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
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.
Research background and purpose Digital technologies offer tangible economic benefits but are also exposed to the risk of misuse. Crowdfunding is a special support form for business, cultural or social enterprises. Due to anonymity, fragmentation of capital and wide coverage, crowdfunding transactions are particularly vulnerable to the risk of criminal activities related to the concealment of the source of income or illegal changes of the financing objective. This article addresses the risks of money laundering and terrorism financing, particularly on the specifics of crowdfunding. Research has proposed a synthetic risk indicator for AML/CFT, which may measure the level of risk and vulnerability of crowdfunding to money laundering and terrorism financing. Design/methodology/approach The discussion in the article is presented against the background of a comprehensive and integrated review of literature, covering national and foreign sources. The theoretical part of the article utilizes: method of analysis and criticism of literature, analysis and synthesis, and method of analysis and logical construction. In the empirical part, to assess the level of risk and vulnerability of crowdfunding to AML/CFT risk compared to other areas, a research procedure based on the TOPSIS linear ordering method was used. The analysis covers the years 2019 and 2023. Findings The results of the studies show that crowdfunding is one of the most vulnerable areas at risk of money laundering and terrorism financing. The high position in the ranking in 2019 and 2023 resulted mainly from the dynamic development of the crowdfunding market in Poland, its increasing availability, a high degree of decentralization, the occurrence of cross-border transactions and the increasing diversity of platforms in their business model. Maintaining the benefits of crowdfunding requires the simultaneous implementation of effective remedies, increased campaign transparency and close cooperation with supervisory authorities and institutions combating financial crime. Value added and limitations The study makes an important contribution to the literature on the subject, providing information on the criminality of crowdfunding. The results of the study can be used by supervisory and regulatory authorities as a tool for shaping security in innovative segments of the financial system. The main limitation was the relatively small number of variables selected for the synthetic measure.
Abstract This research investigates how cryptocurrencies are used in illegal markets, including darknet marketplaces, ransomware payments, and money laundering. The study examines transaction patterns, anonymity techniques, and the tools used by cybercriminals to hide illicit flows. To understand the increasing complexity of these activities, the research explores how digital currencies enable fast, borderless, and pseudonymous transactions that often bypass traditional financial regulations and monitoring systems. The study combines blockchain analysis, case studies, and expert observations to map these illegal flows and identify system vulnerabilities. By assessing the role of mixing services, privacy-oriented cryptocurrencies, decentralized exchanges, and chain-hopping techniques, the research highlights the methods used to obscure the origin and destination of digital assets. These insights help reveal how criminals exploit technology to move funds in ways that challenge traditional policing mechanisms. The findings aim to provide valuable insights for policymakers, cryptocurrency exchanges, and law enforcement agencies to improve detection, prevention, and regulation of illicit cryptocurrency activities. The research also examines current regulatory frameworks, global compliance standards, and existing technological tools used to trace illegal transactions. Furthermore, it discusses the challenges faced by authorities, including cross-border jurisdiction issues, lack of unified regulations, and the rapid advancement of blockchain technologies. Overall, this study contributes to a deeper understanding of how illegal cryptocurrency markets operate and highlights opportunities for strengthening cybercrime prevention through improved regulations, data-sharing frameworks, and innovative blockchain forensic solutions.
This study explores the intersection of cryptocurrency, cybercrime, and global governance. It focuses on identifying criminal techniques, analyzing forensic and regulatory countermeasures, and evaluating the broader governance dilemmas that arise. A qualitative desk-based approach was employed, synthesizing secondary data from peer-reviewed studies, institutional policy papers (FATF, IMF, Europol), and industry reports (Chainalysis, Elliptic, TRM Labs). Thematic content analysis was used to trace patterns in illicit cryptocurrency use, law enforcement responses, and regulatory innovations. The findings indicate that while advances in blockchain forensics and policy coordination have strengthened oversight, criminals increasingly exploit decentralized finance platforms, cross-chain laundering, privacy coins, and mixers to evade detection. Enforcement remains uneven, hindered by fragmented regulations and gaps in cross-border cooperation. Overall, the study concludes that cryptocurrency-enabled cybercrime remains a resilient and evolving threat that challenges the stability of the global financial system and exposes weaknesses in governance frameworks. Without stronger coordination, adaptive regulation, and robust technological capabilities, the risks of illicit finance will continue to outpace control efforts. To mitigate these risks, the study recommends enhancing cross-border collaboration, investing in advanced blockchain forensic tools, and adopting flexible, multi-stakeholder governance models that balance innovation with accountability.
