recommendations issued by the Financial Action Task Force (“FATF”) concerning money laundering risks associated with Security Token Offerings (“STOs”) conducted via blockchain technology. The objective is to identify potential regulatory gaps and areas for improvement in Thailand’s existing AML measures, particularly in the areas of regulatory oversight, licensing requirements, customer due diligence (“CDD”), recordkeeping obligations, and the reporting of suspicious transactions by virtual asset service providers (“VASPs”). The methodological basis of the research is the comparative analysis method, examining Thailand’s applicable AML laws and regulations alongside FATF guidelines, relevant literature, and case law. The research found that Thailand’s applicable AML laws, including the relevant regulations, are inadequacies and inefficiencies in the regulatory oversight of securities offerings that utilize emerging technologies. Specifically, the current regulatory framework is insufficient in effectively preventing or mitigating risks related to money laundering and the financing of terrorism for investors. As a result, it does not adequately ensure the security and integrity of investments in decentralized systems, therefore, it fails to provide sufficient safeguards to protect investors from inadvertently becoming involved in unlawful activities. These shortcomings indicate a lack of alignment with international standards issued by the FATF. This research is useful to legislative authorities, lawyers, law students, and regulatory bodies, especially in Thailand, and only limited to the regulation of money laundering in Thailand and does not provide empirical research.

Mallikarjun Reddy Gouni

Financial crime detection faces unparalleled challenges as criminal networks exploit digital payment channels, cryptocurrency platforms, and cross-border transaction systems outside traditional monitoring frameworks. In this respect, AFCI introduces a novel framework for federated machine learning, regulatory reasoning engines, and real-time risk propagation analytics to build unified global privacy-preserving anti-crime intelligence ecosystems. The framework lets organizations train collaborative models with decentralized institutions, safely aggregating information from multiple parties without sharing sensitive transaction data by means of secure aggregation protocols and differential privacy mechanisms. Large language models coupled with knowledge graphs automate the processes of regulatory interpretation and rule generation, and graph neural networks enable the detection of coordinated criminal activities on a large scale in transaction networks through temporal message passing mechanisms. Reinforcement learning agents continuously optimize detection policies to balance the identification of genuine threats against the goal of minimizing false alarms. The framework bridged critical gaps in cross-border compliance coordination and empowered institutions to develop shared detection capabilities in support of data localization requirements and an array of diverse regulatory frameworks. Long-term security of privacy-preserving federated computation would be guaranteed with post-quantum cryptography. This convergence of advanced technologies allows next-generation financial crime prevention systems to remain effective against evolving criminal methodologies while preserving fundamental privacy rights.

Hugo Almeida, Pedro Pinto, Ana Fernández Vilas

The widespread adoption of cryptocurrencies has transformed the financial landscape by enabling swift, decentralised transactions. However, the pseudonymous nature of digital currencies has also fuelled illicit activities, such as money laundering. Criminals perform money laundering to access illicitly acquired funds without detection and convert illegally obtained assets into untraceable commodities, seamlessly integrated into the financial system. Although new regulatory measures have been introduced, illicit actors continue to exploit various methods, from peer-to-peer exchanges to cryptocurrency mixing services, to obscure the origins of illegal funds. This study presents a parametric analysis of these methods, examining dimensions such as duration, number of actors, contextual requirements, operational difficulty, traceability, and costs across each stage of the money laundering process: placement, layering, and integration. The analysis indicates that, while more sophisticated techniques may provide a higher degree of anonymity, they simultaneously require specialised technical expertise and meticulous planning. Consequently, there is a trade-off between the level of privacy attainable and the operational complexity inherent to each method. By systematically comparing these strategies, this analysis aims to contribute to a deeper understanding of cryptocurrency-based money laundering techniques, providing insight for more effective prevention and mitigation measures for both regulatory authorities and the financial sector.

