Blockchain is a technological innovation that has the potential to radically change our financial markets by providing an alternative management approach to the "promise market", which is the foundation of our financial systems. Its disruptive potential also extends to corporate finance, where blockchain is beginning to influence valuation methods and capital allocation strategies, offering new perspectives on how companies are assessed and financed. However, for a new financial architecture based on blockchain and advancements in technology -- what is commonly referred to as Fintech -- to replace, in whole or in part, traditional finance, it will need to overcome significant challenges such as regulation, environmental sustainability, its association with illegal activities, and achieving greater efficiency in cryptocurrency markets. For this reason, the future of Fintech is likely to be more conventional -- yet also more transparent, efficient, and regulated -- ultimately evolving to resemble the traditional finance we know.
Energy is a fundamental component of modern life, driving nearly all aspects of daily activities. As such, the inability to access energy when needed is a significant issue that requires innovative solutions. In this paper, we propose ED-DAO, a novel fully transparent and community-driven decentralized autonomous organization (DAO) designed to facilitate energy donations. We analyze the energy donation process by exploring various approaches and categorizing them based on both the source of donated energy and funding origins. We propose a novel Hybrid Energy Donation (HED) algorithm, which enables contributions from both external and internal donors. External donations are payments sourced from entities such as charities and organizations, where energy is sourced from the utility grid and prosumers. Internal donations, on the other hand, come from peer contributors with surplus energy. HED prioritizes donations in the following sequence: peer-sourced energy (P2D), utility-grid-sourced energy (UG2D), and direct energy donations by peers (P2PD). By merging these donation approaches, the HED algorithm increases the volume of donated energy, providing a more effective means to address energy poverty. Experiments were conducted on a dataset to evaluate the effectiveness of the proposed method. The results showed that HED increased the total donated energy by at least 0.43% (64 megawatts) compared to the other algorithms (UG2D, P2D, and P2PD).
Özlem Sayılır, Ahmet Özkul, Mehmet Balcılar, Ronald Kuntze
Using blockchain adoption (BCA) data for 81 leading public companies in 2021, this study examines the impact of blockchain adoption on organizations’ environmental, sustainability, and governance performance. Employing the 2022 ESG scores from LSEG (Refinitiv) Database, which assess corporate sustainability performance across environmental, social, and governance dimensions, we regress ESG scores against blockchain adoption levels, company size, and various financial performance metrics. The results from the regression analysis reveal that blockchain adoption is significantly and positively associated with two sub-dimensions of environmental sustainability performance: resource usage and emissions. Additionally, firms exhibiting higher profitability and greater financial leverage appear to more effectively control blockchain adoption to enhance their corporate sustainability performance. These findings support the notion that blockchain adoption offers eco-efficient solutions that contribute to improved corporate sustainability performance, particularly through improved resource management and emissions control, while also offering actionable recommendations for policymakers and industry leaders.
The rapid proliferation of mobile IoT devices with inadequate security measures has elevated security to a critical concern. Researchers have proposed various systems for vulnerability detection based on conventional frameworks. However, these approaches often face challenges such as high computational costs, limited storage capacity, and slow response times. To ensure robust protection against cyberattacks, modern security solutions must continuously monitor and analyze historical data across the entire IoT network. This paper introduces a distributed security framework for IoT networks, leveraging software-defined networking (SDN), blockchain, and edge computing to efficiently detect and mitigate IoT-based attacks. In the proposed framework, SDN facilitates network-wide data monitoring and analysis, enabling effective attack detection. Blockchain technology ensures decentralized and tamper-resistant attack identification, addressing potential vulnerabilities. Meanwhile, the edge computing paradigm enables real-time attack detection at the network edge, ensuring timely alerts. An experimental evaluation of the proposed framework demonstrates its superiority over traditional approaches in terms of detection accuracy (98.7%), false positive rate (1.2%) and response time (101.1 ms), highlighting its effectiveness in securing IoT networks.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
Bu çalışma, kripto para birimlerinin nedensellik ilişkilerini doğrusal olmayan yöntemlerle inceleyerek, bu varlıkların birbirleriyle olan etkileşimlerini daha kapsamlı bir şekilde anlamayı amaçlamaktadır. Çalışmada, 2020'nin ilk haftasından 2022'nin otuz birinci haftasına kadar olan sekiz önemli kripto varlığının (Bitcoin, Ethereum, Tether, USD Coin, Binance Coin, Ripple ve Cardano) haftalık dolar cinsinden döviz kuru verileri kullanılmıştır. Veri seti 135 gözlemi içermektedir. Çalışma, özellikle durağanlık analizi ve doğrusal olmayan nedensellik analizi olmak üzere ekonometrik zaman serisi ve yapay sinir ağı (YSA) analiz yöntemlerini kullanmaktadır. Değişkenlerin durağanlık kararları, üç birim kök testine dayanmaktadır. Bunlar; ADF Testi, PP Testi ve KPSS Testleridir. Değişkenler arasındaki ilişki, Doğrusal olmayan Granger Nedensellik Analizi kullanılarak keşfedilmiştir. Tüm analizler R-Studio programında gerçekleştirilmiştir. Durağanlık analizinde, USDT ve USDC'nin düzeyde (I (0)) durağan olduğu, diğer değişkenlerin ise birinci farkta (I (1)) durağan olduğu belirlenmiştir. Çalışma sonucunda, hiçbir değişken arasında doğrusal olmayan nedensellik ilişkisine rastlanmamıştır.
