The financial services industry has experienced a fundamental transformation through the strategic adoption of distributed systems architecture, fundamentally altering how institutions design, deploy, and scale their product offerings. Traditional banking infrastructure, characterized by monolithic architectures and centralized processing systems, increasingly struggles to meet contemporary demands for real-time processing, continuous availability, and seamless scalability. Distributed systems address these challenges through horizontal scaling capabilities, enabling institutions to accommodate exponential growth in transaction volumes without proportional infrastructure cost increases. The implementation of distributed computing has enabled comprehensive portfolios of digital-first financial products, including mobile banking platforms, real-time transaction processing systems, AI-driven financial advisory services, intelligent customer support solutions, and advanced fraud detection mechanisms. These systems demonstrate superior resilience through redundancy and fault isolation, achieving exceptional availability levels through multi-region deployment strategies. Future developments in distributed financial systems encompass blockchain integration, decentralized finance protocols, advanced artificial intelligence capabilities, and edge computing with IoT integration. However, implementation presents complex technical challenges, including data consistency maintenance, security considerations, regulatory compliance across multiple jurisdictions, operational complexity, and performance optimization requirements that institutions must carefully navigate to realize distributed computing benefits effectively.
In today's digital era, technological advances have brought major changes in various fields, such as the creative economy. The emergence of crowdfunding platforms and Non-Fungible Tokens (NFTs) as creativeoptions for creative funding is one of the latest developments. Artists, musicians, and other creators have seen how they advertise their work by using NFTs which are unique asset holdings on the blockchain. Incontrast, crowdfunding platforms like Patreon and Kickstarter allow creators to get funding directly from their fans without using conventional intermediaries. The purpose of this research is to find the problems and prospects faced by investors and creators when using NFTs and crowdfunding. Qualitative and quantitative methods were used, with case studies and secondary data analysis. The results show that the main challenges to be faced include legal and regulatory uncertainty, marketvolatility, copyright infringement, digital divide, high transaction costs, and environmental impact. Uncertainty regarding ownership rights and consumer protection is caused by regulatory uncertainty.Both creators and investors face significant risks due to the volatility of the NFT market. The case of plagiarism in NFTs shows that copyright must be strengthened. Some creators cannot use this technology due to the limitations of digital technology. A more environmentally friendly solution is also needed due to the high transaction fees and the impact of the Ethereum blockchain on the environment. In addition, many creators still have difficulty maintaining crowdfunding funding.
Central Bank Digital Currencies (CBDCs) are on the rise as major financial innovations, making use of technologies such as Distributed Ledger Technology (DLT) to transform monetary systems. India, under the Reserve Bank of India (RBI), is working on a number of CBDC projects to evaluate their feasibility, although a conclusive position on their adoption remains to be formulated. In this chapter, an organized summary of current literature covering CBDCs and their capacity to advance economic development has been presented. To address the prominence of individual pieces of research tending to end up with fragmentary insights, a bibliometric analysis was designed to present innovative findings regarding undeveloped realms.
Abstract: Intellect Chain: Decentralized IP Trading and Licensing via Blockchain and Tokenization. is a Web3 platform designed to revolutionize the intellectual property (IP) ecosystem. By utilizing blockchain technology, the platform enables the secure tokenization, licensing, and trading of IP assets such as books, films, music, and digital media. It creates an inclusive environment where creators can directly control, validate, and monetize their IP without relying on intermediaries, empowering them to retain ownership and earn royalties in a transparent and secure manner. Developed using the MERN stack (MongoDB, Express, React, Node.js), Genesis Protocol ensures high performance and scalability for seamless user interaction. Ethereum-based smart contracts manage the licensing process, ownership verification, and royalty distribution, while IPFS (InterPlanetary File System) is used for decentralized hosting, guaranteeing tamper-proof and secure access to digital content. NFTs play a critical role in validating and minting IP assets, offering both identity tokens and access rights, and enabling creators to directly interact with consumers and other stakeholders in the ecosystem. Intellect Chain: Decentralized IP Trading and Licensing via Blockchain and Tokenization aims to empower creators, reduce dependency on traditional intermediaries, and enable the transparent, secure, and efficient exchange of IP assets. By creating a decentralized marketplace for IP, the platform fosters innovation, ensures fair compensation for creators, and redefines the way intellectual property is managed, licensed, and traded globally. Keywords: Decentralized IP Trading, Web3, NFT Licensing, Creator Economy, Blockchain Tokenization, Smart Contracts, IPFS Hosting, MERN Stack, Intellectual Property.
