In the modern financial landscape, cryptocurrency investments have gained substantial traction among both seasoned and novice investors. However, given the complexity, volatility, and risk associated with digital currencies, financial literacy plays a fundamental role in shaping an individual’s investment decisions. This study explores the intricate relationship between financial literacy and cryptocurrency investment behavior, analyzing how knowledge of financial principles influences an investor’s ability to assess risk, formulate strategies, and make informed decisions in the highly speculative crypto market. This research adopts a mixed-methods approach, combining both qualitative and quantitative data collection techniques. Surveys and structured interviews were conducted among cryptocurrency investors of various demographics, ranging from experienced market participants to first-time investors, to assess their understanding of financial concepts and their influence on investment strategies. Additionally, secondary data was sourced from financial reports, academic journals, and regulatory analyses to contextualize the findings within broader financial literacy frameworks. The results of the study indicate that individuals with a higher level of financial literacy are more likely to engage in thorough research before investing, effectively utilize risk management techniques, and demonstrate a more disciplined approach to cryptocurrency trading. Conversely, a subset of investors, despite having adequate financial knowledge, continues to engage in speculative trading driven by social trends, herd mentality, and market hype, often leading to irrational financial decisions. This suggests that while financial literacy is crucial, external factors such as psychological influences, peer recommendations, and media narratives can significantly impact investment behavior. The study further highlights the role of financial education in mitigating impulsive investment decisions. It emphasizes the need for targeted educational programs that equip investors with the analytical skills required to navigate the complexities of digital asset investments. By understanding key financial concepts such as market volatility, asset diversification, and risk assessment, investors can make more informed decisions and minimize exposure to financial losses. In conclusion, this study provides valuable insights into the role of financial literacy in shaping investment behaviors in the cryptocurrency space. The findings contribute to the ongoing discussion on financial education and its implications for emerging markets, digital assets, and investment decision-making processes. The study also serves as a foundation for further research on how investor psychology, regulatory frameworks, and technological advancements intersect with financial literacy in the evolving cryptocurrency ecosystem.
The migration of E-commerce applications to Blockchain offers a resilient solution to the vulnerabilities inherent in centralized servers. By dispersing data across multiple nodes, Blockchain ensures continuous service availability, even in the event of server failure or cyber attacks. Moreover, its inherent encryption and immutability features guarantee the security and integrity of customer and product data. Blockchain revolutionizes the E-commerce landscape by providing decentralized platforms that address critical challenges such as security, transparency, efficiency, and trust. This technology presents numerous opportunities for enhancing various aspects of E-commerce, including payment systems, supply chain management, and the implementation of smart contracts for automated workflows. With its robust capabilities, Blockchain emerges as a pivotal development poised to transform the E- commerce industry, paving the way for enhanced security, transparency, and efficiency in online transactions.
The rapid growth of cryptocurrency and blockchain technology has raised significant legal and ethical questions, particularly in Muslim-majority countries like Indonesia, where Islamic law (Shariah) plays a central role in financial regulation. This study examines the role of Islamic law in regulating cryptocurrency and blockchain technology, focusing on Indonesia’s regulatory framework. The research aims to assess the compatibility of these technologies with Shariah principles and identify gaps in the current regulatory approach. By doing so, it seeks to provide recommendations for developing a Shariah-compliant regulatory framework that balances innovation with ethical and legal considerations. Using a mixed-methods approach, this study combines legal analysis of Indonesia’s regulatory framework with qualitative interviews with Islamic scholars, regulators, and industry experts. Data were analyzed to evaluate the alignment of cryptocurrency and blockchain technology with Shariah principles, such as the prohibition of riba (interest) and gharar (uncertainty). The findings reveal that while blockchain technology has potential applications in Islamic finance, cryptocurrencies face significant challenges due to concerns over volatility, speculation, and lack of intrinsic value. The study concludes that Indonesia’s regulatory framework must be adapted to address the unique challenges posed by cryptocurrency and blockchain technology while ensuring compliance with Shariah principles.