This research investigates privacy protection mechanisms and data security policy optimization for blockchain-based digital rights management platforms to balance transparency with robust privacy protection. A comprehensive experimental framework was developed, integrating advanced cryptographic techniques with intelligent policy management systems. A multi-layered validation methodology employed formal verification, black-box/white-box testing, and stress tests to validate performance across security, efficiency, and usability dimensions. The implemented solution provided 99.99% security assurance while achieving a 47% improvement in processing efficiency through zero-knowledge proofs and homomorphic encryption. Transaction processing reached 3,750 TPS (peaking at 4,200 TPS), with 99.8% regulatory compliance and 99.9% automated policy conflict resolution. The research demonstrates significant advancements in blockchain-based privacy protection through novel cryptographic implementation and automated policy management, establishing a robust framework for secure digital rights management. This solution offers substantial value for content delivery networks, digital asset management systems, financial institutions, and government services where the balance between transparency and privacy is critical, while reducing compliance management costs.
Asif Ahmad Bhat, Rizal Mohd Nor, Md Amiruzzaman, Md. Rajibul Islam · 5 authors
Blockchain, such as Bitcoin and Ethereum, has received significant attention and widespread usage in recent years. However, blockchain scalability has emerged as a challenging issue. This article explores the existing scalability options for blockchain, which can be categorized into two groups: first layer solutions and second layer solutions. First layer solutions involve network modifications like altering block size, while second layer solutions encompass techniques applied outside of the blockchain. Ethereum, the second largest blockchain, utilizes the Ethereum Virtual Machine (EVM) for executing smart contracts on the blockchain. Currently, there are several EVM-compatible blockchains with noticeable differences. In this study, we evaluated multiple platforms for conducting business processes in trade finance. We considered both Layer 1 and Layer 2 blockchain solutions and examined variations in cost and performance (speed). Based on the evidence gathered in this study, we provide recommendations for system designers to consider when selecting a blockchain platform.
Kyounggon Kim, Seok‐Hee Lee, Sundaresan Ramachandran, Ibrahim Alzahrani
Cybercriminals are employing sophisticated techniques to illegally obtain money from victims, with ransomware, that is the most notorious malware utilized for financial gain. This paper focuses on the Arab world, a prime target region for ransomware gangs. Due to rapid economic growth and digitalization in this region, cybercriminals are increasingly targeting it. However, there is a lack of research on ransomware crime syndication in the Arab region. Data on claimed ransomware victims from 2020 to 2023 was collected from the darknet. Analysis of ransomware gangs in this area revealed significant findings. Based on three years of data collection and analysis, 20 ransomware gangs primarily operating in the Arab region were identified in 2023. Three major ransomware gangs-LockBit, ALPHV/BlackCat, and CL0P-are predominantly targeting the Arab world, with the United Arab Emirates and Saudi Arabia being major targets, along with the manufacturing industry. In addition to identifying the ransomware gangs, the tactics, techniques, and procedures (TTP) used by them were also identified. There was 17 TTPs used by ransomware gangs. This study has also developed a platform to track ransomware gangs and cryptocurrency transactions. Bitcoin’s anonymity and popularity made it the most preferred cryptocurrency by ransomware gangs. This research lays the groundwork for further studies to understand the exact trends and data related to ransomware in the Arab world.
