We employed Multifractal Detrended Fluctuation Analysis (MF-DFA) and Refined Composite Multiscale Sample Entropy (RCMSE) to investigate the complexity of Bitcoin, GBP/USD, gold, and natural gas price log-return time series. This study provides a comparative analysis of these markets and offers insights into their predictability and associated risks. Each tool presents a unique method to quantify time series complexity. The RCMSE and MF-DFA methods demonstrate a higher complexity for the Bitcoin time series than others. It is discussed that the increased complexity of Bitcoin may be attributable to the presence of higher nonlinear correlations within its log-return time series.
The shipping market involves multiple stakeholders, including cargo owners, shipping companies, and maritime authorities. To prevent issues such as information concealment and privacy breaches, these stakeholders often establish complex and multi-layered data exchange processes, which significantly reduce the operational efficiency of the shipping market. Decentralized blockchain technology can prevent data tampering, thereby establishing an effective consensus mechanism among multiple parties. This paper first summarizes the application scenarios of blockchain technology in the shipping market, including supply chain management, smart contracts, port management, crew management, marine insurance, and environmental protection. Subsequently, using real-world cases such as TradeLens, CargoX, COSCO SHIPPING Hi ECO, and Insurwave, the advantages of blockchain technology in enhancing shipping efficiency and reducing costs are elaborated. Finally, the challenges of applying blockchain technology in the shipping market, from both technical and regulatory perspectives, are outlined.
Managing finances in a supply chain today is not as straightforward as it once was. The world is constantly shifting—markets fluctuate, risks emerge unexpectedly—and companies are continually trying to stay one step ahead. In all this, financial resilience has become more than just a strategy. It is a survival skill. In our research, we examined how newer technologies (such as blockchain and the Internet of Things) can make a difference. The idea was not to reinvent the wheel but to see if these tools could actually make financing more transparent, reduce some of the friction, and maybe even help companies breathe a little easier when it comes to liquidity. We employed two optimization methods (Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) to achieve a balanced outcome. The goal was lower financing costs, better liquidity, and stronger resilience. Blockchain did not just record transactions—it seemed to build trust. Meanwhile, the Internet of Things (IoT) provided companies with a clearer picture of what is happening in real-time, making financial outcomes a bit less of a guessing game. However, it gives financial managers a better chance at planning and not getting caught off guard when the economy takes a turn.
Journal of Theoretical and Applied Information Technology
Digital identity management that incorporates blockchain technology provides a safe and effective way to enhance safety systems. Typical issues with older methods of managing identities include data breaches, identity theft, and unauthorized access. This encrypted, decentralized, and irreversible blockchain method ensures 100% accurate identification verification by eliminating any weak points and consolidating power. By further automating the authentication process, smart contracts aim to boost transparency while lowering fraud risks. This paper focuses on how safety-critical settings may leverage blockchain-based digital identity management systems to provide data accessibility, integrity, and privacy. In addition to assisting corporations in meeting and exceeding security laws, blockchain, a distributed ledger technology, gives individuals more control over their data. The suggested method discourses problems like interoperability, scalability and regulatory constraints, paving the way for a more secure and reliable structure. The primary emphasis of this research is on the revolutionary potential of blockchain technology as it pertains to identity management. Therefore, current safety systems will have enhanced dependability, security and efficiency.
Adimas Agus Ahmad Asy’arie, Arlinta Prasetian Dewi, Binti Nur Asiyah
This paper proposes a conceptual blockchain-based Sharia accounting model designed to enhance transparency and trust within Islamic Financial Institutions (IFIs). The research addresses the growing yet challenged Islamic finance industry by leveraging blockchain's inherent features, including immutability, decentralization, and smart contracts. The model aims to improve accountability, reduce fraud, and strengthen Sharia compliance by providing real-time, verifiable financial records. This approach offers a novel contribution by systematically integrating advanced technology with Islamic ethical principles, leading to more robust and reliable financial reporting for all stakeholders.
