The increasing complexity of global financial systems has necessitated the adoption of more efficient and transparent mechanisms for combating money laundering (AML). Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a promising solution to address the limitations of traditional AML systems. This paper represents a review, exploring the potential applications of AI and blockchain in enhancing financial control systems, in particular, within AML compliance, focusing on key areas such as transaction monitoring, cross-institutional data sharing, and regulatory reporting. The integration of blockchain can streamline AML processes, reduce operational costs, and increase the effectiveness of detecting illicit financial activity. The combination of blockchain technologies and artificial intelligence algorithms in financial control is considered. It is shown how automation of transaction analysis can strengthen the stability of the banking system and prevent financial crimes. It is demonstrated that the convergence of Artificial Intelligence and blockchain technologies presents a transformative opportunity to strengthen AML frameworks, particularly in the face of rising crypto-enabled financial crimes. This research offers several important contributions to the academic literature. First, it presents a synthesis of the current status of artificial intelligence approaches used for compliance in detecting fraud in Bitcoin transactions. This review discusses the essential methodologies and tactics in a particular area that intersects finance and compliance but falls under the broader disciplines of AI-driven finance and decentralized finance (DeFi). The incorporation of AI into financial control marks a tremendous technological revolution that is affecting industries across the board. Second, the study assesses the current state of the publications, major trends, and research gaps, emphasizing areas that deserve additional investigation.
Considerando a crescente expansão do mercado de criptoativos e a recorrente associação do bitcoin a práticas de ocultação patrimonial, torna-se relevante examinar sua utilização no delito de lavagem de dinheiro, especialmente diante dos riscos de responsabilização penal indevida de agentes que atuam licitamente nesse setor. Objetiva-se analisar a utilização do bitcoin no delito de lavagem de dinheiro, a partir do exame de sua definição, de suas formas de circulação e de sua possível inserção em dinâmicas de ocultação e dissimulação de valores de origem ilícita. Para tanto, proceder-se-á a uma pesquisa de abordagem qualitativa, com emprego do método jurídico-dogmático e da linha crítico-metodológica, mediante revisão bibliográfica e documental da legislação e da doutrina especializada. Desse modo, observa-se que o bitcoin, embora apresente características que podem favorecer sua utilização em esquemas de lavagem de capitais, como pseudoanonimato, descentralização, mobilidade transnacional e inexistência física, não constitui, por si só, instrumento ilícito. O que permite concluir que sua relevância penal depende da demonstração concreta de vínculo com infração penal antecedente e da prática de atos voltados à ocultação ou dissimulação da origem ilícita dos valores.
Penelitian ini mengevaluasi efektivitas federated learning dalam mendeteksi alamat ilegal pada blockchain Ethereum untuk Anti Money Laundering (AML). Studi ini membandingkan XGBoost centralized dan federated dalam simulasi multi exchange, data dibagi secara horizontal menjadi 3 subset yang merepresentasikan entitas bursa independen. Untuk mengisolasi pengaruh pelatihan terdistribusi, pembagian data dilakukan secara homogen (IID), sehingga analisis difokuskan pada perbedaan mekanisme pembelajaran tanpa dipengaruhi heterogenitas data. Hasil menunjukkan bahwa model federated mencapai performa yang kompetitif dengan PR AUC 0,9962 dan akurasi 97,11%, dibandingkan model terpusat dengan PR AUC 0,9975 dan akurasi 97,75%. Namun, performa tersebut disertai peningkatan durasi pelatihan 4,01 detik dibandingkan 3,39 detik, yang disebabkan oleh beban komunikasi selama proses pembaruan model. Temuan ini menegaskan adanya trade off antara kinerja dan efisiensi dalam penerapan federated learning. Meskipun mampu mendekati performa pembelajaran centralized, pendekatan ini menimbulkan biaya operasional tambahan. Studi ini merupakan evaluasi dasar dalam kondisi terkontrol dan belum merepresentasikan skenario dunia nyata, sehingga diperlukan penelitian lanjutan pada data non IID dan skala sistem yang lebih besar.