The emergence of Non-Fungible Tokens (NFTs) – unique, blockchain-based tokens – has introduced a new dimension to the concept of property rights in the digital domain. Recent legal developments in the UK and the proposal of the Property (Digital Assets etc) Bill fuelled the discussion on how to legally conceptualise digital assets, including Non-Fungible Tokens (NFTs). This paper explores the evolving legal landscape surrounding property rights over NFTs, examining the challenges and ambiguities that arise from their intersection with existing property law frameworks. It analyses how property is defined and transferred in the context of NFTs, the implications for creators and acquires, and the best way to protect the latter. By critically assessing these issues, this paper aims to provide some insights regarding the legal principles that should guide the recognition and enforcement of property rights over NFTs, while suggesting new legal paths to accommodate this rapidly evolving technology.
Lending protocols have transformed the Decentralized Finance (DeFi) ecosystem, driving innovation while also introducing new risks. This study develops a machine learning framework to predict user behavior and assess factors influencing changes in health ratios within the Compound V2 protocol. By analyzing user historical data, position metrics, and market conditions, we propose machine learning-based models to predict whether users will adjust their positions or face liquidation. We find that Random Forest and XGBoost models excel in predicting these outcomes, with features like collateral values, historical risk exposure, and asset composition playing significant roles. Additionally, panel regression models reveal insights into health ratio dynamics over time and across asset types, as well as user sophistication. These findings offer a better understanding of user behavior, highlighting opportunities for improved risk modeling and adaptive strategies in DeFi lending.
Hardhik Mohanty, Giovanni Zaarour, Bhaskar Krishnamachari
Everlasting options, a relatively new class of perpetual financial derivatives, have emerged to tackle the challenges of rolling contracts and liquidity fragmentation in decentralized finance markets. This paper offers an in-depth analysis of markets for everlasting options, modeled using a dynamic proactive market maker. We examine the behavior of funding fees and transaction costs across varying liquidity conditions. Using simulations and modeling, we demonstrate that liquidity providers can aim to achieve a net positive PnL by employing effective hedging strategies, even in challenging environments characterized by low liquidity and high transaction costs. Additionally, we provide insights into the incentives that drive liquidity providers to support the growth of everlasting option markets and highlight the significant benefits these instruments offer to traders as a reliable and efficient financial tool.
Hong Qu, Krzysztof Gogol, Florian Grötschla, Claudio J. Tessone
Decentralized Finance (DeFi) lending enables permissionless borrowing via smart contracts. However, it faces challenges in optimizing interest rates, mitigating bad debt, and improving capital efficiency. Rule-based interest-rate models struggle to adapt to dynamic market conditions, leading to inefficiencies. This work applies Offline Reinforcement Learning (RL) to optimize interest rate adjustments in DeFi lending protocols. Using historical data from Aave protocol, we evaluate three RL approaches: Conservative Q-Learning (CQL), Behavior Cloning (BC), and TD3 with Behavior Cloning (TD3-BC). TD3-BC demonstrates superior performance in balancing utilization, capital stability, and risk, outperforming existing models. It adapts effectively to historical stress events like the May 2021 crash and the March 2023 USDC depeg, showcasing potential for automated, real-time governance.
This thesis examines how blockchain-based fundraising mechanisms like ICOs and IEOs reshape startup finance by offering decentralized access to capital. Analyzing 100 projects from 2019–2025, it identifies key success drivers using regression analysis. Findings show that strong community presence, top-tier investor backing, and compliance measures (e.g., KYC) significantly influence fundraising success. ICOs raise more than IEOs, despite looser oversight, highlighting a trade-off between decentralization and trust. Interaction effects reveal that credibility signals are especially effective in fragmented regions like APAC, and that compliance enhances ICO outcomes, while offering minimal added value in IEOs due to existing exchange-level due diligence.