Development of Centralization and Decentralization Political Policy Changes in the socio-political atmosphere in Indonesia that emerged, as well as the economic crisis then developed into a socio-political crisis had implications for changes in various fields, including the field of education The issue of centralization and decentralization, which had previously been raised as an effort to empower the regions, has become stronger. Driven by the atmosphere of state political change, it is increasingly believed that one of the important efforts that must be made in improving the quality of education is to empower schools through MBS (School Based Management), which essentially provides authority and delegation of authority (delegation of authority) to schools to make improvements and quality continuous improvement. The approach in this research is qualitative with the type of case study research, data collection techniques through observation, interviews, documentation. The results of the research The method used in system development is the iteration method where the work of a stage can be done repeatedly and if there are errors it can be corrected immediately, the stages of the iteration method are: Survey, Analysis, Design, Creation, Implementation, Maintenance. The results of this study can be concluded that the financing available at SD Negeri 2 Cikalong School only relies on BOS funds from the government. The allocation of funds is used to meet the National Education Standards. The obstacles that exist in financing education at SD Negeri 2 Cikalong are the limited funds received from the government.
In Kenya’s small-scale agricultural sector, where productivity is often hindered by limited credit access, delayed payments, and exploitative intermediaries, blockchain-based smart contracts offer a bold new frontier. This study explores how these automated, self-executing agreements built on decentralized blockchain platforms are reshaping the performance landscape for smallholder farms. By enabling secure, transparent, and trustless transactions, smart contracts promise to streamline value chains, enforce agreements without intermediaries, and accelerate financial flows. Guided by a pragmatic philosophy and a mixed-methods design, this research draws on both quantitative and qualitative data. Focus group discussions with village champions provided contextual insight into farmers’ lived experiences with smart contract platforms. A census of 52 blockchain-enabled agricultural firms yielded structured survey data, analyzed using descriptive statistics, thematic coding, Pearson and Spearman correlation tests, and regression modeling. Findings show that smart contracts significantly improve farm performance by enhancing payment reliability, reducing transactional friction, and increasing transparency in financial and operational records. The technology also fosters trust and coordination among stakeholders, while facilitating access to financing and new markets. Ultimately, this study underscores the transformative potential of blockchain smart contracts in small-scale farming not only as a technical innovation but as a catalyst for inclusive agricultural development. However, realizing this potential at scale will require sustained investment, policy support, and farmer-centered digital literacy initiatives.
Bitcoin is a decentralized digital currency, without a central bank or single administrator, which can be sent from user to user on the peer-to-peer bitcoin network without the need for intermediaries.Bitcoin, block chains, and crypto currencies are very complicated concepts because there are so many factors to understand.These aspects are hard to analyze because it is not constrained to a single academic discipline.The multidisciplinary nature of bitcoin one of the reasons why this is considered as an academic black hole, and when you try to understand each element, every concepts simply synthesises more questions.The research should start with 'What is Bitcoin?' but the answers would come from the disciplines of economics, law, computer science, finance, civil society, history, geopolitics, and more.We can write an entire academic series of books upon the bitcoin and have plenty of factors to be still addressed.
Bitcoin, the first decentralized digital currency, was introduced in 2009 by an unnamed individual named Satoshi Nakamoto. Since then, it has evolved from a niche cryptography experiment to a globally recognized technological and financial innovation. Blockchain technology, a distributed ledger that ensures decentralization, transparency, and immutability, is the foundation of Bitcoin. By eliminating the need for intermediaries like banks or governments, Bitcoin offers a peerto-peer financial system that challenges the current monetary paradigm. This essay explores the many aspects of Bitcoin's evolution, including its technical underpinnings and socioeconomic implications. The blockchain raises concerns over energy consumption and environmental sustainability even if its dependence on proof- of-work consensus processes ensures transaction integrity. Due to its set supply of 21 million coins, which provides the illusion of digital scarcity, many people view Bitcoin as "digital gold" and a potential inflation hedge. In addition to serving as a mediumofcommerce, Bitcoin has spurred a global movement toward decentralizationandfinancial sovereignty. It has an impact onavariety of things, such as investment portfolios, fintech innovation, cross-borderremittances, and even monetary policy. Bitcoin's ability to upend establishedeconomies is demonstrated by the fact that nations like El Salvador have made it legal tender. But there have been obstacles to Bitcoin'sexpansion. Mainstreamacceptance is still significantly hampered by regulatoryuncertainties, market volatility, scalabilityproblems, and usage for illegal purposes. Furthermore, public opinion and policyarestill influenced by the current discussionsurrounding environmental sustainabilityandcentral bank digital currencies (CBDCs). Bitcoin is at the nexus of philosophy, technology, and finance as it develops further. This essay explores its limitations andpossibilities for the future while criticallyanalyzing its transformative impact. Througha technological, economic, and regulatoryanalysis, this study seeks to advanceknowledge of Bitcoin's influence onthedirection of international finance.