AI-powered microloans are transforming financial inclusion by enabling microenterprises in financially excluded geographies to access critical capital through innovative technologies. This article examines how artificial intelligence addresses traditional microfinance challenges through alternative credit scoring systems that analyze diverse data sources beyond conventional credit histories. By leveraging mobile usage patterns, transaction histories, psychometric assessments, and other digital footprints, AI algorithms create comprehensive risk profiles that extend financial services to previously excluded entrepreneurs. The technology not only improves initial credit assessments but also enhances ongoing risk management through behavioral analytics that predict repayment issues before they materialize. Despite significant technical implementation challenges in connectivity-limited regions, the article explores promising solutions, including edge computing, explainable AI frameworks, adaptive learning systems, and federated learning approaches. Ethical considerations regarding data privacy, algorithmic bias, and interest rate transparency require careful attention to ensure these innovations promote genuine inclusion. The evolution of this field points toward embedded financial services, decentralized finance integration, and collaborative AI models that could further democratize access to capital for marginalized entrepreneurs worldwide.
Decentralized Finance (DeFi) represents a paradigm shift in the financial ecosystem, leveraging blockchain technology to offer innovative, transparent, and permissionless financial services. By eliminating intermediaries, DeFi applications enable direct peer-to-peer transactions and smart contract-driven solutions for lending, borrowing, trading, and asset management. This paper explores the architecture and functionalities of blockchain-based DeFi applications, highlighting their potential to enhance financial inclusivity, reduce transaction costs, and improve system efficiency. Key technical components such as decentralized exchanges (DEXs), liquidity pools, and yield farming are examined, along with the role of governance tokens in community-driven ecosystems. The paper also addresses critical challenges, including scalability, security vulnerabilities, regulatory compliance, and market volatility, which can impact DeFi's adoption and sustainability. Through case studies and performance analyses of leading DeFi platforms, this study provides insights into the transformative potential of blockchain-based DeFi applications in reshaping traditional financial paradigms.
Hong-Sheng Huang, Jason Y. Ho, Hao Chen, Hung–Min Sun
Poorly designed smart contracts are particularly vulnerable, as they may allow attackers to exploit weaknesses and steal the virtual currency they manage. In this study, we train a model using unsupervised learning to identify vulnerabilities in the Solidity source code of Ethereum smart contracts. To address the challenges associated with real-world smart contracts, our training data is derived from actual vulnerability samples obtained from datasets such as SmartBugs Curated and the SolidiFI Benchmark. These datasets enable us to develop a robust unsupervised static analysis method for detecting five specific vulnerabilities: Reentrancy, Access Control, Timestamp Dependency, tx.origin, and Unchecked Low-Level Calls. We employ clustering algorithms to identify outliers, which are subsequently classified as vulnerable smart contracts.
Blockchain and smart contracts have emerged as revolutionary technologies transforming distributed computing. While platform evolution and smart contracts' inherent immutability necessitate migrations both across and within chains, migrating the vast amounts of critical data in these contracts while maintaining data integrity and minimizing operational disruption presents a significant challenge. To address these challenges, we present SmartShift, a framework that enables secure and efficient smart contract migrations through intelligent state partitioning and progressive function activation, preserving operational continuity during transitions. Our comprehensive evaluation demonstrates that SmartShift significantly reduces migration downtime while ensuring robust security, establishing a foundation for efficient and secure smart contract migration systems.
The main purpose of this paper is to compare the forecasting results of time series machine learning models to predict the cryptocurrencies’ future prices for pre-COVID-19, during COVID-19 and post COVID 19 pandemics. Time series data collected from Yahoo Finance was used for the period between January 2017 till February 2024 as test data and the training data to predict 12 months from March 2024 till February 2025. The author undertook three machine learning models: SARIMA, LSTM and FbProphet for the forecasting analysis. LSTM model performs well in predicting the daily price forecasting as compared to SARIMA and Fb prophet models. Bitcoin is predicted to be in the range of $55000 to $65000 by February 2025. Results show a robust trend of volatility during COVID and post COVID periods and pre COVID period was not volatile resulting to no price movements. Based on the forecasting results post-COVID-19 pandemic, the LSTM model outperforms with better predictions than the other models. The findings also revealed that LSTM-RNN model can significantly increase the predictive power in the studies of deep learning models. This paper contributes to the literature on machine learning and forecasting models and the finding provides unique information while modeling the returns. It also insights on what machine learning model is the best to predict movements in the time series data. The results in this paper are expected to enhance our understanding on the role of machine learning models in forecasting future prices of investment instruments in the market, making it valuable for academics, investors, and policymakers alike.