This study analyzes the role of cryptocurrencies in portfolio diversification by comparing their risk-return profiles to traditional assets using correlation analysis, risk-adjusted metrics, and Monte Carlo simulations. Cryptocurrencies show the potential for higher returns but introduce substantial volatility and tail risk. Strategies such as VaR, CVaR, futures, and stablecoin allocations mitigate risks, with optimal exposure capped at 5-10% to balance returns and risk tolerance. Cryptocurrency markets remain sensitive to regulatory shifts, necessitating adaptive risk frameworks and continuous correlation monitoring of institutional investors. Policymakers are urged to clarify regulations to foster institutional adoption, and future research should explore DeFi tokens and CBDCs. These findings provide insights for managing crypto-inclusive portfolios in evolving digital asset markets. Future research directions include exploring decentralized finance (DeFi) tokens and central bank digital currencies (CBDCs) as emerging diversification tools. These insights equip investors with strategies for navigating crypto-inclusive portfolios in evolving digital asset landscapes.
Web3 grant programs are evolving mechanisms aimed at supporting innovation within the blockchain ecosystem, yet little is known on about their effectiveness. This paper proposes the concept of maturity to fill this gap and introduces the Grant Maturity Framework (GMF), a mixed-methods model for evaluating the maturity of Web3 grant programs. The GMF provides a systematic approach to assessing the structure, governance, and impact of Web3 grants, applied here to four prominent Ethereum layer-two (L2) grant programs: Arbitrum, Optimism, Mantle, and Taiko. By evaluating these programs using the GMF, the study categorizes them into four maturity stages, ranging from experimental to advanced. The findings reveal that Arbitrum's Long-Term Incentive Pilot Program (LTIPP) and Optimism's Mission Rounds show higher maturity, while Mantle and Taiko are still in their early stages. The research concludes by discussing the user-centric development of a Web3 grant management platform aimed at improving the maturity and effectiveness of Web3 grant management processes based on the findings from the GMF. This work contributes to both practical and theoretical knowledge on Web3 grant program evaluation and tooling, providing a valuable resource for Web3 grant operators and stakeholders.
Smart contracts are revolutionizing financial transactions by automating contractual agreements through blockchain technology, eliminating the need for intermediaries while enhancing security, efficiency, and accessibility across the financial sector. These self-executing protocols operate on predefined conditions, automatically verifying and executing terms without human intervention. Built on distributed ledger technology, smart contracts inherit key blockchain characteristics, including immutability, transparency, and cryptographic security, creating auditable transaction trails that significantly reduce fraud potential. While offering substantial benefits like reduced operational costs, accelerated settlement times, and enhanced financial inclusion, smart contracts face critical challenges, including security vulnerabilities, regulatory uncertainty across jurisdictions, and scalability limitations. Ongoing developments in security approaches like formal verification and specialized auditing firms are addressing vulnerability concerns, while progressive regulatory frameworks are emerging in forward-thinking jurisdictions. The future integration landscape is being shaped by advancements in cross-chain interoperability, Oracle integration for real-world data feeds, layer-2 scaling solutions, AI-enhanced optimization, and hybrid systems combining traditional legal contracts with automated execution. As blockchain technology matures, smart contracts are positioned to fundamentally transform financial infrastructure, contingent upon the continued evolution of security practices and regulatory frameworks.
This systematic review investigates the transformative impact of artificial intelligence (AI) and financial technology (FinTech) innovations on small and medium-sized enterprise (SME) financing, with a focus on enhancing transparency, efficiency, and financial inclusion. Despite the significant potential of AI and FinTech, substantial gaps remain in understanding their cross-regional and cross-industry effects, as well as in addressing persistent challenges such as AI adoption barriers, regulatory constraints, and decentralized data integration. The review synthesizes findings from peer-reviewed articles published from 2024 onward, sourced from Scopus and Web of Science databases, and examines the role of AI-driven solutions and digital financial platforms in SME financing. Results indicate that AI applications in risk assessment and credit scoring have reduced processing times by approximately 40% and improved loan approval rates by 25%. FinTech innovations have contributed to a 30% increase in financial inclusion, particularly among underserved SMEs in emerging economies. However, critical challenges, including data privacy concerns and limited technological infrastructure, continue to hinder broader adoption. This study contributes to the existing body of knowledge by systematically highlighting the role of AI and FinTech in enhancing SME financial performance and by providing actionable insights for policymakers, financial institutions, and entrepreneurs. The findings underscore the need for future research to address adoption barriers and to conduct cross-country comparative studies. Limitations include the exclusive focus on English-language, peer-reviewed sources, which may restrict the generalizability of the conclusions. Further investigations are recommended to explore the long-term impact of AI and FinTech innovations on SME sustainability and the evolution of regulatory frameworks supporting their implementation.
