Nourhaine Nefzi, A. Melki, Sahar Loukil, Ahmed Jeribi
Abstract This study investigates the dynamic connectedness within the cryptocurrency market by analyzing four distinct cryptomarket blocks: Bitcoin and Ethereum (conventional cryptocurrencies); PAXG, DGX, and GLC (gold-backed cryptocurrencies); LINK and MNK (decentralized finance); and THETA and MANA (nonfungible tokens). Using the time-varying parameter quantile vector autoregressive (TVP-Quantile VAR) model for the period 2019–2023, our analysis reveals significant insights into the risk transmission dynamics among cryptocurrencies. Both conventional cryptocurrencies exhibit a consistent net transmitter effect in extreme periods, whereas decentralized finance (DeFi) and nonfungible tokens (NFTs) shift between a net shock transmitter and a net shock receiver over time and quantiles. Moreover, our results shed light on the hedging and safe haven properties of these assets. By linking the dynamic connectedness findings with established literature on hedging and safe haven functions, we elucidate how these cryptocurrencies perform under varying market conditions. Specifically, we report that the role of LINK, MNK, THETA, and MANA as reliable safe-haven assets is contingent upon the observed period. We also observe the hedge and safe haven properties of selected gold-backed cryptocurrencies within the network. Overall, our findings suggest that, despite the dynamic connectedness of the cryptocurrency market, investors have the flexibility to diversify across these digital assets.
This article examines the revolutionary impact of Artificial Intelligence (AI) on transforming cryptocurrency trading, a sector characterised by extreme volatility, dynamism, and nonlinear data. Through a rigorous bibliometric analysis based on the Web of Science database, this study examines a sample of 555 scientific papers published between 2016 and 2025, utilising the PRISMA protocol for systematic selection, and tools such as VOSviewer and MS Excel. The analysis identifies five major thematic clusters: (1) blockchain infrastructure and AI integration in decentralised ecosystems, (2) data analysis and practical applicability in crypto markets, (3) financial and social data analysis—machine learning algorithms, (4) algorithmic trading and automation, and (5) prediction and modelling of crypto market developments. The originality of this study lies in providing an overview of the implementation stage of these technologies by integrating the results into a map of Technology Readiness Levels (TRLs). The findings highlight a clear transition from traditional statistical methods to autonomous decision-making systems capable of processing massive volumes of data for portfolio optimisation. This study’s limitation is that it may require periodic updates, as the AI and cryptocurrency landscape are constantly evolving.
Unlike Ethereum, which was conceived as a general-purpose smart-contract platform, Bitcoin was designed primarily as a transaction ledger for its native currency, which limits programmability for conditional applications. This constraint is particularly evident when considering oracles, mechanisms that enable Bitcoin contracts to depend on exogenous events. This paper investigates whether new oracle designs have emerged for Bitcoin Layer 1 since the 2015 transition to the Ethereum smart contracts era and whether subsequent Bitcoin improvement proposals have expanded oracles' implementability. Using Scopus and Web of Science searches, complemented by Google Scholar to capture protocol proposals, we observe that the indexed academic coverage remains limited, and many contributions circulate outside journal venues. Within the retrieved corpus, the main post-2015 shift is from multisig-style, which envisioned oracles as co-signers, toward attestation-based designs, mainly represented by Discreet Log Contracts (DLCs), which show stronger Bitcoin community compliance, tool support, and evidence of practical implementations in real-world scenarios such as betting and prediction-market mechanisms.
Huei-Wen Teng, Wolfgang Karl Härdle, Joerg Osterrieder, Daniel Traian Pele · 31 authors
Digital assets (DAs) such as cryptocurrencies, tokenized securities, stablecoins, non-fungible tokens (NFTs), and central bank digital currencies, are transforming financial markets with new business models, investment opportunities, and transaction efficiencies. Underpinned by blockchain, distributed ledger technology, and smart contracts, digital innovations are reshaping the financial ecosystem. However, their rapid growth introduces substantial risks, including fraud, market manipulation, cybersecurity threats, and regulatory uncertainty. This position paper offers an interdisciplinary and empirically grounded analysis of the DA landscape. We define and classify major asset types, trace their evolution from speculative instruments to functional tools, and assess current adoption trends. Additional technological developments (e.g., decentralized finance and NFT expansion) are examined for their role in accelerating this transformation. We also analyze the global regulatory landscape, highlighting jurisdictional differences, classification challenges, and emerging governance frameworks. To address key risks, we derive mitigation strategies via quantitative analysis and case-based evidence. The risks include balancing innovation with investor protection through adaptive regulatory design, promoting cross-border regulatory harmonization to prevent arbitrage and fragmentation, and supporting experimentation through regulatory sandboxes and innovation hubs. By adopting a forward-looking, evidence-based, and collaborative regulatory approaches, stakeholders can harness the benefits of DAs while managing systemic risks and maintaining market integrity.