The study examines research on Blockchain technology in the global finance using trends analysis, bibliometric analysis, and literature review using the Elsevier Scopus database from 2016-2023. The findings showed that (Blockchain in Financial Management) BCFin research has generated 185 publications comprising 53.5% articles, 45.4% conference proceedings and 1.1% reviews. The subject area analysis revealed three major publications, groupings spanning Computer Science, Engineering, and Decision Sciences. In contrast, the source titles revealed ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences as the top mediums of publications for BCF in researchers. Stakeholders analysis showed that the top researchers and affiliations for BCFin are Jon M. Truby and Qatar University (Qatar), with 4 and % publications, respectively. The most productive nation on the topic is China, which is largely ascribed to active funding agencies such as the National Natural Science Foundation (NNSF) of China, which has funded 18 publications. Keywords Co-occurrence Analysis revealed that the top three keywords on BCFin are Blockchain, Finance, and Supply Chain Finance. Cluster analysis revealed three (3) clusters comprising 4-8 keywords, 132 links, and a TLS of 866. Based on the keywords, these clusters could be broadly categorised as the following terms: Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is an active, multidisciplinary, and impactful research area. BCFin has transformed the global financial industry by overcoming trust issues, enhancing transaction security, and improving communication effectiveness, but it still faces challenges like interoperability, system integration, and sustainability.
Noor Ul Ain Afzal, Muhammad Kamran Abid, Muhammad Fuzail, Naeem Aslam · 5 authors
Ponzi schemes have surfaced on the Ethereum platform as blockchain technology continues to gain traction. Using smart contracts, these schemes, also referred to as smart Ponzi schemes, have caused significant financial losses and adverse effects. Byte code features, op code characteristics, account qualities, and smart contract transaction behavior are the main focus areas for current Ethereum smart Ponzi scheme detection techniques. However, these methods often do not record the behavioral features of the Ponzi scheme, resulting in high false alarm rates and poor identification accuracy. In this study, we provide the source P. Source P is a unique way of knowing intelligent Ponzi schemes on the Ethereum platform, passed by dataflow. Using the intelligent contract's source code as a function eliminates the difficulty of collecting data and extracting functions from available identification methods. In particular, we convert the code into statistical flow diagrams, apply educated models, and use code representations to create classification models for the detection of Ponzi schemes. Experimental results show that SourceP outperforms cutting-edge technology in terms of sustainability and effectiveness, achieving an F1 score of 92.4% and a recall of 90.1% in Ethereum's smart Ponzi schema detection. Ponzi, Blockchain, Source Code, Intelligent Contracts.
Decentralized finance (DeFi) lending platforms often require over-collateralization, excluding users without substantial crypto holdings. This paper introduces LFG, a novel DeFi protocol that leverages on-chain social profiles and tokenized reputation to assess creditworthiness. By integrating Ethereum smart contracts with Layer-2 solutions (Ethereum, Polygon), decentralized storage (IPFS) and zero-knowledge proofs, LFG enables undercollateralized loans while preserving privacy. We present a technical architecture, analyze security risks, and compare LFGs with traditional models using quantitative metrics. The results show a 40% reduction in collateral requirements for users with high reputation scores on the chain.
The emergence of the Metaverse as a decentralized digital ecosystem has transformed traditional contract enforcement by introducing smart contracts, self-executing agreements embedded in blockchain systems. This study conducts a comparative legal analysis of the regulatory frameworks governing smart contracts within Metaverse operations in Nigeria and Uganda. Employing a doctrinal legal method, the research critically examines primary legal sources such as statutory laws and case law, alongside scholarly literature, to assess legal recognition, enforceability, and institutional preparedness. The study reveals a significant regulatory gap in Nigeria, where the absence of a comprehensive legal framework creates uncertainty in the enforceability of smart contracts, despite growing blockchain policy initiatives. In contrast, Uganda has established more definitive legal provisions, particularly through its Electronic Transactions and Signature Acts, which explicitly validate digital contracts. The novelty of this study lies in its regional comparative focus on emerging economies and its analysis of how traditional contract principles interact with decentralized digital platforms. The urgency of this inquiry is underscored by the rapid digitalization of commerce, which necessitates timely legal adaptation to prevent regulatory obsolescence and safeguard stakeholders. This research contributes to the discourse on digital governance by proposing a legal reform agenda for Nigeria, advocating for the adoption of a smart contract-enabling framework modeled after Uganda’s approach. Ultimately, it calls for regional and international harmonization to ensure legal certainty, consumer protection, and dispute resolution within Metaverse-driven economies.