Anti-Money Laundering regulatory frameworks share an unstated design premise: the adversary is human. We demonstrate that this premise is architecturally false: a fully autonomous, deterministic multi-agent system can perform every function of a human criminal organization without possessing computational mens rea. We formalize this system as HYDRA-A and prove that against it, intent-based AML detection mechanisms have zero expected utility. Contributions: (C1) A formal model of the autonomous AML adversary with role-by-role replacement of human criminal organization structure. (C2) The Theorem of Absent Mens Rea: a formal proof that a fully-specified autonomous adversary cannot possess criminal intent, derivable from the typed component interfaces of HYDRA-A. (C3) A Corollary of AML Framework Obsolescence: every intent-based detection mechanism has zero expected utility against HYDRA-A. (C4) A personality vector P=(rho, alpha, sigma, tau) with five adversarial archetypes. (C5) A No Stationary Nash Equilibrium theorem for the arms race between HYDRA-A and adaptive defenses. (C6) Zero-knowledge behavioral verification is the only architectural class that provides a formal defense guarantee against HYDRA-A.
Brandon C. Dulisse, Jean Denis, Nathan T. Connealy
Purpose This systematic scoping review aims to map the peer-reviewed evidence on cryptocurrency-enabled money laundering to highlight significant gaps in knowledge, particularly regarding its role in the rapid expansion of Southeast Asia’s scam economy. It focuses on scam compounds, money mule networks and anti-money laundering/counter-terrorist financing strategies to inform policy responses. Design/methodology/approach Following systematic scoping review guidelines, 7,669 records from five databases (2009–2025) were screened, yielding 25 peer-reviewed studies. Dual-reviewer screening, standardized extraction and thematic synthesis were used, with quality appraisal emphasizing methodological rigor and theoretical depth. Findings Four themes emerged: (1) industrial scam ecosystems in Southeast Asia are linked to crypto-enabled wealth transfer and laundering, though peer-reviewed evidence on these specific regional operations remains limited; (2) offender rationales favor low-risk, high-reward techniques like mixing, privacy coins and high-volume, low-amount transfers; (3) blockchain forensics enable tracing and evidence gathering, but are challenged by evolving privacy tools; and (4) regulatory attempts often lag behind criminal innovation, necessitating financial reforms. However, the review reveals a critical gap: only a minority of studies directly address Southeast Asia’s scam compounds, underscoring the need for targeted research amid the region’s burgeoning scam economy. These themes illustrate that cryptocurrency has become essential financial infrastructure for organized crime, yet empirical insights specific to Southeast Asia remain limited. Originality/value To the best of the authors’ knowledge, this is the first systematic scoping review highlighting the shortage of peer-reviewed research and studies of Southeast Asia’s scam economy in cryptocurrency laundering research, synthesizing broader literature to advocate for criminologically informed interventions.
Scarlett Sieber, Ian Fong, Tina Lončarić, Dhanum Nursigadoo · 6 authors
Fraud is the financial world’s biggest headache. It doesn’t matter if you’re in traditional finance (TradFi) or decentralized finance (DeFi); you’re faced with an unrelenting wave of criminal behavior.
Abstract Money laundering is one of the most insidious and sophisticated threats to the integrity of worldwide financial systems. With criminals increasingly employing sophisticated methods to disguise the origin of ill-gotten gains, formal and informal financial structures are acutely exposed to abuse. This chapter discusses the underlying mechanisms and stages of the laundering process—placement, layering, and integration. It discusses the profound interlinkages between money laundering and other criminal activities, such as drug trafficking, terrorist financing, and kleptocracy, to establish its status as the fulcrum of world illicit economies. Economic consequences of illicit financial flows are carefully weighed, citing their role in undermining market integrity, facilitating capital flight, undermining tax systems, and exacerbating social inequality. Case studies, such as the FinCEN Files and the Danske Bank case, expose structural weaknesses in regulation and enforcement. An assessment of international legal mechanisms—such as the FATF Recommendations, EU AML Directives, and US PATRIOT Act—examines their efficacy, implementation, and cross-border cooperative frameworks. Key compliance tools such as know-your-customer (KYC), suspicious transaction reporting (STR), and beneficial ownership registers are evaluated in the context of the evolving role of financial intelligence units. Lastly, this chapter discusses the new challenges posed by digital finance, namely with regard to crypto-assets and decentralized finance platforms. It assesses the potential of regulatory technology, artificial intelligence, and blockchain analytics in strengthening enforcement measures. This chapter concludes with recommendations for reforms aimed at enhancing transparency, institutional coordination, and global financial resilience against laundering operations.