Financial assets often exhibit explosive price surges followed by abrupt collapses, alongside persistent volatility clustering. Motivated by these features, we introduce a mixed causal–noncausal invertible–noninvertible autoregressive moving average generalized autoregressive conditional heteroskedasticity (MARMA–GARCH) model. Unlike standard ARMA processes, our model admits roots inside the unit disk, capturing bubble-like episodes and speculative feedback, while the GARCH component explains time-varying volatility. We propose two estimation approaches: (i) Whittle-based frequency-domain methods, which are asymptotically equivalent to Gaussian likelihood under stationarity and finite variance, and (ii) time-domain maximum likelihood, which proves to be more robust to heavy tails and skewness—common in financial returns. To identify causal vs. noncausal structures, we develop a higher-order diagnostics procedure using spectral densities and residual-based tests. Simulation results reveal that overlooking noncausality biases GARCH parameters, downplaying short-run volatility reactions to news (α) while overstating volatility persistence (β). Our empirical application to Bitcoin and Ethereum enhances these insights: we find significant noncausal dynamics in the mean, paired with pronounced GARCH effects in the variance. Imposing a purely causal ARMA specification leads to systematically misspecified volatility estimates, potentially underestimating market risks. Our results emphasize the importance of relaxing the usual causality and invertibility assumption for assets prone to extreme price movements, ultimately improving risk metrics and expanding our understanding of financial market dynamics.
With the emergence of illegal behaviors such as money laundering and extortion, the regulation of privacy-preserving cryptocurrency has become increasingly important. However, existing regulated privacy-preserving cryptocurrencies usually rely on a single regulator, which seriously threatens users’ privacy once the regulator is corrupt. To address this issue, we propose a linkable group signature against malicious regulators (ALGS) for regulated privacy-preserving cryptocurrencies. Specifically, a set of regulators work together to regulate users’ behavior during cryptocurrencies transactions. Even if a certain number of regulators are corrupted, our scheme still ensures the identity security of a legal user. Meanwhile, our scheme can prevent double-spending during cryptocurrency transactions. We first propose the model of ALGS and define its security properties. Then, we present a concrete construction of ALGS, which provides CCA-2 anonymity, traceability, non-frameability, and linkability. We finally evaluate our ALGS scheme and report its advantages by comparing other schemes. The implementation result shows that the runtime of our signature algorithm is reduced by 17% compared to Emura et al. (2017) and 49% compared to KSS19 (Krenn et al. 2019), while the verification time is reduced by 31% compared to Emura et al. and 47% compared to KSS19.
This paper examines the role of advanced biofuels in promoting energy access and economic growth in rural areas, with a focus on developing countries. Advanced biofuels, produced from non-food biomass sources such as agricultural residues, algae, and waste, have the potential to reduce rural energy poverty while creating economic opportunities. Through case studies of successful initiatives in countries like India and Brazil, this study highlights how decentralized biofuel production has improved energy access, created local employment, and enhanced agricultural value chains. Notable findings include the establishment of community-led biofuel plants that reduced reliance on imported fossil fuels and generated sustainable incomes for farmers by utilizing crop residues. The study also identifies key challenges such as limited infrastructure, access to financing, and policy support, while offering actionable recommendations to scale advanced biofuel adoption. Overall, advanced biofuels present a promising pathway to sustainable rural development by enhancing energy security, reducing environmental impact, and fostering economic growth.
UTokyo Repositoryは本学で生産されたさまざまな学術成果を電子的形態で集中的に蓄積・保存し、世界に発信することを目的としたインターネット上の発信拠点です。 The UTokyo Repository is the system to store and provide digital resources created by members of the University of Tokyo. Its main purpose is to develop digital collections, make them available online, and preserve them for long-term access.
Electronic Health Records (EHRs) are now a necessary component of contemporary healthcare, but managing them presents a number of security, privacy, and interoperability issues. In order to solve these issues, this study introduces a unique framework for EHR management that combines four cutting-edge technologies: blockchain, Zero-Knowledge Proofs (ZKP), Ciphertext-Policy Attribute-Based Encryption (CP-ABE), and InterPlanetary File System (IPFS). Our solution makes use of the Ethereum blockchain for transparent and safe record-keeping, IPFS for efficient and decentralized data storage, CP-ABE for fine-grained access control, and ZKP for private authentication. We offer computational proofs for important components together with a thorough security analysis utilizing formal verification tools like ProVerif and Tamarin Prover. Comparing our framework to other alternatives, the findings show that it provides stronger security guarantees, better privacy protection, and increased scalability. Our approach also defends against a broader variety of possible threats, such as man-inthe-middle attack, repudiation attacks, and side-channel attacks. This work opens the door for more effective and patient-cantered healthcare information systems by advancing secure and privacy-preserving EHR management.