Jiarong Lu, Bin Liao, Yi Liu, Kutorzi Edwin Yao

Ethereum has become a significant trading platform for financial activities such as Dapps, ICOs, and DeFi. However, it has also become a hub for criminal activities such as fraud, money laundering, and illicit fundraising. The construction of fraud detection models employing machine learning techniques is currently a mainstream research direction. Nevertheless, existing studies face significant challenges, including class imbalance in data samples and a lack of model interpretability. In this content, this work proposes a novel explainable model for Ethereum illicit account detection, ETHIAD (Ethereum Illicit Account Detection). Firstly, we pre-process the dataset by ADASYN oversampling and Lasso feature selection, etc., to more efficiently achieve feature modeling of transaction structures. Then, the ETHIAD model is trained using the XGboost algorithm, with an accuracy, precision, recall, F1 score, and AUC value of 99.70%, 99.51%, 99.02%, 99.26%, and 99.45%, respectively, the model outperforms the existing SOTA model by 0.05%-1.1%. Finally, we introduce SHAP framework to analyze the key influencing factors of illicit accounts from multiple perspectives, and the conclusions strongly enhance the explainability of the model.

Vikas Reddy Mandadhi

High-value payment transactions (HVTs) face heightened exposure to money laundering risks due to their large monetary volumes, cross-jurisdictional nature, and the increasing complexity of financial networks. Traditional Anti-Money Laundering (AML) procedures rely heavily on sharing customer identities, transactional attributes, and risk-model outputs across institutions and regulators—creating substantial privacy, security, and data-handling risks. Zero-Knowledge Proofs (ZKPs) offer a transformative alternative by enabling financial institutions to prove compliance with AML requirements without revealing the underlying sensitive information. This paper examines the design and application of ZKP-based compliance frameworks for HVT ecosystems, detailing how AML checks—including KYC verification, sanctions screening, transaction-amount threshold validation, behavioral-risk scoring, and source-of-funds assessment—can be cryptographically attested through privacy-preserving proofs. We propose a hybrid architecture that combines off-chain AML computation with an on-chain ZKP verification and audit layer supported by secure regulatory nodes. Through structured workflows and proof types such as range proofs, list membership proofs, and rule-compliance circuits, the model ensures regulatory oversight while maintaining strict confidentiality. The study also evaluates the performance implications of ZKP systems in high-volume transaction environments and addresses security, interoperability, and oracle-reliability concerns. Ultimately, ZKP-enabled AML frameworks demonstrate significant potential to enhance compliance efficiency, reduce data-exposure risk, and strengthen trust across global payment networks. The paper concludes by outlining future research opportunities, including AI-driven AML circuits, cross-border ZKP interoperability standards, and integration with decentralized identity solutions.

Melo, Lisana Hildegard

Resumo / Abstract : Este artigo pretende demonstrar como o Bitcoin se insere na construção de uma cultura de paz. Apresenta a evolução do conceito, de “não guerra” para “não violência”, e caracteriza a cultura de paz como uma dinâmica social de colaboração. Pontua que as transações não mediadas por terceiros possibilitam que os indivíduos escapem da influência econômica que acentua a assimetria de poder. Reconhece que a dinâmica que recompensa e incentiva a integridade da rede bitcoin privilegia a colaboração. Ao final, conclui que estamos diante de uma infraestrutura monetária que possibilita aquilo que queremos ver acontecer. This paper aims to demonstrate how Bitcoin fits into the construction of a culture of peace. It presents the evolution of the concept, from "non-war" to "non-violence," and characterizes the culture of peace as a social dynamic of collaboration. It points out that transactions not mediated by third parties allow individuals to escape the economic influence that accentuates power asymmetry. It recognizes that the dynamic that rewards and encourages the integrity of the Bitcoin network prioritizes collaboration. In conclusion, it states that we are facing a monetary infrastructure that enables what we want to see happennig.