Crime, Illicit Activities, and Governance
Business and Economic Development
Legal, Health, Environmental and COVID-19 Challenges
Abstract The rapid growth of cryptocurrencies has redefined the global financial landscape, enabling decentralized and borderless transactions. While digital assets offer efficiency, innovation, and financial inclusion, they have also introduced new avenues for financial crime. This chapter critically examines the intersection of cryptocurrency and illicit financial activity, with a focus on typologies such as money laundering, terrorist financing, ransomware payments, investment fraud, and tax evasion. Through an analysis of real-world cases and peer-reviewed research, this chapter highlights how features such as pseudonymity, decentralized finance, and privacy-enhancing technologies have complicated regulatory enforcement and forensic tracking. This chapter also provides a comparative overview of global regulatory responses, including frameworks from the United States, European Union, Singapore, India, and China, as well as guidance from international institutions such as the Financial Action Task Force and the Organization for Economic Co-operation and Development. Key challenges such as legal ambiguity, technological complexity, and institutional fragmentation are explored in depth. In response, this chapter identifies emerging opportunities to strengthen oversight, including blockchain analytics, regulatory sandboxes, supervisory colleges, and capacity-building initiatives. It concludes by recommending a coordinated, adaptive, and risk-based regulatory approach that balances innovation with accountability.
Due to the fast development of cryptocurrency and blockchain technologies, the field of financial innovation, data privacy, and legal regulation has become a complex area with a multi-faceted regulatory environment. In this paper, the authors discuss the critical problem of ensuring the rights to privacy of individuals and the necessity of an effective control over the regulatory framework in decentralized digital financial systems. Although cryptocurrencies like Bitcoin have facilitated peer-to-peer payments, increased transparency, and financial inclusion, their pseudonymous and borderless characteristics have also brought serious concerns associated with money laundering, terrorist funding, market volatility, and consumer protection. In a comparative and interdisciplinary approach, the research assesses the current regulatory reactions and outlines the increasing role of international principles, constructed by the Financial Action Task Force. It contends that the conventional approaches to regulation, which were developed to deal with centralized financial institutions, cannot deal with the contingencies of decentralized ecosystems. In this regard, the paper will present a technology-based governance model that incorporates the use of law, institutional, and technological solutions to emerge with a harmonious regulatory strategy. This is highlighted in the study as the new technologies including blockchain analytics, artificial intelligence, smart contracts, and privacy protection tools like zero-knowledge proofs could be used to facilitate regulatory compliance without compromising user privacy. It also highlights the significance of risk-based, adaptive regulation, regulatory sandboxes and international collaboration in reducing regulatory arbitrage and global financial integrity. Finally, the paper argues that the future of cryptocurrencies regulation is in the creation of adaptable, innovation-oriented, and privacy-sensitive rules. A balance between law and technology can enable policymakers to create a secure, transparent, inclusive digital financial ecosystem and protect basic rights and the larger interest of society.
This paper examines the evolving relationship between blockchain architecture and financial privacy, focusing on the inherent tension between transparency, pseudonymity, and regulatory oversight. It begins by analysing the structural foundations of blockchain systems, including distributed ledgers, cryptographic security, and decentralized consensus mechanisms, which collectively replace institution-based trust with system-based verification. While such architecture enhances transparency and immutability, it simultaneously generates new privacy challenges. Through a comparative analysis of Bitcoin, Monero, and Zcash, the paper highlights a spectrum of privacy designs within the cryptocurrency ecosystem. Bitcoin represents a model of transparent yet pseudonymous transactions, where public ledger visibility enables traceability despite the absence of explicit identity markers. In contrast, Monero adopts a privacy-centric approach using ring signatures, stealth addresses, and confidential transactions to obscure sender, receiver, and transaction value. Zcash introduces a hybrid model, employing zero-knowledge proofs (zk-SNARKs) to reconcile transactional confidentiality with verifiability, alongside selective disclosure mechanisms. The study further explores the limitations of transparent blockchains, including risks of transaction traceability, address clustering, and linkage to real-world identities through regulatory touchpoints such as exchanges. It also evaluates the regulatory implications of privacy-enhancing technologies, particularly their impact on anti-money laundering (AML) and counter-terrorism financing (CTF) frameworks. The paper underscores the growing role of international standards and regulatory bodies in shaping compliance mechanisms within decentralized ecosystems. Finally, the paper considers emerging solutions such as privacy-preserving smart contracts, decentralized identity systems, hybrid blockchain models, and regulatory technologies (RegTech), which aim to balance user privacy with legal accountability. It argues that the future of blockchain governance lies not in choosing between transparency and privacy, but in developing adaptive frameworks that integrate both. The study concludes that achieving this balance will require sustained interdisciplinary collaboration and coordinated global regulatory efforts.