Cryptocurrency markets are highly volatile, creating challenges for accurate risk management and forecasting. As digital assets become more integrated into financial systems, understanding their volatility dynamics is essential for investors and policymakers. Previous research has primarily applied standard GARCH models to cryptocurrencies, often neglecting advanced specifications that capture asymmetry, regime-switching, and long-memory effects. This limits the accuracy of volatility forecasts and fails to reflect the unique behaviour of digital assets. This study aims to identify the most effective GARCH-class models for forecasting volatility in Bitcoin, Ethereum, Binance Coin, and Ripple. We analyse daily returns from August 2017 to December 2024, applying eight advanced GARCH specifications: EGARCH, GJR-GARCH, FIGARCH, HYGARCH, MSGARCH, CS-GARCH, and Log-GARCH. Hyperparameter tuning is conducted via grid search across lag orders (p, q ∈ [1, 5]), mean equations, and error distributions. Model performance is evaluated using AIC, BIC, RMSE, and MAE. Results show that MSGARCH and EGARCH outperform symmetric and short-memory models, highlighting the importance of regime-switching and leverage effects. FIGARCH provides the best fit for Bitcoin and Ethereum, confirming long-memory persistence. Skewed Student’s t and GED distributions improve accuracy by capturing heavy tails and asymmetry. These findings demonstrate the limitations of standard GARCH models and underscore the value of advanced specifications in modelling cryptocurrency volatility. The study offers practical insights for traders and risk managers, contributing to more robust forecasting in non-stationary markets. Advanced GARCH models significantly enhance volatility prediction for digital assets. Future research could extend this framework to other speculative instruments or integrate machine learning techniques to further improve performance.
The pharmaceutical supply chain has a critical component, the Drug Traceability System, which tracks drugs from manufacturers for further processing and distribution. The integration of blockchain technology yields a secure solution for monitoring drugs throughout the supply chain management process. The paper proposes a novel Blockchain-enabled Secured Vertical Aggregation Algorithm (BSVA) by leveraging the Hyperledger model. The proposed model minimizes the requirement for a centralized authority to ensure privacy while also enhancing scalability to reduce response time in the process of managing transactions on the Blockchain. The Certificate Authority is used to maintain a secure data-sharing process. The robust aggregation is used for the local models to process the chain code, ensuring the successful execution of the secured transaction. The smart contract is deployed into a blockchain model as the block is stored and linked to the distributed Ledger. The decentralized framework is used by chain code, which guarantees that transactions are highly transparency. The performance parameters demonstrate the efficiency of the proposed model by enhancing the overall performance of the drug traceability system, as the proposed algorithm ensures the integrity of pharmaceutical products throughout the supply chain.
With the widespread adoption of Internet of Things (IoT) technologies in healthcare systems, security issues related to user privacy during data transmission and sharing have become increasingly prominent. To address these challenges, this paper proposes a medical privacy protection and secure sharing scheme based on Quantum Key Distribution (QKD). The scheme integrates multiple technologies, including blockchain, smart contracts, zero-knowledge proofs, and Chebyshev chaotic mapping, to ensure secure data sharing and access control among multiple communication entities. Compared with existing solutions, our approach enhances key management security through quantum keys and improves communication resilience against attacks by leveraging chaotic systems. User identity privacy is protected via zero-knowledge proofs. Under the random oracle model, the security of the proposed scheme is formally proven. Moreover, comparative experiments with existing protocols demonstrate the scheme's comprehensive advantages in terms of security and performance, evaluated across throughput, computational overhead, communication overhead, and storage overhead.
As Ethereum continues to gain traction as a leading blockchain platform, its open and decentralized nature has also made it an attractive target for fraudulent activities. This paper presents a machine learning-based approach to detect fraudulent Ethereum transactions by analyzing behavioral patterns within transaction data. Using a labeled dataset of Ethereum transactions, various classification algorithms such as Random Forest, XGBoost, and Support Vector Machines were trained and evaluated. The proposed system focuses on identifying anomalies and suspicious transaction behavior by extracting relevant features like gas usage, transaction value, and timing. Experimental results show that the model can achieve high accuracy and precision in distinguishing between legitimate and fraudulent transactions. This work contributes to the growing field of blockchain security by demonstrating the viability of intelligent fraud detection techniques and providing a framework that can be integrated into real-world applications.