The proxy design pattern separates data and code in smart contracts into proxy and logic contracts. Data resides in proxy contracts, while code is sourced from logic contracts. This pattern allows for flexible smart contract development, enabling upgradeability, extensibility, and code reuse. Despite its popularity and importance, there is currently no systematic study to understand the prevalence, use scenarios, and development pitfalls of proxies. We present the first comprehensive study on Ethereum proxies. To gather a dataset of proxies, we introduce PROXYEX, the first framework to detect proxies from bytecode, achieving over 99% accuracy. Using PROXYEX, we collected a dataset of 2,031,422 Ethereum proxies and conducted the first large-scale empirical study. We analyzed proxy numbers and transaction traffic to understand their current status on Ethereum. We identified four proxy use patterns: upgradeability, extensibility, code-sharing, and code-hiding. We also pinpointed three common issues: proxy-logic storage collision, logic-logic storage collision, and uninitialized contracts, creating checkers for these by replaying historical transactions. Our study reveals that upgradeability isn't the sole reason for proxy adoption in DApps, and many proxies present issues like storage collisions and uninitialized contracts, which enhances the understanding of proxies and guide future smart contract research on the development, usage, quality assurance, and bug detection of proxies.
In the music industry, established players and new entrants are exploring blockchain blockchain for innovative intermediation solutions between artists and consumers. Blockchain, smart contract smart contract s, and non-fungible tokens (NFTs) are expected to reduce transaction costs and complexities arising from multiple rights and contracts, while enabling the emergence of a token economy. Relying on intermediation theories intermediation theory , this chapter aims to analyze blockchain’s impact on the music industry’s structure, organizations, and value distribution, highlighting the roles of strategies, technological capabilities, and governance frameworks. An extensive empirical study identified three scenarios: radical disintermediation, traditional intermediaries optimizing workflows with blockchain, and new entrants widely adopting blockchain.
Abstract This study investigates whether corporate social responsibility (CSR) serves as a financial tool to mask corporate financialization. Using data from publicly listed non‐financial firms from 2008 to 2020 in China, we analyze the effects and mechanisms of CSR on corporate financialization. The results show that CSR, particularly those targeting stakeholders such as investors, customers, and the community, is positively associated with corporate financialization, suggesting that CSR acts as a financial tool that supports, rather than curtails, financialization. Employing the B‐Z three‐step method, we find that financing constraints partially mediate the effect of CSR on financialization, implying that CSR activities can ease financing constraints, thus providing funds for financial investments. This supports the view that enterprises' allocation of financial assets may be driven more by profit‐seeking motives than precautionary liquidity management. Further analysis reveals that the financial tool hypothesis is primarily reflected in the stage where financing constraints impact financing and is particularly pronounced in non‐state‐owned enterprises, firms with decentralized ownership structures, and those led by management teams prioritizing short‐term returns. This research offers a reference for studying the dual‐edged implications of CSR in global corporate practices.
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.
Debugging and auditing zero-knowledge-compatible smart contracts remains a significant challenge due to the lack of source mapping in compilers such as zkSolc. In this work, we present a preliminary source mapping framework that establishes traceability between Solidity source code, LLVM IR, and zkEVM bytecode within the zkSolc compilation pipeline. Our approach addresses the traceability challenges introduced by non-linear transformations and proof-friendly optimizations in zero-knowledge compilation. To improve the reliability of mappings, we incorporate lightweight consistency checks based on static analysis and structural validation. We evaluate the framework on a dataset of 50 benchmark contracts and 500 real-world zkSync contracts, observing a mapping accuracy of approximately 97.2% for standard Solidity constructs. Expected limitations arise in complex scenarios such as inline assembly and deep inheritance hierarchies. The measured compilation overhead remains modest, at approximately 8.6%. Our initial results suggest that source mapping support in zero-knowledge compilation pipelines is feasible and can benefit debugging, auditing, and development workflows. We hope that this work serves as a foundation for further research and tool development aimed at improving developer experience in zk-Rollup environments.