The quick proliferation of cryptocurrency markets has essentially transformed the financial frameworks of the globe in that it has made it possible to initiate the means of value transfer across borders that are decentralized, borderless, and technologically advanced. Cryptocurrencies are based on blockchain and cryptographic protocols and enable peer-to-peer transactions without use of traditional financial intermediaries, which improves efficiency, lowers the costs of transactions, and increases financial inclusion, especially in underserved areas. In addition to payments, the technologies have stimulated innovation in fields like decentralised finance, smart contracts, and systems of digital identity. But the very same characteristics which render cryptocurrencies appealing also pose serious threats to regulation and law enforcement. The anonymity of transactions combined with the decentralized and cross-border structure of blockchain networks make it difficult to determine who the users are and apply jurisdiction-specific legislation. As a result, cryptocurrencies have become more and more related to different types of financial crime such as money laundering, terrorist financing, tax evasion, ransomware attacks, and illegal trading in darknet markets. The paper discusses the principal types of crime in the context of cryptocurrency and evaluates the challenges encountered by regulatory bodies and law enforcement agencies that might need to overcome these challenges. It also discusses the international regulation reaction, including the involvement of the international standard-setting organizations and the development of compliance systems, including anti-money laundering (AML) and know-your-customer (KYC) systems. Furthermore, the paper also mentions that technological solutions, such as blockchain analytics, are increasingly gaining significance in enhancing investigative potential. The paper concludes that, regardless of the revolutionary potential of cryptocurrencies in terms of financial innovation, their productive regulation involves a moderate and coordinated strategy, incorporating legal and regulatory models, technological progress, and global collaboration.
This thesis, submitted at the Institute for Law and Finance at Goethe University Frankfurt, provides a critical legal and technological analysis of the effectiveness of the Financial Action Task Force framework in addressing money laundering risks arising from decentralized finance. It examines how decentralized blockchain-based systems fundamentally challenge the assumptions underlying traditional anti-money laundering regulation.The study argues that FATF Recommendations, originally designed for centralized financial systems, are structurally incompatible with decentralized architectures that operate without identifiable intermediaries such as Virtual Asset Service Providers. Through an integrated legal and technological assessment, the research demonstrates how privacy-enhancing tools, including non-custodial wallets, cryptocurrency mixers, zero-knowledge proof mechanisms, and cross-chain bridges, obscure ownership trails and significantly impair regulatory oversight.While these technologies are designed to enhance user privacy, they simultaneously enable sophisticated money laundering techniques, including chain hopping, transaction obfuscation, and the untraceable movement of assets across blockchain networks. The thesis further identifies critical regulatory gaps in the application of core FATF standards, particularly in relation to customer due diligence, beneficial ownership transparency, and the implementation of the Travel Rule.A case study of Bosnia and Herzegovina illustrates the practical consequences of fragmented regulatory implementation. Divergent adoption of FATF standards across its entities reflects the broader “Sunrise Issue,” whereby asynchronous global implementation of the Travel Rule generates cross-border inconsistencies and enforcement challenges.To address these structural deficiencies, the thesis proposes a reinterpretation of FATF standards based on the principle of functional equivalence, extending AML obligations to any actor or protocol exercising effective control over financial transactions, irrespective of formal legal classification. It further advocates for the integration of RegTech, tokenization, and machine learning as tools to reconcile regulatory oversight with technological innovation.The research concludes that the current FATF framework remains fundamentally misaligned with the operational realities of decentralized finance. Ensuring the continued integrity of the global financial system will require the adoption of technologically adaptive, risk-based, and internationally coordinated regulatory approaches. Only through such innovation can AML enforcement remain effective in an increasingly decentralized digital economy.