Anti-money laundering (AML) remains a critical challenge in cryptocurrency ecosystems, where blockchain’s transparency paradoxically coexists with pseudonymity. Traditional methods often fall short in modeling the temporal and structural complexity of transaction networks. This paper introduces ChronoWave-GNN, a graph neural framework designed from the theoretical perspective of time-frequency representation learning. By combining wavelet-based frequency decomposition with temporal encoding, our model captures nonstationary and multi-scale patterns inherent in illicit financial activity. This dual-domain perspective enhances the expressive capacity of graph representations without relying on modular patching. We validate our approach on the Elliptic dataset, where ChronoWave-GNN achieves a test accuracy of 0.9802 and F1-score of 0.9799, surpassing prior state-of-the-art results. These findings suggest that unifying temporal dynamics and spectral compression offers a principled and effective pathway for robust AML in decentralized financial systems.
The rise in Blockchain-based digital assets has transformed the financial ecosystems, which has also created complex governance and taxation challenges. The pseudonymous and cross-border nature of crypto transactions undermines traditional tax enforcement, leaving regulators such as the South African Revenue Service (SARS) reliant on voluntary disclosures with limited verification mechanisms, while existing Blockchain forensic tools and regulatory technologies (RegTechs) have advanced in anti-money laundering and institutional compliance, their integration into issues related to taxpayer compliance and locally adapted solutions remains underdeveloped. Therefore, this study conducts a state-of-the-art review of Blockchain forensics, RegTech innovations, and crypto tax frameworks to identify gaps in the crypto tax compliance space. Then, this study builds on these insights and proposes a conceptual model that integrates digital forensics, cost basis automation aligned with SARS rules, wallet interaction mapping, and non-fungible tokens (NFTs) as verifiable audit anchors. The contributions of this study are threefold: theoretically, which reconceptualise the adoption of Blockchain forensics as a proactive compliance mechanism; practically, it conceptualises a locally adapted proof-of-concept for diverse transaction types, including DeFi and NFTs; and lastly, innovatively, which introduces NFTs to enhance auditability, trust, and transparency in digital tax compliance.
Financial identity systems were built for humans. Know-Your-Customer (KYC), Anti-Money Laundering (AML), and beneficial ownership frameworks assume that economic actors are natural persons or legally incorporated entities. That assumption no longer holds. Autonomous artificial intelligence agents now negotiate contracts, execute procurement, trade digital assets, allocate treasury capital, and conduct cross-border transactions without real-time human intervention. Yet these agents possess no formal financial identity. This article introduces Know-Your-Agent (KYA)—a governance framework that extends financial identity infrastructure beyond humans to autonomous systems. Article argue that AI agents operating in financial contexts must be identifiable, accountable, auditable, and risk-classified. We develop a layered identity architecture, outline an agent risk scoring model, explore behavioral drift monitoring, analyze legal liability structures, and examine regulatory implications across jurisdictions. Through detailed use cases in retail procurement, decentralized finance (DeFi), enterprise treasury management, and IoT payment ecosystems, we demonstrate why KYA is not optional but foundational for the next generation of digital trust infrastructure. The article concludes with a strong future research agenda spanning explainability standards, cross-jurisdictional identity portability, agent-to-agent contract governance, systemic risk modeling, and the emergence of AI insurance markets.
This article explores Jeffrey Epstein's financial habits, focusing on his longstanding efforts to evade traditional banking oversight and his early interest in emerging financial technologies such as Bitcoin. While there is no evidence linking cryptocurrency to his criminal activity, newly released records and reporting reveal that Epstein studied and invested in technology, seeking ways to minimize reliance on regulated intermediaries. The piece argues that Epstein's attraction to Bitcoin's features, especially its capacity to bypass formal banking structures, parallels the reasons cryptocurrency later became popular in illicit financial networks.
The rapid expansion of blockchain-based financial systems has fundamentally transformed the structure of economic crime. While distributed ledger technologies provide unprecedented transparency, they simultaneously enable pseudonymous interactions that can be exploited for illicit financial activities, including money laundering and tax evasion. This paper develops a theoretical and computational framework for detecting illicit financial behavior in blockchain networks. By integrating economic criminology, graph-based analysis, and machine learning techniques, it proposes a composite detection model capable of identifying suspicious transaction patterns through structural and behavioral indicators. The study argues that blockchain-based financial crime is not hidden but structurally embedded within transparent systems, requiring algorithmic interpretation rather than traditional investigative approaches. The findings highlight the importance of scalable, data-driven enforcement mechanisms and coordinated regulatory responses in addressing financial crime in decentralized environments.