Distributed Ledger Technologies (DLTs) and smart contracts are revolutionizing industries by enabling transparent, decentralized, and automated transactions. However, the security of smart contracts remains a significant concern, as vulnerabilities can undermine the reliability of such systems and lead to substantial financial losses. Despite the critical importance of ensuring their integrity, there is a notable lack of automated frameworks to comprehensively assess smart contracts' security throughout their lifecycle, leaving them susceptible to various threats. This position paper proposes a framework to enhance smart contract security auditing, i.e., to efficiently and effectively support smart contract code analysis and testing and identify critical vulnerabilities. The framework encompasses several key components: identification of a target security profile, prioritization of potential vulnerabilities, systematic testing planning and execution, and a robust auditing and certification process. By establishing a structured approach to testing, we aim to enhance the security and reliability of smart contracts. In addition, we analyze the open challenges that must be addressed to build this framework effectively.
Anuj J. Ghom, Atharv N. Phuse, Harish S. Chopade, Mahesh A. Ghongade · 5 authors
Crowdfunding has emerged as a vital mechanism for raising funds, enabling startups, social causes, and creative projects to receive financial support from a broad audience.However, traditional crowdfunding platforms face challenges such as high transaction fees, lack of transparency, centralized control, and risks of fraud or fund mismanagement.To address these issues, we propose a Blockchain-Based Decentralized Crowdfunding Platform that leverages blockchain technology and smart contracts to enhance security, transparency, and trust in fundraising.By eliminating intermediaries, the system facilitates direct peer-to-peer transactions, ensuring immutability and automated fund distribution based on predefined conditions.This implementation utilizes the Ethereum blockchain to create an environment where fundraisers and backers can interact securely.The paper details the system architecture, smart contract design, security considerations, and a comparative analysis with traditional crowdfunding models.The results demonstrate improved transparency, reduced operational costs, and enhanced trust in the crowdfunding ecosystem.
Edgar Roberto Dulce Villarreal, Giovanni Hernández, Jesús Insuasti, Julio Ariel Hurtado Alegría · 5 authors
The exchange of medical information significantly benefits people's quality of life, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge. Blockchain technology is an alternative to find a balance in the healthcare ecosystem. Smart contracts (SC) are decentralized and self-executing programs that allow the automation of agreements without intermediaries to improve operational efficiency. However, the constant development of new Blockchain technologies and programming languages for smart contracts is a growing problem. This work presents the validation by expert judgment of the MUISCA (Mechanism for UnIversal SmartContrAct) tool, which uses Model Driven Engineering (MDE). MUISCA uses transformations of models and models to text to generate smart contracts in healthcare environments and specific to Blockchain platforms. The validation is conducted by smart contracts development experts, who show positivity in the perceived usefulness.
The rise of digital finance has led to a surge in fraudulent activities, particularly in credit card transactions and cryptocurrency ecosystems. With financial crimes becoming more sophisticated, traditional fraud detection methods often fail to identify complex fraudulent patterns. This research explores the application of machine learning (ML) and artificial intelligence (AI) techniques to enhance the security of digital finance by detecting fraudulent activities in credit card transactions and cryptocurrency wallets within the USA. The study utilizes large-scale transaction datasets containing key financial indicators such as transaction frequency, spending patterns, anomaly scores, and network behaviors. To develop an AI-driven fraud detection framework, we implement and compare six machine learning models: XGBoost, RLightGBM, Decision Trees, K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNNs), and Autoencoders. The models are trained on both structured financial data (e.g., credit card transaction logs) and unstructured blockchain transaction records (e.g., Bitcoin wallet addresses and transaction flows). To address data imbalance, the study applies the Synthetic Minority Over-sampling Technique (SMOTE), ensuring fair representation of fraudulent transactions. Model performance is evaluated using Precision, Recall, F1-score, and ROC-AUC metrics to determine the most effective fraud detection approach. Additionally, the research emphasizes data privacy and security, incorporating anonymization techniques and regulatory compliance measures to safeguard sensitive financial information. This study contributes to the ongoing fight against financial fraud by demonstrating how AI-based solutions can enhance the security and resilience of digital finance systems in the USA.