Purpose The purpose of this study is to examine the applicability of conventional criminological theories to white-collar offenders involved in cryptocurrency-related market manipulation, specifically pump-and-dump schemes. Using Sutherland’s differential association (DA) framework as a theoretical foundation, this research tests whether demographic and theoretical factors – such as self-control, DA, anomie and strain – predict illegal financial behavior in emerging digital markets. Design/methodology/approach Survey data from a national sample of US adults on the promotion of cryptocurrencies for financial gain were analyzed using t-tests and regression models. Findings The findings of this study suggest that traditional theories of crime, including DA, anomie and strain, lose predictive significance when demographic variables are considered. High-income, male and younger individuals were most likely to engage in cryptocrime in general. Overall, the results of this study highlight the complexity of white-collar criminality in digital spaces and suggest that financial and demographic factors outweigh conventional criminological theories when predicting involvement in cryptocrime. Originality/value This paper considers early notions of white-collar crime against modern online financial crimes. The authors addressed the intersection of criminological theory and modern cryptocurrency crime.
This study explores the integration of blockchain technology with anti-money laundering (AML) systems to enhance transaction transparency, ensure immutable audit trails, and reduce regulatory non-compliance. Through a mixed-methods approach, including a systematic literature review and hypothetical dataset analysis, the research examines blockchain’s potential to address AML challenges in financial institutions. Findings indicate that blockchain-enabled AML systems improve transaction traceability by 35%, reduce compliance costs by 20%, and enhance audit reliability through immutable ledgers. However, scalability and regulatory harmonization remain barriers. The study proposes a framework for blockchain-AML integration and offers policy recommendations for stakeholders. These results contribute to the discourse on leveraging distributed ledger technology for financial regulatory compliance, highlighting practical and theoretical implications for global banking systems.
Money laundering in cryptocurrency networks poses persistent challenges for financial intelligence units due to the pseudo-anonymous architecture of blockchain systems and the limited effectiveness of conventional rule-based detection methods. This study introduces chaos theory and recurrence quantification analysis (RQA) as a novel framework for characterizing temporal behavioral dynamics in Bitcoin money laundering transactions. Analyzing 46,564 labeled transactions from the Elliptic Bitcoin Dataset spanning 2009-2018, we construct aggregate time series for illicit and licit transaction volumes across 49 discrete temporal steps, corresponding to the dataset’s inherent graph-based snapshot structure, and apply phase space reconstruction techniques to compute three RQA metrics: determinism (DET), laminarity (LAM), and entropy (ENTR). Results reveal paradoxically higher determinism in illicit transactions (38.24% vs. 16.67% for licit), substantially elevated laminarity (35.80% vs. 0.00%), and greater entropy (0.45 vs. 0.00%), indicating that sophisticated obfuscation strategies inadvertently introduce detectable deterministic signatures. Augmenting conventional graph-based features with RQA metrics significantly enhances Random Forest classification performance, reaching near-optimal levels (F1 = 1.000, AUC = 1.000) within the evaluated dataset environment, with entropy emerging as the single most discriminative predictor. While these exceptional results reflect the high fidelity of chaos-based features in capturing structured laundering patterns from this period, they serve as a benchmark for the theoretical potential of nonlinear analysis in blockchain forensics. These findings demonstrate that temporal complexity features offer a powerful diagnostic tool for real-time monitoring and detection of systemic financial crime in evolving cryptocurrency ecosystems.