Rashid Hamed Al-Balushi, Zaeem Nasser Al-Shuaibi

Abstract Blockchain technology is emerging as one of the most profound and cutting-edge innovations of the twenty-first century, providing a decentralized, immutable system for recording transactions. It has enabled the tokenization of distinctive digital assets, including art, music and real estate, through non-fungible tokens (NFTs). NFTs enable asset transfers by operating on pseudonymous blockchain networks, thereby preventing the disclosure of the owner’s real-world identity. While it enhances user privacy and innovation, it also creates significant anti-money laundering and counter-terrorism financing challenges. Fraudsters and other bad-faith actors can use these assets to obfuscate dirty money and illicit financial transactions, given lax or non-existent regulations on NFTs and extremely lax Know-Your-Customer compliance. In light of the above, the authors explore the nexus between NFTs and financial crime (with a particular focus on the legal frameworks of the Sultanate of Oman, the United Arab Emirates and the United Kingdom) in this article. The paper aims to evaluate how each jurisdiction’s response to NFT-related abuse has evolved and been effective in practice. This will be done through a review of existing laws, enforcement, regulations and regulatory gaps. The article ends with specific policy recommendations to enhance regulatory certainty, enforcement effectiveness and international cooperation, supporting an innovation-first approach to the NFT space tempered by necessary measures to prevent criminal abuse.

Oluwatosin Lawal, Awele Okolie, Callistus Obunadike

The detection of illicit cryptocurrency transactions remains a significant challenge due to the extreme class imbalance and limited generalization capabilities of machine learning models applied to Anti–Money Laundering (AML) data. In the widely used Elliptic dataset, illicit transactions represent less than 2% of all nodes, creating a high-risk setting in which models can achieve deceptively high training accuracy while failing to meaningfully identify malicious behavior. This study examines the behavior of Graph Neural Networks (GNNs) under these constraints and emphasizes the limitations rather than the performance of the approach. Instead of treating the model’s high training accuracy as a success, we demonstrate how imbalance, structural sparsity, and label noise impede reliable learning. We evaluate the model with and without common imbalance-handling strategies including class weighting and focal lossand illustrate that performance remains unstable. Furthermore, we investigate the explainability of the model using GNNExplainer, showing example subgraphs and salient features for known illicit nodes, and discuss their alignment with money-laundering patterns such as fan-out and transaction mixing. Our findings underscore the difficulties of applying GNNs to heavily imbalanced AML datasets and highlight the need for improved modeling strategies, semi-supervised techniques, and more robust explainability methods for real-world financial crime detection.

ISAKA AMIDY KIPALULE

The regulation of cryptocurrency presents a major challenge to financial governance in emerging economies such as Tanzania. The rapid growth of virtual assets, their decentralized nature, and potential for anonymity have raised significant concerns regarding money laundering, terrorist financing, and consumer protection. Internationally, the Financial Action Task Force (FATF) has established standards that require member states to regulate Virtual Asset Service Providers (VASPs) through licensing, supervision, and compliance with Anti–Money Laundering and Counter–Terrorist Financing (AML/CFT) measures. This article critically analyses the Tanzanian legal and institutional framework governing cryptocurrency in light of these international standards. It argues that although Tanzania has made preliminary steps such as recognizing digital assets under the Finance Act, 2024 and issuing public notices through the Bank of Tanzania there remains a significant regulatory gap in achieving full FATF compliance. The study concludes that comprehensive legislation is required to address the legal status of virtual assets, enhance regulatory oversight, and foster a balance between innovation and financial integrity.

Wiwit Prawitri, Laras Angelia Nnirwan, Elman Azizov

This research explores the implementation of a blockchain-based forensic audit framework designed to enhance the detection and investigation of suspicious financial activities within decentralized finance (DeFi) ecosystems. The main problem addressed in this study concerns the inefficiency, lack of transparency, and vulnerability to data manipulation commonly found in traditional forensic auditing systems. The objective is to develop a model that integrates blockchain technology with graph-based anomaly detection to improve accuracy, transparency, and scalability in financial audits. The proposed method combines blockchain’s immutable ledger capabilities with automated detection algorithms and Chain of Custody (CoC) verification to ensure data integrity and accountability. The results demonstrate that the proposed system achieves a detection accuracy exceeding 90%, as presented in Table 1, and effectively categorizes different suspicious transaction patterns illustrated in Figure 2. Compared to conventional methods, the framework offers superior performance in terms of speed, reliability, and adaptability. The findings suggest that this approach establishes a new paradigm in forensic auditing by combining automation, transparency, and scalability into a cohesive analytical model. In conclusion, the study confirms that blockchain-based forensic auditing significantly enhances digital financial oversight and provides a foundation for developing intelligent, tamper-proof audit systems suitable for the evolving landscape of decentralized finance.