Sadaf Azimi, Ali Golzari, Naghmeh Ivaki, Nuno Laranjeiro
Abstract Smart contracts have accelerated the adoption of blockchain technology across various domains by enabling coded agreements between transaction participants. However, increased software defects and vulnerabilities in smart contracts, driven by developer inexperience with languages like Solidity and a lack of effective detection tools, pose significant risks. Given the high value of assets managed on blockchain (e.g., cryptocurrencies), these vulnerabilities can lead to severe consequences. Researchers and practitioners have proposed numerous smart contract design patterns to mitigate certain faults or vulnerabilities. Despite these efforts, it remains unclear which types of defects these patterns target and how effectively they address the wide range of existing smart contract security vulnerabilities. In this paper, we review the state of the art in smart contract design patterns, categorizing them and analyzing their effectiveness in mitigating known security vulnerabilities. Our findings reveal that only five patterns directly aim to prevent security vulnerabilities, collectively addressing just 6 out of 94 security issues identified by OpenSCV (a state-of-the-art vulnerability taxonomy), highlighting the need for further research on smart contract security design patterns.
This paper investigates the impact of cross-chain deployment on the market performance of decentralized applications (Dapps) within the evolving multichain Web3 ecosystem. While cross-chain Dapps benefit from broader user reach, improved scalability, and enhanced resilience, they also face significant challenges, including technical complexities, security risks, and fragmented liquidity. This paper analyses how Dapps' transaction distribution across multiple blockchains influences their market performance. Preliminary findings reveal that Dapps operating on multiple chains tend to underperform in terms of market capitalization, token price, and transaction volume compared to those concentrated on a single or few chains. These results highlight critical concerns about the effectiveness of cross-chain strategies.
José Juan de León, Cenchuan Zhang, Christos - Spyridon Koulouris, Francesca Medda · 5 authors
The growing interest in decentralized finance (DeFi), driven by advancements in blockchain technologies such as Ethereum, highlights the crucial role of smart contracts. However, the inherent openness of blockchains creates an extensive attack surface, exposing participants’ funds to undetected security flaws. In this work we investigated the use of deep reinforcement learning techniques, specifically Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), for detecting and classifying vulnerabilities in smart contracts. This approach utilizes control flow graphs (CFGs) generated through EtherSolve to capture the semantic features of contract bytecode, enabling the reinforcement learning models to recognize patterns and make more accurate predictions. Experimental results from extensive public datasets of smart contracts revealed that the PPO model performs better than DQN and demonstrates effectiveness in identifying unchecked-call vulnerability. The PPO model exhibits more stable and consistent learning patterns and achieves higher overall rewards. This research introduces a machine learning method for enhancing smart contract security, reducing financial risks for users, and contributing to future developments in reinforcement learning applications.
This research underscores the critical role of supply chain management in the globalized economy and the challenges posed by traditional financing models, such as information asymmetry and low capital turnover efficiency. The paper examines how blockchain's decentralized and immutable nature addresses these issues, enhancing transparency, security, and efficiency in supply chain data sharing. Blockchain has potential to reduce transaction costs, improve transparency, and secure supply chain financing. The research also employs case study analysis, scrutinizing the integration of blockchain in three distinct industries: Walmart's food safety initiative, Maersk and IBM's TradeLens platform, and Everledger's diamond supply chain tracking. These cases illustrate the practical application and benefits of blockchain in enhancing financing efficiency and traceability. While blockchain offers significant advantages, such as improved capital turnover and reduced transaction costs, it also faces limitations related to technological maturity, legal regulations, and market acceptance. It advocates for further research, policy support, and technological innovation to harness blockchain's full potential in supply chain financing.
Blockchain-driven financial innovation in Hong Kong from 2018 to 2024 has transformed cross-border payment systems through strategic regulatory frameworks and public-private collaboration. Key developments include the e-HKD Pilot Programme, integration with China's digital yuan, and pioneering CBDC initiatives like Project mBridge. This review analyzes technological implementations (Layer-2 solutions, zero-knowledge proofs), regulatory evolution across three distinct phases, and economic impacts including 38\% cost reduction in SME transactions. We examine Hong Kong's unique position bridging China's financial infrastructure with global markets while navigating geopolitical tensions and compliance challenges. The study provides quantitative metrics from 50+ corporate disclosures and regulatory documents, establishing a model for hybrid governance systems in financial technology adoption.