Modern financial security issues have arisen as a result of the rapid proliferation of decentralized financial systems and cryptocurrencies. One such issue is the identification of individuals who are laundering money in blockchain networks. Blockchain technology's distributed ledgers clarify matters; however, the anonymity of wallet addresses facilitates illicit financial transactions by criminals. It is crucial to have effective methods to identify these crimes, as over $82 billion in cryptocurrencies were associated with money laundering in 2025. This study demonstrates a method for detecting indications of money laundering in blockchain transaction networks through the use of machine learning. The proposed method for identifying unusual patterns in transactions involves the combination of supervised machine learning, graph-based feature extraction, and data cleansing. The system examines transaction graphs to identify unusual patterns that are associated with illicit financial activities by employing techniques such as Random Forest, Gradient Boosting, and Graph Neural Networks.
The introduction section highlights the potential exploitation of financial technology, particularly cryptocurrencies, for terrorist financing, emphasizing the need for empirical research on the involvement of political extremist groups, such as right-wing extremists. Despite theoretical arguments suggesting vulnerabilities in the financial system, limited empirical evidence exists on the extent of cryptocurrency funding within these groups. The chapter aims to address this gap by conducting the first empirical analysis on the financing activities of extreme right-wing groups in the United States.
Open access
Terrorism, Counterterrorism, and Political Violence
Employing a time-varying multifractal approach, we highlight the influence of political narratives and speculative expectations surrounding the second presidency of Trump on Bitcoin efficiency. Bitcoin exhibits persistent dynamics, deviating from the ideal efficiency benchmark. During the anticipation of Trump’s victory, Bitcoin returns became more predictable (less efficient) due to narrative-driven speculation and arbitrage, whereas during his presidency, efficiency increased, reflecting institutional adoption and favorable regulations. In recent months, Bitcoin has regained its natural balance, with efficiency converging to levels observed in non-speculative periods. Thus, U.S. political narratives function as mechanisms of speculative market arbitration, distorting efficiency while favoring decentralized assets.
Web3, the notion of a decentralised internet powered by blockchain technology, has introduced new scams that are masked in legitimacy and perpetrated through social media. Drawing on interviews and social media data, the study reveals that Web3 fraud thrives among African youth due to economic hardship and weak regulatory oversight. It contends that Web3's ethos feeds a population embroiled in the quest for survival, creating an avenue for manipulation in a largely unregulated space. Here, two kinds of fraud thrive: the use of Web3 as a smokescreen by fraudsters and 'community as bailout' coupled with the 'fear of missing out' (FOMO) as an entrapment, thus revealing how 'communities' become exploitative tools within digital economies of trust. It stresses the need for increased Web3 literacy and clearer oversight as essential to addressing fraud, and situates 'Satoshi-Pablo' as a framework for understanding how innovation and exploitation co-exist in Nigeria's digital landscape.
Henrique Yassuyuki Tsuboi, Rafael Sousa Lima, Kleber Vasconcellos de Oliveira
Purpose This study aims to provide an alternative machine learning model to more quickly and efficiently detect addresses on the Ethereum network suspected of involvement in fraudulent activities. Design/methodology/approach This study performed a machine learning technique known as LightGBM. The machine learning model is trained by using a dataset that identifies licit or illicit addresses on the Ethereum network. This study then applies the trained model to predict the probability that a new transaction should be classified as suspicious for money laundering. Findings Through a set of performance metrics, we show that our model outperforms machine learning models from previous studies, better predicting suspicious money laundering activities. The most relevant attributes in identifying an illicit transaction are: (i) the time difference between the first and last activity of the crypto wallet (a short “lifetime” of the address); (ii) the total number of transactions (accounts used only once or a few times) and (iii) the difference in the distribution of values between the crypto wallets (low values). Research limitations/implications Machine learning techniques have great potential to contribute to the activities of government agents, regulatory authorities and accounting professionals. Practical implications This study adds another tool to combat money laundering, which could lead to improvements in auditing and forensic accounting procedures. This study may be of special interest to regulators and policymakers in their anti-money-laundering roles. Originality/value This study adopts a modern technique that can be considered a valuable tool in identifying and combating fraudulent activities on blockchain networks.