Robo-advisors have emerged as a transformative force in wealth management, leveraging artificial intelligence (AI) and machine learning to provide automated financial advisory services. This study conducts a bibliometric analysis of research on robo-advisors using data exclusively from the Scopus database and analyzed through VOSviewer. The findings reveal that research in this field has evolved from foundational discussions on fintech and artificial intelligence to advanced themes such as machine learning, decentralized finance, and algorithmic transparency. The keyword analysis highlights "wealth management," "fintech," and "machine learning" as central themes, while the co-authorship network indicates strong interdisciplinary collaboration among researchers. Additionally, the study identifies key regulatory and ethical challenges, including data privacy, fiduciary responsibility, and algorithmic bias, which require further investigation. The discussion explores the technological advancements, investor behavior, and regulatory landscape shaping the future of robo-advisory services. This research contributes to the growing academic discourse by mapping the intellectual structure of robo-advisor studies and suggesting future research directions, particularly in the areas of explainable AI (XAI), blockchain integration, and personalized financial advisory models.
The article discusses the concept of new financial instruments, known as synthetic assets, which combine traditional finance with blockchain and decentralized finance (DeFi). These synthetic assets are digital tokens that are created artificially using derivatives. They aim to replicate the characteristics of realworld assets, such as stocks, commodities, and currencies, allowing investors to access these assets without owning them directly. These platforms are powered by smart contracts, which enable access to previously inaccessible markets. The author examines the various classifications, operational models, advantages, regulatory challenges, and potential for future growth and integration of synthetic assets into the global financial system. These synthetic assets are classified based on their underlying asset and liquidity/maturity, and their functionality is based on real-time price predictions transmitted through external tools to the blockchain. Key operational principles for synthetic assets include imitating the behavior of their underlying assets, decentralized operation through the use of smart contracts, the use of collateral, often in the form of cryptocurrencies, and mechanisms to increase liquidity. Various strategies, such as the use of derivatives and leverage, are employed in the trading of these assets. The differences between synthetic assets and other financial instruments are discussed. Synthetic assets have several advantages compared to traditional, tokenized, and derivative assets. These include accessibility, improved risk management, partial ownership, lower transaction costs, programmability, and potentially higher liquidity. However, there are also significant risks associated with synthetic assets, such as volatility due to underlying cryptocurrencies, regulatory uncertainty, and the dependence on price forecasts. The author also considers regulatory and law enforcement issues regarding the classification and decentralized nature of these assets.
Syeda Fizza Abbas, Sumiya Tahir, Sayyid Haider Mustafa Rizavi
This study examines the financial performance of diversified portfolios composed of various asset categories, including green cryptocurrencies, non-green cryptocurrencies, energy cryptocurrencies, stocks of leading companies, stocks of top energy companies, and stocks of prominent sustainable companies within the context of G7 nations. Additionally, it investigates the financial performance of green and non-green cryptocurrency portfolios across these regions. It aims to compare returns while examining the initiatives undertaken by these countries to foster sustainable financial systems. The research also explores how investors can leverage portfolio optimization to enhance returns in the rapidly evolving digital currency market. The study employs two machine learning techniques. First, six constraints, including maximum Sharpe ratio, minimum variance, maximum return, Sortino ratio, and Black-Litterman model, were applied to build portfolios for green and non-green cryptocurrencies. The model started with an 80%-20% train-test separation to find suitable allocations that it improved using full dataset retraining. The results explained that the highest Sharpe ratio portfolio generated the finest performance in the U.S. and Japan because of their strong financial market institutions and active participation from institutions. The investment cultures of Canada and Italy led to their selection of minimum variance portfolios. The Black-Litterman model worked well in the UK to produce equilibrium between market expectations and real risk-returns while German investors chose maximum return portfolios due to their risk tolerance. The French financial industry put risk-adjusted returns at the forefront thus the optimized Sortino ratio strategy proved most appropriate. A comparison between green and non-green portfolios shows that green portfolios regularly exhibited lower volatility together with superior risk-adjusted returns especially when sustainability policies were clearly defined in the nation. The higher returns from non-green portfolios came alongside higher speculative risk which made them susceptible to market volatility. This study demonstrated that selecting portfolios should be done based on specific market features that vary from country to country. Those who need stable long-term returns can achieve it through green investing while investors with high tolerance for risks can spend in non-green investments. Future studies should concentrate on developing dynamic rebalancing methods for portfolios while integrating decentralized finance (DeFi) technology to optimize portfolio management systems.