This research aims to deeply explore the origin of the metaverse and its impact on the development of corporate strategy, especially in the field of non-fungible token (NFT) artwork design. Through the analysis of the current development status of NFT artworks, an artificial intelligence-based enterprise development strategy and a metaverse NFT artwork design method are proposed. In addition, the generation logic, technical attributes, and technical and legal risks of metaverse NFT artworks under different design methods are also studied. It is expected that this research will provide strong theoretical support and practical guidance for the legal protection of metaverse NFT artwork design.
Blockchain networks have become a cornerstone of decentralized finance and digital asset management, yet they remain susceptible to fraudulent activities, money laundering, and illicit financial transactions. Traditional anomaly detection methods, including rule-based systems and supervised machine learning models, often struggle to generalize across evolving blockchain transaction patterns due to their reliance on static heuristics and manually engineered features. Graph-based learning techniques offer a more robust approach by leveraging the inherent structure of blockchain transactions, where wallets and transactions form a dynamic graph.This study proposes a novel Spatial-Temporal Graph Neural Network (STGNN)-based anomaly detection framework for blockchain transactions. By modeling transaction flows as evolving graphs, the proposed system captures both spatial dependencies between wallets and temporal patterns in transaction sequences. The framework employs Graph Convolutional Networks (GCN) or Graph Attention Networks (GAT) to extract spatial representations, while Gated Recurrent Units (GRU) or Temporal Convolutional Networks (TCN) model the time-dependent evolution of transaction behaviors. The fusion of these spatial-temporal features enables the detection of anomalous transactions that deviate from expected network behaviors.Experimental evaluations on real-world blockchain datasets demonstrate that the STGNN-based model achieves higher detection accuracy, lower false positive rates, and better adaptability than traditional fraud detection techniques. The study further explores the system's scalability and generalization across different blockchain networks, revealing its potential for real-time monitoring of illicit financial activities. These findings highlight the effectiveness of graph-based deep learning models in strengthening blockchain security and provide a foundation for future research in decentralized fraud detection, anti-money laundering (AML) compliance, and intelligent financial surveillance.
Giovanni Rosa, Simone Scalabrino, S. Mastrostefano, Rocco Oliveto
Abstract Smart contracts, i.e., self-executing contracts written in code, have gained popularity in recent years due to the introduction of blockchain technology. These contracts are executed automatically when certain conditions are met, and, once deployed, they can not be modified. This presents issues when errors are found or updates are needed. Previous research has mainly focused on introducing approaches and tools for detecting bugs or vulnerabilities in smart contracts. However, it is unclear if these are the only maintenance-related operations developers perform. In this paper, we aim to understand why and how developers maintain smart contracts. We run a qualitative analysis on 590 commits from 14 open-source smart contract repositories written in Solidity, the most popular programming language for smart contracts. We analyze the commit messages, related issues, and the changes made to understand what triggered changes. Then, we examine how developers changed the source code. As a result, we define two taxonomies: one reporting the reasons for the maintenance and one regarding the patterns of modifications. Our findings suggest that smart contract maintenance is often focused on improving the internal quality of the scripts (40% of the cases), and that many changes aim to fix bugs despite the several approaches available for detecting them beforehand.
Phoebe Wong, Wilson K.S. Leung, Markus Vanharanta, Calvin Wan
Purpose Consumer adoption of decentralized blockchain solutions, such as decentralized finance (DeFi) applications, has demonstrated considerable technological promise. However, to benefit from DeFi applications, consumers must purchase and own cryptocurrencies, which is a potential obstacle to adopting decentralized blockchain technology. This study employed a push-pull-mooring model to examine factors influencing individuals’ willingness to use cryptocurrencies. In particular, how do push (i.e. diminishing value and pricing problems), pull (i.e. relative security and perceived value) and mooring (i.e. switching cost and personal innovativeness) factors shape individuals’ switching intentions. Design/methodology/approach About 300 valid responses were collected via an online survey and analyzed using partial least squares structural equation modeling (PLS-SEM). Findings The results confirm that the factors of push (i.e. pricing problem and low perceived value of traditional fiat money), pull (i.e. relative security and perceived value of cryptocurrency) and mooring (i.e. switching cost and personal innovativeness in technology) significantly impact switching intention to cryptocurrency. These findings offer key insights and implications for consumer adoption of cryptocurrencies as a precursor to participating in decentralized blockchain ecosystems. Originality/value Cryptocurrencies have been associated with numerous risk and security concerns, potentially holding back consumer adoption of DeFi financial solutions. Accordingly, this paper contributes to extending the knowledge of consumer adoption of cryptocurrency, switching from traditional money to using cryptocurrencies based on the push-pull-mooring theory (PPM). This allows for a detailed analysis of the critical factors that hinder or promote consumers' adoption of decentralized blockchain solutions.