Sextortion has rapidly expanded into a global cyber-enabled crime that leverages anonymous digital communication and decentralized payment systems. This study examines the financial infrastructures underlying contemporary sextortion by conducting a two-phase analysis of 87 confirmed cases involving cryptocurrency payments. Using blockchain forensic tools and open-source intelligence, the research traces fund movements across perpetrator-controlled wallets, identifies laundering techniques such as mixers, peel-chain transfers, and exchange-based cash-outs, and links these behaviors to narrative patterns within victim reports. The results reveal a dual-tier ecosystem in which mass-produced, multilingual extortion scripts coexist with divergent laundering typologies that differentiate lower-value, high-volume scams from more organized and higher-yield operations. By integrating qualitative and quantitative evidence, this study provides a forensic framework for detecting illicit cryptocurrency activity, improving threat classification, and strengthening investigative and regulatory responses to sextortion and related crypto-enabled interpersonal crimes.
Daniel Rabitha, Novi Dwi Nugroho, Ismail, Marpuah · 10 authors
This study explores the strategic shift in terrorist financing methods employed by the Mujahidin Indonesia Timur (MIT) network, specifically the transition from decentralized crowdfunding to centralized single-donor mechanisms. Using a qualitative case study grounded in Fraud Diamond Theory, this research investigates how foreign philanthropic channels are manipulated to support militant operations. The findings reveal that single donors possess the technical sophistication to exploit transnational financial systems, specifically through the manipulation of Non-Profit Organizations (NPOs) and informal charity networks. Consequently, this study proposes an enhanced risk-profiling framework for Financial Intelligence Units (FIUs) that prioritizes individual behavioral patterns and ideological alignments over mere transactional volumes, offering critical insights for anticipatory counter-terrorism financing measures
Open access
Crime, Illicit Activities, and Governance
Terrorism, Counterterrorism, and Political Violence
The rapid development of cryptocurrency as a digital financial asset has introduced new challenges for the prevention and eradication of money laundering crimes. While cryptocurrencies offer efficiency, decentralization, and borderless transactions, these very characteristics also create significant vulnerabilities for misuse, particularly in facilitating illicit financial flows. In Indonesia, the existing legal framework on anti-money laundering, primarily regulated under Law Number 8 of 2010, was formulated prior to the widespread adoption of cryptocurrency and therefore faces limitations in addressing technology-driven financial crimes. This article examines the challenges of law enforcement in combating cryptocurrency-based money laundering in Indonesia through a normative juridical approach. The study analyzes relevant statutory regulations, institutional authority, and enforcement mechanisms involving agencies such as PPATK, Bappebti, the Financial Services Authority, and law enforcement bodies. The findings indicate that law enforcement faces substantial obstacles, including regulatory fragmentation, jurisdictional complexities, difficulties in tracing blockchain-based transactions, evidentiary constraints, and limited technical capacity among enforcement institutions. Furthermore, the absence of comprehensive regulation concerning decentralized finance and non-custodial digital wallets exacerbates enforcement difficulties. This article argues that without regulatory harmonization, enhanced institutional coordination, and the integration of technological capabilities into law enforcement practices, the Indonesian legal system risks lagging behind the evolving landscape of financial crime. Strengthening adaptive legal frameworks is therefore essential to ensure effective anti-money laundering enforcement in the digital asset era.
Global illicit fund flows exceed an estimated $3.1 trillion annually, with stablecoins emerging as a preferred laundering medium due to their liquidity. While decentralized protocols increasingly adopt zero-knowledge proofs to obfuscate transaction graphs, centralized stablecoins remain critical transparent choke points for compliance. Leveraging this persistent visibility, this study analyzes an Ethereum dataset to establish an empirical baseline for behavioral AML detection. Our findings demonstrate that domain-informed tree ensemble models achieve higher Macro-F1 score, significantly outperforming graph neural networks, which struggle with the increasing fragmentation of transaction networks. The model's interpretability goes beyond binary detection, successfully dissecting distinct typologies: it differentiates the complex, high-velocity dispersion of cybercrime syndicates from the constrained, static footprints left by sanctioned entities. This methodological approach provides actionable insights that align with industry shifts toward deterministic verification, informing the auditability and compliance requirements under regulations such as the EU's MiCA and the U.S. GENIUS Act while minimizing unjustified asset freezes. By providing a high-precision behavioral classification of suspicious wallets, this approach contributes to raising the economic cost of financial misconduct while informing compliance practice under emerging stablecoin regulations.