Rozina Chohan, Gulshan Naheed, Dr Khakoo Mal, Muhammad Jalil Afridi · 6 authors
The growing popularity of the Internet of Things (IoT) networks has called out a growing need in sound and scalable security. The blockchain technology and its features of decentralization, immutability, and transparency can offer an effective solution to manage key security vulnerabilities, namely, authentication, data integrity, and privacy. This paper discusses the use of blockchain to IoT security, namely: decentralized authentication systems, data integrity management, and the challenge of scalability of large IoT networks. The findings indicate that despite the fact that blockchain can greatly improve security by guaranteeing data integrity and scheme-level device authentication, the performance of blockchain faced with output times of heightened issuance, slow transaction processing, and network constraints only emerge as network sizes rise. It has been proven that Proof of Stake (PoS) performs better than Proof of Work (PoW) regarding energy consumption, transaction latency, and time of recovery after failures due to attacks. Nonetheless, the paper underlines that streamlining blockchain protocols can help to mitigate performance stalls during massive IoT usage. The paper shows that, despite blockchain offering a secure basis to IoT, refinements in autonomous agreements, privacy safety nets, and scalability methods are required to make it an opportunity with broad IoT implementations.
Breno Jacinto Duarte da Costa, Márcio Ferro, Mohamed Yassine Zarouk, Alan Silva · 6 authors
Traditional learning management systems are cloud-based and teacher-centric, limiting accessibility, flexibility, and student-centered learning. We present the Active Learning EXperience (ALEX), a decentralized learning management system aligned with Education 4.0, supporting flipped and project-based learning. ALEX leverages Web3 technologies (blockchain, InterPlanetary File System, and offline-first mechanisms) to enhance security, privacy, and access in low-connectivity environments. This study evaluated, through the case study method, ALEX's usability, performance, and pedagogical impact with nine third-year information systems students and one facilitator in an authentic project-based course at a Brazilian university, with two non-governmental organizations taking part as clients. Instruments included a custom usability questionnaire, and a pre/post-test skill assessment. Results showed significant improvements in hard and moderate improvement in soft skills, suggesting further enhancements. Future work will focus on expanding ALEX's collaborative features and scalability testing to strengthen its impact on student engagement and learning outcomes.
Blockchain technology has rapidly evolved as a decentralized solution offering high security and transparency; however, several challenges still hinder the effective management of workflows within blockchain based environments. This study aims to develop an adaptive workflow management model that utilizes decentralized artificial intelligence (AI) and distributed ledger technology (DLT) to enhance the performance, security, and flexibility of processes in blockchainn networks. A mixed method approach combining simulation and experimentation on a dedicated blockchain platform was employed. The adaptive workflow model consists of a realtime process monitoring module, a decentralized AI module for adaptive decision making, and a DLT component that ensures data consistency and security. Statistical methods and system performance evaluations were used to analyze the experimental data. Results show that the proposed model can reduce workflow response times by up to 25% and increase the successful execution rate of smart contracts to 98%. Moreover, the integration of decentralized AI optimizes workload distribution across nodes, enabling network scalability improvements of up to 150% without significant performance degradation. The findings demonstrate that the adaptive workflow model combining AI and DLT enhances the flexibility and governance of blockchain networks through AI’s predictive capabilities and DLT’s security. Nevertheless, challenges such as high computational resource demands and technical complexities must be addressed. This research opens opportunities for further development to expand the scope of complex and dynamic blockchain applications and supports their integration with technologies like the Internet of Things (IoT).