Ia Eradze

Cheap energy, absence of regulations on mining, low taxes, free industrial zones made Georgia an attractive place for Bitcoin mining and home to such big companies as Bitfury and Binance. This paper asks how and why Georgia become a crypto mining hub and examines crypto mining in relation to the neoliberal state and its economic development mode. This study frames crypto currency mining as a state facilitated development project, which is embedded in Washington Consensus (WC) liberalization and deregulation policies and is enabled by Wall Street Consensus (WSC) derisking policies. The paper argues that crypto currencies - once emerged on allegedly nonpolitical economic grounds to challenge the state and existing financial order - need the state and its sovereign space. The study also unfolds continuities between WC and WSC and demonstrates the destructive character of crypto mining. The paper thus challenges the claims of the crypto industry of being against the state and traditional financial system, provides insights into the political economy of Bitcoin from a peripheral country perspective and enriches ongoing debates on neoliberal derisking states.

Shikah J. Alsunaidi, Hamoud Aljamaan, Mohammad Hammoudeh

Smart Contracts (SCs), self-executing programs on blockchain platforms, are transforming industries such as banking, healthcare, and supply chains through automated, trustless transactions. However, their inherent vulnerabilities have led to severe financial and operational losses, with large-scale exploits causing substantial economic damage. Machine Learning (ML) has emerged as a promising approach for SC vulnerability detection, yet its effectiveness, adaptability, and generalizability remain insufficiently explored. This article comprehensively classifies current Ethereum SC vulnerabilities and attacks. It also surveys 108 ML-based detection methods, covering both traditional models and a structured taxonomy of advanced approaches such as GNN-based, LLM-based, contrastive learning, ensemble, hybrid, meta-learning, and transfer learning techniques. The strengths, limitations, and practical challenges of these methods are systematically analyzed, with particular attention to factors such as detection stages, classification problems, dataset characteristics, feature engineering, performance evaluation, generalizability, detection capability, model aging, and ethical and privacy implications. Additionally, existing datasets on SC vulnerabilities are reviewed and consolidated. By integrating these insights, this work provides actionable guidelines and a foundation for building secure, resilient, and trustworthy SC ecosystems.

Gioia Arnone, Giovanni Scire’, Enzo Bivona

Abstract This study explores the intersection between cryptocurrencies and criminal organizations through a systematic literature review. By examining peer-reviewed academic publications, the research identifies the mechanisms by which criminal groups leverage digital currencies for illicit activities such as money laundering, fraud, and extortion. A structured PRISMA-based methodology was adopted, employing well-defined inclusion, and exclusion criteria to ensure transparency and replicability. The study reveals critical trends in the misuse of cryptocurrencies, the technological challenges faced by regulators, and the limitations of current enforcement frameworks. The originality of this research lies in synthesizing existing academic insights while identifying key gaps in the literature. The findings highlight the urgent need for tailored regulatory responses and greater cross-border cooperation, contributing to both academic understanding and policymaking. The practical implications include recommendations for policy adaptation and future research pathways to mitigate cryptocurrency-enabled criminal behavior.