This study conducted a systematic literature review (SLR) on the function and potential of non-fungible tokens (NFTs) and blockchain technology with regard to ownership in gaming. We explored theoretical viewpoints, analytical frameworks, measures, relationships, and procedures up to 2024 to examine the relationship between NFTs, blockchain, and ownership in gaming. A four-step methodology (planning, execution, analysis, and reporting) was followed, based on Tranfield et al.'s (2003) approach. The widely adopted PRISMA technique was utilized to ensure transparency and rigor in conducting the systematic literature review. The contribution of this study lies in its exploration of how NFTs and blockchain transform ownership in gaming, marking the first comprehensive SLR on this topic from the standpoint of digital asset ownership transformation. The [*expected*] findings suggest that NFTs and blockchain significantly transform traditional ownership structures in gaming by enabling decentralized, verifiable, and transferable digital assets. However, fully realizing this potential remains challenging. Persistent issues—such as partial rather than complete decentralization, economic inequalities and speculation-driven markets, psychological complexities around player motivation, and technological hurdles like scalability and interoperability—complicate the path toward sustainable NFT-based ecosystems. Adoption appears influenced as much by hype and financial incentives as by informed choice. These insights underscore the necessity for developers, policymakers, and stakeholders to collaborate on clear legal frameworks, robust security measures, equitable economic models, and player-centric designs. In doing so, the gaming industry can foster genuine, enduring ownership experiences that align innovation with fairness, trust, and meaningful engagement.
ABSTRACT Blockchain technology, when combined with smart contracts, enables buyers to distinguish between greenwashed and genuinely eco‐friendly products. The presence of counterfeit items can severely impact supply chains by diminishing brand value, eroding consumer confidence, and undermining market trust. This article explores how smart contracts can help mitigate the circulation of counterfeit goods and safeguard brands by establishing institutional trust through tamper‐proof data, enhanced transparency, and improved traceability. Information asymmetry on digital marketing platforms significantly contributes to the proliferation of greenwashed counterfeit goods. We introduce an infection‐leakage model based on anecdotal case evidence to explain the interactions between different market types. The transition from relying solely on traditional written contracts, certifications, and brands to incorporating blockchain and smart contract technology is analyzed for its potential to strengthen supply chains and curtail the spread of counterfeit greenwashed products. Blockchain technology provides consumers with detailed product information, empowering them to choose authentic green products over counterfeit “lemons.” Our theoretical framework suggests that this shift to blockchain smart contracts can reduce the transaction costs associated with counterfeit infiltration, thereby protecting brands and the intellectual property rights of authentic sustainable products.
This paper proposes a sociotechnical framework to address these issues by integrating fairness metrics, explainable AI (XAI), and game-theoretic models. We adapt statistical fairness criteria (demographic parity, equal opportunity) to audit bias, extend SHAP values to blockchain data for transparency in DeFi, and simulate stakeholder dynamics using agent-based models. Novel contributions include a governance-aware fairness metric that combines technical parity with stakeholder trust scores and a multi-layer agent model linking AI behavior to decentralized governance. Our findings reveal that DeFi systems exhibit narrower bias gaps than traditional systems but introduce new risks (e.g., collateral volatility), while profit-driven DAO governance often prioritizes short-term gains over systemic stability. This work advances interdisciplinary approaches to AI governance, emphasizing the need to reconcile technical robustness with social accountability.