Existing electronic voting systems suffer from security concerns, identity theft, electoral fraud, and insufficient transparency in digital voting systems, which pose significant challenges to electoral integrity. Blockchain-based electronic voting systems provide immutability and decentralization. However, they are inappropriate for large-scale elections because of their inadequate consensus mechanisms, scalability issues, and security weaknesses. To provide an equitable electoral process, an electronic voting system must be scalable, secure, and efficient. This method requires real-time vote verification, secure vote recording to avert fraud, and voter authentication. This study introduces a blockchain-based smart contract electronic voting system (BCVS) to improve the security and efficiency of electronic voting. The three algorithms are employed by the proposed BCVS to safeguard and improve the electronic voting process via the utilization of smart contracts. These algorithms ensure the precise tabulation of results and establish a robust foundation for electronic voting by resolving disputes. The proposed approach ensures transparency, immutability, and a minimal likelihood of manipulation through the utilization of the prioritized delegated proof-of-stake (PDPoS) consensus mechanism. The PDPoS functions on Tier 3 scalable networks and diligently documents transactions on the blockchain to resolve critical challenges associated with electronic voting. The scalability and integrity of the proposed e-voting system are ensured through the implementation of a practical Byzantine fault tolerance algorithm. Improved voter authentication is accomplished by multi-factor authentication and elliptic curve digital signatures, reducing the dangers of unwanted access. Additionally, Nightshade sharding from the NEAR algorithm enhances scalability by partitioning the blockchain network into numerous smaller shards, facilitating parallel transaction processing. Consequently, throughput is markedly enhanced, and latency is diminished. The testing results indicate that the proposed BCVS achieves 100% confirmed transactions, 98% compatibility, 95% accuracy, and 95% audited votes. The proposed BCVS outperforms existing state-of-the-art systems in multiple essential domains, such as the volume of votes cast within a specified timeframe, precision, interoperability with other systems, quantity of confirmed transactions, auditability, and duration of vote counting.
Large language models (LLMs) have changed the reality of how software is produced. Within the wider software engineering community, among many other purposes, they are explored for code generation use cases from different types of input. In this work, we present an exploratory study to investigate the use of LLMs for generating smart contract code from business process descriptions, an idea that has emerged in recent literature to overcome the limitations of traditional rule-based code generation approaches. However, current LLM-based work evaluates generated code on small samples, relying on manual inspection, or testing whether code compiles but ignoring correct execution. With this work, we introduce an automated evaluation framework and provide empirical data from larger data sets of process models. We test LLMs of different types and sizes in their capabilities of achieving important properties of process execution, including enforcing process flow, resource allocation, and data-based conditions. Our results show that LLM performance falls short of the perfect reliability required for smart contract development. We suggest future work to explore responsible LLM integrations in existing tools for code generation to ensure more reliable output. Our benchmarking framework can serve as a foundation for developing and evaluating such integrations.
Decentralized exchange platforms such as Uniswap and Balancer operate on several pools where each pool contains two or more cryptocurrencies and constitutes direct trading pairs. The drawbacks here are that liquidity providing requires contribution of tokens in a specific proportion, and trading may require hopping between pools, hence increasing transaction fee and gas fee. We propose an automated market maker (AMM) protocol where liquidity providers can deposit any amount of tokens into the pool. The protocol will preserve the proportion of tokens by total value at the time of deposit and can be seen as a personalized self-balancing portfolio manager. In addition, since the invariant function is dynamic, all exchange pairs are executed from a single composite pool. Nevertheless, the scheme is vulnerable to flash loan attacks and must be used in conjunction with preventive measures.
Blockchain technology is widely used in various fields due to its ability to provide decentralization and trustless security. This is a fundamental understanding held by many advocates, but it is misunderstood, leading participants to fail to recognize the limitations of the security that blockchain can provide. Among all current network attacks, Denial of Service (DoS) attacks pose significant threats due to their ease of execution and destructive potential. This paper, based on the blockchain architecture hierarchy, categorizes and organizes existing DoS attacks, with a focus on explaining the principles and methods of contract layer and consensus layer DoS attacks. Furthermore, this paper comprehensively analyzes and compares commonly used detection methods and defense technologies, which will contribute to strengthening the security and stability of blockchain systems and promoting further innovation and application of blockchain systems.
This paper investigates the dynamics of risk transmission in cryptocurrency markets and proposes a novel framework for volatility forecasting. The framework uncovers two key empirical facts: the asymmetric amplification of volatility spillovers in both tails, and a structural decoupling between market size and systemic importance. Building on these insights, we develop a state-adaptive volatility forecasting model by extracting time-varying quantile spillover features across different volatility components. These features are embedded into an extended Log-HAR structure, resulting in the SA-Log-HAR model. Empirical results demonstrate that the proposed model outperforms benchmark alternatives in both in-sample fitting and out-of-sample forecasting, particularly in capturing extreme volatility and tail risks with greater robustness and explanatory power.