This report examines the convergence of generative artificial intelligence, cryptocurrency laundering infrastructure, and cross-border social engineering in the evolution of romance scam-enabled financial crime affecting Canadian institutions. Drawing on reporting from the Federal Bureau of Investigation Internet Crime Complaint Center (FBI IC3), the Financial Transactions and Reports Analysis Centre of Canada (FINTRAC), the Royal Canadian Mounted Police (RCMP), and blockchain analytics firms Chainalysis and TRM Labs, the analysis identifies a measurable transition from opportunistic, manually-operated fraud schemes toward industrialized transnational operations. The report documents how AI-generated personas, deepfake impersonation tools, and multilingual automation systems have reduced operational costs for fraud actors while increasing victim acquisition at scale. Particular attention is given to cryptocurrency laundering pathways — including stablecoin conversion, decentralized finance (DeFi) layering, cross-chain transfers, and over-the-counter (OTC) broker off-ramping — that exploit the opacity of digital asset ecosystems and exceed the detection capabilities of traditional threshold-based anti-money laundering (AML) monitoring systems. The report further assesses Canada's specific vulnerability profile, attributing heightened exposure to widespread Interac e-Transfer adoption, high public trust in digital financial systems, and fragmented cross-border compliance coordination. Three systemic risk vectors are identified and analyzed: the industrialization of victim acquisition, increased laundering opacity through decentralized cryptocurrency infrastructure, and the systematic exploitation of Canadian digital payment rails. Recommendations are directed at the Economic and Financial Crimes Commission (EFCC), Nigerian financial intelligence agencies, and Canadian financial institutions and cryptocurrency platforms.
Blockchain technology has revolutionized numerous industries by providing decentralized, transparent, and immutable ledgers. However, its adoption is hindered by persistent security challenges, including arbitrage attacks, liquidity exploits, and noncompliance with antimoney laundering (AML) regulations. This paper proposes an enhanced framework to address these issues, combining dynamic pricing mechanisms, AI‐based anomaly detection, and regulatory compliance checks within a multilayered architecture. The framework is composed of five interconnected layers: the input layer for data collection and validation, the data warehouse layer for structured data classification, the processing layer for anomaly detection and pricing adjustments, and the decision layer for transaction validation, execution, and reporting. The integration of these layers ensures robust security and compliance mechanisms, reducing system vulnerabilities while optimizing efficiency. To validate the proposed framework, we conducted simulations using real‐world blockchain scenarios, including decentralized finance (DeFi) platforms and cryptocurrency exchanges. Results demonstrate significant reductions in arbitrage opportunities and liquidity risks, with improved accuracy in anomaly detection and compliance adherence. For instance, the dynamic pricing mechanism mitigated 87% of arbitrage attack attempts, while the AI‐based anomaly detection achieved an 89% accuracy rate in identifying high‐risk transactions. This study provides actionable insights and a scalable solution for enhancing blockchain security and trust. Future work will focus on integrating cross‐chain interoperability, real‐time threat intelligence, and privacy‐preserving techniques to further expand the framework’s applicability. By addressing critical vulnerabilities, this research contributes to the development of secure, transparent, and compliant blockchain ecosystems, paving the way for wider adoption across industries. Unlike previous blockchain security models, our framework introduces a real‐time, AI‐enhanced risk assessment mechanism that dynamically updates transaction risk scores, mitigating financial threats in decentralized environments. This holistic approach provides a scalable, explainable, and adaptive security system that not only protects decentralized financial infrastructures but also aligns with emerging regulatory requirements, ensuring long‐term applicability.
This research assesses the key blockchain infrastructures utilized in decentralized finance, tokenized asset markets, institutional settlement systems, and the development of new digital financial ecosystems. The paper focuses on both Layer 1 and Layer 2 networks, emphasizing aspects such as scalability, institutional uptake, privacy frameworks, transaction traceability, and alignment with regulations. Data sourced from credible industry publications, official institutional announcements, and peer-reviewed research suggests that Ethereum remains the leading platform for decentralized finance and tokenized asset infrastructure. In contrast, Solana has shown significant institutional growth through stablecoin settlements, tokenized real-world assets, and enterprise collaborations. Chains that prioritize privacy are increasingly facing regulatory challenges due to anti-money laundering (AML) obligations. The study concludes that hybrid blockchain models, which integrate public settlement with compliance frameworks, are the primary pathway for adoption within the financial sector.