This study explores the transformative potential of blockchain technology in revolutionizing cross-border payment systems. Traditional methods are hindered by inefficiencies such as high transaction fees, prolonged processing times, and opaque operations, which impede seamless global financial interactions. Blockchain, with its decentralized and immutable ledger, offers a secure and transparent alternative that can significantly streamline payment processes. This paper examines how blockchain can facilitate real-time settlements, eliminate intermediaries, and enhance data integrity, thereby reducing costs and improving efficiency. Further, it addresses the practical applications and regulatory challenges associated with integrating blockchain into existing payment infrastructures. Ultimately, this research aims to provide actionable insights for developing a more efficient, transparent, and cost-effective cross-border payment ecosystem.
Natalia Zakharchenko, Natalia Dobrova, Eduard Karazhiya
This article examines the key sectors and emerging trends in venture capital investment focused on technology startups.It highlights industries such as fintech, biotechnology, artificial intelligence (AI), cybersecurity, clean technology, robotics, and the metaverse that are increasingly attracting the attention of venture capitalists.Purpose.The purpose of the study is to analyze the rise of venture capital investment in startups aligned with ESG (Environmental, Social, Governance) principles, focusing on how these companies contribute to sustainable business development and long-term value creation.The study also explores new opportunities for venture investment beyond traditional hubs such as Silicon Valley.Findings.The findings demonstrate the growing importance of ESG-compliant startups, which are attracting significant support due to their focus on environmental sustainability and social responsibility.The article also looks at the democratization of access to venture capital through crowdfunding and decentralized finance (DeFi), enabling a wider range of startups to raise funds.In addition, the article describes examples of 2023 startups, such as Trove, Charm Industrial, Ecovative Design, Ampersand, Verne Global, Living Carbon, Commonwealth Fusion Systems, and TerraPower, that have received significant venture backing for their innovations in renewable energy, carbon capture, clean transportation, and sustainable materials.Conclusions.Venture capital investment in technology startups is evolving rapidly, driven by the need for innovation in sustainable sectors and global economic shifts.The research shows that ESG-focused companies and new financial models have an important role to play in shaping the future of venture capital and driving long-term growth in key technology sectors.
César da Silva Robusti, Aline Bento Ambrósio Avelar, Milton Carlos Farina, Claudio Alexandre Gananca
Purpose This study conducts a systematic review to analyze the impact of blockchain technology and smart contracts on digital entrepreneurial finance and venture funding. It aims to investigate how these technologies can transform business operations, offering enhanced transparency, security and efficiency in various financial contexts. Design/methodology/approach A systematic literature review was conducted following PRISMA guidelines to assess the effectiveness of blockchain and smart contracts in generating business value. The study used the Scopus database to identify relevant articles published between 2015 and 2023. Quantitative and qualitative analyses were performed on the data using IRAMUTEQ software and Bibliometrix tool, both based in the “R” programming language. Findings The study reveals significant potential for blockchain and smart contracts to disrupt traditional venture funding models by automating contract execution and reducing administrative costs. Blockchain’s secure, decentralized ledger offers advantages in peer-to-peer transactions and risk mitigation, particularly in supply chain management and international trade. However, challenges related to scalability, energy consumption and regulatory compliance were also identified. Research limitations/implications Despite the contributions, systematic review of the literature has limitations. Only articles in English, published in journals, were considered for this research, omitting, for example, conference articles and research in other languages, which may also contain relevant information. Originality/value The study contributes to the existing literature by synthesizing recent advancements in blockchain and smart contract applications within entrepreneurial finance. It highlights the transformative potential of these technologies while addressing their limitations and offering insights into future research directions. The findings provide valuable implications for academics, practitioners and policymakers interested in leveraging blockchain for financial innovation.