Introduction. The modern world is undergoing a transformation that encompasses all aspects of the economy, technology, and social life, and the financial sector is no exception. Financial technologies are becoming the driving force of this evolution, changing approaches to money management, investments, lending, and financial services in general. Thanks to the integration of artificial intelligence, blockchain, big data, and other innovations, financial services are becoming more accessible, personalized, and efficient, opening up new horizons for business and society. At the same time, this industry faces a number of challenges, such as the need to adapt to the regulatory environment, the growth of cyber threats, and ensuring financial inclusion for broad segments of the population. The development of financial technologies is taking on unique features in different regions of the world, from innovative platforms in the United States and Europe to revolutionary changes in financial services in Asia, Africa, and Ukraine. This multifaceted nature emphasizes the importance of global cooperation, technological progress, and a strategic approach to shaping the financial ecosystem of the future, which will be not only stable but also adapted to the needs of modern society. The purpose of the research is to deepen theoretical and methodological approaches to the management of financial services and innovative technologies aimed at optimizing, simplifying and reducing the cost of financial processes. Research methods. In the process of implementing the established goal of the scientific research, both general scientific and specific research methods were used, namely: generalization, induction and deduction, financial analysis and synthesis when establishing the influence of technological and innovative factors. The results. It was found that the future of financial technologies is promising. The main areas of development will be artificial intelligence, blockchain, open banking and decentralized finance (DeFi). It is expected that financial services will become even more personalized thanks to data analytics and customer behavior prediction. It was established that the development of supervisory (SupTech) and regulatory (RegTech) technologies will allow for more effective market monitoring, risk identification and transparency in the financial sector. Innovations in the field of cybersecurity will also become a priority, as users increasingly trust digital platforms with their financial data. The role of financial inclusion is identified, which will develop through the creation of accessible mobile platforms that provide services to people even in the most remote regions. Special emphasis will be placed on the development of financial literacy so that users can effectively use new tools. It is predicted that financial technologies will create new business models and stimulate their economic growth through innovation, which will have a significant impact not only in the financial sector, but also in people’s daily lives, changing the way they interact with their finances. The future of financial technologies is a digital transformation that will make financial services more accessible, efficient and secure for everyone. Prospects. Further research should be aimed at: creating and implementing a regulatory ‟sandbox” for rapid testing of innovations in the financial sector; increasing the level of financial literacy and involvement among the population and business; forming an educational base focused on implementing the concept of open banking; developing innovations in supervision and regulation technologies that ensure financial market stability, increase process efficiency, contribute to expanding the client base, as well as identifying and minimizing risks.
The form of money has been highly variable in history and continues to change drastically. New forms regularly appear, and their path to becoming money in a full sense is uncertain. Cryptocurrencies are a novel form of e-money based on distributed ledger technology and steadily growing in use. They are distinct from credit money but their own “moneyness” remains in doubt. This special issue of The Japanese Political Economy offers a political-economy-based analysis of their prospects. It considers deeper issues of validation, costs of use, and persistent speculation. It discusses functioning as unit of account, means of payment and reserve formation, and especially as world money. It also examines important differences between native coins with their own blockchains, Central Bank Digital Currencies, and stablecoins. Finally, the social character of cryptocurrencies is contrasted to community-based money. The jury is still out on whether cryptocurrencies could become money in a full sense, but it is shown that they have made great strides in that direction.
Privately created money based on Distributed Ledger Technology (DLT) emerged in the late 2000s at the same time as mobile money. The latter, exemplified by M-Pesa, has become a prevalent form of money in several countries, especially in Africa. DLT-based cryptocurrencies, in contrast, have achieved a rather limited monetary presence. This paper compares the creation and functioning of these two forms of digital money to establish reasons for the relatively weak social acceptability of cryptocurrencies. For the most prominent cryptocurrencies, such as Bitcoin and Ether, these reasons are shown to include deficiency as units of account, high costs of use, and fragmentation of blockchains. Ultimately, these are due to the decentralized and permissionless character of privately created DLT-based monies, which invites peculiar forms of capitalist profit making, including speculation. Despite its weaknesses, such money has the potential to become widely used, but that would require state intervention, which would alter its character.