Dan Lin, Ding, Yanli, Weipeng Zou, Jiajing Wu · 8 authors

Cryptocurrency money-laundering forensic analysis after Web3 incidents faces challenges such as fragmented evidence, expanding transaction paths, and cross-chain discontinuity. Existing Web3 AML methods largely rely on manual clues and heuristic or graph-search-based tracing, with outputs limited to lists of suspicious addresses and lacking path-level evidence and verifiable explanations. Directly applying general-purpose large language models to raw transaction flows also struggles to ensure evidence constraints and result verifiability. To address these limitations, this paper presents RISKTAGGER, an LLM-guided agent for forensic tracing of Web3 cryptocurrency money laundering. RISKTAGGER embeds the LLM as an evidence-constrained decision component within a controlled tracing loop. It extracts case clues from public incident materials, recursively expands a risk-labeled fund-flow graph over on-chain evidence, and generates evidence-organized reports for analyst review. We evaluate it on five real-world incidents spanning multiple years and covering heterogeneous attack patterns and laundering path structures. We further conduct cross-case generalization analysis, baseline comparison, component ablation, and LLM backend analysis. In the main Bybit case, the system achieves a 97.33% address recall and a 98.69% expert-reviewed sampled address precision. Across the other four incidents, it achieves 95.24-100.00% address recall and 91.27-100.00% expert-reviewed address precision. The cross-case results further show that the complexity of Web3 money laundering arises from heterogeneous mechanisms, including short-cycle fund fragmentation, long-range laundering paths, interwoven DeFi services, and deterministic denomination splitting. RISKTAGGER can recover case-related fund paths, identify high-priority risk accounts, and organize public evidence into verifiable forensic reports.

Jairo Dote-Pardo, María Teresa Espinosa-Jaramillo

This policy paper reviews the evolution of research from 2016 to 2024 on the role of digital assets in illicit financial flows and evaluates the effectiveness of existing detection and prevention strategies. It highlights two central policy imperatives for Latin America: (i) fostering regional cooperation among central banks to harmonize anti-money-laundering standards and address regulatory arbitrage, and (ii) integrating supervisory technologies (SupTech and RegTech) into compliance frameworks to improve detection capacity. The paper concludes that combining technological innovation with coordinated regional regulation is essential to safeguard financial integrity in the digital-asset era.

Stefan Kitzler, Masarah Paquet-Clouston, Bernhard Haslhofer

The Decentralized Finance (DeFi) ecosystem has experienced over \$10 billion in direct losses due to crime events. Beyond these immediate losses, such events often trigger broader market reactions, including price declines, trading activity changes, and reductions in market capitalization. Decentralized Autonomous Organizations (DAOs) govern DeFi applications through tradable governance assets that function like corporate shares for voting and decision-making. Leveraging DeFi's granular trading data, we conduct an event study on 22 crime events between 2020 and 2022 to assess their economic impact on governance asset prices, trading volumes, and market capitalization. Using a dynamic difference-in-differences (DiD) framework with counterfactual governance assets, we aim for causal inference of intraday temporal effects. Our results show that 55% of crime events lead to significant negative price impacts, with an average decline of about 14%. Additionally, 68% of crime events lead to increased governance asset trading volume. Based on these impacts, we estimate indirect economic losses of over $1.3 billion in DAO market capitalization, far exceeding direct victim costs and accounting for 74% of total losses. Our study provides valuable insights into how crime events shape market dynamics and affect DAOs. Moreover, our methodological approach is reproducible and applicable beyond DAOs, offering a framework to assess the indirect economic impact on other cryptoassets.

Eva Harmelia Valentina, Kinza Aish

Objective: This research aims to develop a comprehensive framework to identify and prevent money laundering in Decentralized Finance (DeFi) by leveraging big data analytics, integrating advanced machine learning algorithms, and network analysis techniques to address the challenges of pseudonymity and decentralization inherent to this ecosystem. Research Design & Methods: This research utilizes a mixed method approach with machine learning analysis based on Elliptic Dataset and qualitative policy study, applying graph models and classification algorithms to detect illegal transactions with precision in the context of imbalanced data. Findings: The results show that the MLP and GCN models achieve high accuracy (98% and 97.3%) and excellent recall (99.5% and 99.4%) on the Elliptic Dataset, significantly outperforming traditional methods. Exploratory data analysis and graph visualization confirmed that illegal transactions form denser clusters and more complex paths, indicating a layering pattern. Implications and Recommendations: Theoretically, this research extends the application of big data and graph theory to new financial systems, providing a blueprint for future RegTech and FinTech research. Practically, the framework offers tangible tools for regulators, law enforcement, and DeFi platforms to enhance AML capabilities, supporting the development of real-time monitoring tools and risk assessment models. Contribution and Value Added: The main contribution of this research is the development of a robust and adaptive big data analytics-based AML framework, which effectively addresses the unique challenges of DeFi.