With the increasing security issues in blockchain, smart contract vulnerability detection has become a research focus. Existing vulnerability detection methods have their limitations: 1) Static analysis methods struggle with complex scenarios. 2) Methods based on specialized pre-trained models perform well on specific datasets but have limited generalization capabilities. In contrast, general-purpose Large Language Models (LLMs) demonstrate impressive ability in adapting to new vulnerability patterns. However, they often underperform on specific vulnerability types compared to methods based on specialized pre-trained models. We also observe that explanations generated by generalpurpose LLMs can provide fine-grained code understanding information, contributing to improved detection performance. Inspired by these observations, we propose SAEL, a LLMbased framework for smart contract vulnerability detection. First, we design prompts targeting specific smart contract vulnerabilities to guide general-purpose LLMs in detecting vulnerabilities and providing explanations. The detection results generated by LLMs serve as prediction features. Then, we employ prompt-tuning on CodeT5 and T5 respectively to process contract code and explanations, enhancing model performance on specific tasks. To leverage the strengths of each component, we introduce Adaptive Mixture-of-Experts, a dynamic architecture for smart contract vulnerability detection. This mechanism dynamically adjusts feature weights through a Gating Network, which selects the most relevant features by applying TopK filtering and Softmax normalization, and a Multi-Head Self-Attention mechanism, which enhances cross-feature relationships by processing multiple attention heads in parallel. This design ensures that prediction results for LLMs, explanation features, and contract code features are effectively integrated through gradient optimization. The loss function focuses on the independent prediction performance of each feature and the overall performance of weighted predictions. Experimental results show that SAEL outperforms existing methods in detecting various vulnerabilities.
Ethan Buchman, Paolo Dini, Shoaib Ahmed, Andrew Miller · 5 authors
For centuries, financial institutions have responded to liquidity challenges by forming closed, centralized clearing clubs with strict rules and membership that allow them to collaborate on using the least money to discharge the most debt. As closed clubs, much of the general public has been excluded from participation. But the vast majority of private sector actors consists of micro or small firms that are vulnerable to late payments and generally ineligible for bank loans. This low liquidity environment often results in gridlock and leads to insolvency, and it disproportionately impacts small enterprises and communities. On the other hand, blockchain communities have developed open, decentralized settlement systems, along with a proliferation of store of value assets and new lending protocols, allowing anyone to permissionlessly transact and access credit. However, these protocols remain used primarily for speculative purposes, and so far have fallen short of the large-scale positive impact on the real economy prophesied by their promoters. We address these challenges by introducing Cycles, an open, decentralized clearing, settlement, and issuance protocol. Cycles is designed to enable firms to overcome payment inefficiencies, to reduce their working capital costs, and to leverage diverse assets and liquidity sources, including cryptocurrencies, stablecoins, and lending protocols, in service of clearing more debt with less money. Cycles solves real world liquidity challenges through a privacy-preserving multilateral settlement platform based on a graph optimization algorithm. The design is based on a core insight: liquidity resides within cycles in the payment network's structure and can be accessed via settlement flows optimized to reduce debt.
Farangis Azim, Qurratulain Razak, Surayya Jamal, Abdullah Shah · 5 authors
This study examines how Bitcoin has emerged in South Asia and how it can negatively disrupt the traditional banking system or become an enabler of financial innovation. Bitcoin, as a decentralized digital currency, has received a lot of attention in the region especially in states with high unbanked populations and remittance inflows like India, Pakistan, Bangladesh, and Sri Lanka. The analysis delves into how Bitcoin is used in remittance services, cross-border payments and financial inclusion, and how it can be used by financial institutions and regulatory structures. The research examines consumption rates, governmental reactions, and the changing fintech market to conclude that Bitcoin holds the dual value of disrupting traditional financial intermediaries simultaneously with enhancing fintech innovation via blockchain technology. Although regulatory uncertainty and Bitcoin volatility are currently thorny issues, the results indicate that Bitcoin is actively transforming the way people conduct financial activities in South Asia, particularly its ability to increase financial inclusions and transaction fees. The analysis determines that the future of Bitcoin in the region depends on establishment of moderate regulatory frameworks and integration into the financial infrastructure that exist.