Lending within decentralized finance (DeFi) has facilitated over \$100 billion of loans since 2020. A long-standing inefficiency in DeFi lending protocols such as Aave is the use of static pricing mechanisms for loans. These mechanisms have been shown to maximize neither welfare nor revenue for participants in DeFi lending protocols. Recently, adaptive supply models pioneered by Morpho and Euler have become a popular means of dynamic pricing for loans. This pricing is facilitated by agents known as curators, who bid to match supply and demand. We construct and analyze an online learning model for static and dynamic pricing models within DeFi lending. We show that when loans are small and have a short duration relative to an observation time $T$, adaptive supply models achieve $O(\log T)$ regret, while static models cannot achieve better than $Ω(\sqrt{T})$ regret. We then study competitive behavior between curators, demonstrating that adaptive supply mechanisms maximize revenue and welfare for both borrowers and lenders.
The rapid digitalization of banking services has significantly transformed financial transactions, offering enhanced convenience and efficiency for consumers. However, the increasing reliance on digital banking has also exposed financial institutions and users to a wide range of cybersecurity threats, including phishing, malware, ransomware, data breaches, and unauthorized access. This study systematically examines the influence of cybersecurity threats on digital banking security, adoption, and regulatory compliance by conducting a comprehensive review of 78 peer-reviewed articles published between 2015 and 2024. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this research critically evaluates the most prevalent cyber threats targeting digital banking platforms, the effectiveness of modern security measures, and the role of regulatory frameworks in mitigating financial cybersecurity risks. The findings reveal that phishing and malware attacks remain the most commonly exploited cyber threats, leading to significant financial losses and consumer distrust. Multi-factor authentication (MFA) and biometric security have been widely adopted to combat unauthorized access, while AI-driven fraud detection and blockchain technology offer promising solutions for securing financial transactions. However, the integration of third-party FinTech solutions introduces additional security risks, necessitating stringent regulatory oversight and cybersecurity protocols. The study also highlights that compliance with global cybersecurity regulations, such as GDPR, PSD2, and GLBA, enhances digital banking security by enforcing strict authentication measures, encryption protocols, and real-time fraud monitoring.
The nature of virtual assets and their legal regulation is a challenge for policymakers, because virtual assets themselves are a new phenomenon in the field of social and economic relations, which is significantly different from established types of property. The market of virtual assets, which has achieved significant development over the past 10 years, is of interest for research and from a fiscal point of view, because despite its significant volume, agreed approaches to the taxation of operations carried out in such a market are absent or are at the stage of development. A significant number of new challenges facing the legislator when determining the tax regime of operations with virtual assets arise from their qualities, which are categorically different from other types of assets. Virtual assets have a significant number of subspecies, which on the one hand are significantly different from each other, and on the other hand share common features. In particular, the most famous virtual assets - Bitcoin, Ethereum are completely decentralized, do not have a specific issuer, do not certify any civil rights of the owner, and do not have security. On the other hand, such types of virtual assets as electronic money tokens («stablecoins») or tokens related to assets are a form of expression of civil rights, namely the rights of claim against the issuer. Thus, it is problematic to determine which set of characteristics to use to distinguish virtual assets from other types of property while taking into account the full range of diversity of virtual assets themselves. In addition, transactions with virtual assets take place in forms different from transactions with cash, securities, etc. The ability of subjects to store, exchange, acquire and alienate virtual assets without the participation of any financial institutions or other intermediaries is another challenge in rulemaking, because it complicates the application of existing control methods in the field of taxation. A separate category of problems is also the phenomenon of decentralized finance («DeFi»), which eliminates intermediaries not only from the basic operations of moving virtual assets, but also from more complex economic operations, such as credit activities, loans, collateral, derivative contracts, etc. Considering the above, the relevance of the research lies in the emergence of qualitatively new categories of social relations, which, like any other social and economic relations, require legal regulation. Currently available regulatory instruments are not able to fully cover all the variety of operations with virtual assets, and to provide appropriate, special regulation of them.
This article examines the integration of blockchain technology and smart contracts within financial regulatory systems and their potential to transform traditional compliance frameworks. The distributed and immutable nature of blockchain presents unique opportunities for enhancing regulatory reporting, fraud detection, and compliance monitoring in financial institutions. Through analysis of implementation cases and theoretical frameworks, this article identifies key applications in automated reconciliation, real-time monitoring, and cross-border regulatory coordination. Despite promising benefits in transparency and automation, significant challenges persist in scalability, legacy system integration, and regulatory uncertainty. This article contributes to the growing body of literature on regulatory technology by providing a comprehensive examination of blockchain applications in financial oversight, offering insights for both regulatory bodies and financial institutions navigating this technological transition. The articles suggest that while blockchain implementation requires substantial infrastructure adaptation, its potential to create more efficient, transparent, and secure regulatory systems warrants continued exploration and development.