This working paper introduces the Blockchain First-Principles Analysis (BFPA) framework, a novel methodology for evaluating distributed ledger systems by constructing explicit derivation chains from physical laws and cryptographic assumptions through a praxeological action axiom to concrete protocol design decisions. The framework features a four-level axiom hierarchy (physics, cryptography, praxeology, social consensus), a Nash equilibrium gate for social layer stability, a four-stage stability profile, a lock-in typology distinguishing design-emergent, ecosystem-emergent, corporate-imposed, and regulatory-granted lock-in, and a network effect genesis model identifying five necessary conditions for spontaneous adoption. Systematic application to eight major blockchain systems (Bitcoin, Ethereum, Solana, Monero, XRP, Polkadot, Tezos, BNB Chain) reveals that epistemic design quality correlates weakly with market outcomes, while lock-in type and network effect genesis conditions are substantially stronger predictors. The analysis provides principled explanations for the Tezos Paradox and the Monero Paradox. Comments welcome.
Research background and purpose Digital technologies offer tangible economic benefits but are also exposed to the risk of misuse. Crowdfunding is a special support form for business, cultural or social enterprises. Due to anonymity, fragmentation of capital and wide coverage, crowdfunding transactions are particularly vulnerable to the risk of criminal activities related to the concealment of the source of income or illegal changes of the financing objective. This article addresses the risks of money laundering and terrorism financing, particularly on the specifics of crowdfunding. Research has proposed a synthetic risk indicator for AML/CFT, which may measure the level of risk and vulnerability of crowdfunding to money laundering and terrorism financing. Design/methodology/approach The discussion in the article is presented against the background of a comprehensive and integrated review of literature, covering national and foreign sources. The theoretical part of the article utilizes: method of analysis and criticism of literature, analysis and synthesis, and method of analysis and logical construction. In the empirical part, to assess the level of risk and vulnerability of crowdfunding to AML/CFT risk compared to other areas, a research procedure based on the TOPSIS linear ordering method was used. The analysis covers the years 2019 and 2023. Findings The results of the studies show that crowdfunding is one of the most vulnerable areas at risk of money laundering and terrorism financing. The high position in the ranking in 2019 and 2023 resulted mainly from the dynamic development of the crowdfunding market in Poland, its increasing availability, a high degree of decentralization, the occurrence of cross-border transactions and the increasing diversity of platforms in their business model. Maintaining the benefits of crowdfunding requires the simultaneous implementation of effective remedies, increased campaign transparency and close cooperation with supervisory authorities and institutions combating financial crime. Value added and limitations The study makes an important contribution to the literature on the subject, providing information on the criminality of crowdfunding. The results of the study can be used by supervisory and regulatory authorities as a tool for shaping security in innovative segments of the financial system. The main limitation was the relatively small number of variables selected for the synthetic measure.
Amid the institutionalization wave of Decentralized Finance (DeFi), U.S. institutional Liquidity Providers (LPs) have emerged as the core incremental capital for leading Decentralized Exchanges (DEXs). However, the adaptation gap between Uniswap V4's concentrated liquidity mechanism and institutional risk preferences, as well as regulatory compliance requirements, has hindered their market entry. This study focuses on the integration of "technical characteristics - institutional constraints - precise pricing" and constructs a machine learning pricing model optimized across three dimensions: return, risk, and compliance. By integrating Uniswap V4 on-chain data, institutional risk preference data, and market data, a Stacking ensemble architecture combining LightGBM and CNN-LSTM is designed, incorporating 22 core features to achieve precise pricing. Empirical results show that the model's Mean Absolute Error (MAE) on the test set was reduced by 37% compared to the benchmark, and the Root Mean Square Error (RMSE) is reduced by 42%. The Sharpe ratio reaches 1.87 (an increase of 62% compared to the benchmark), with a volatility of 15.3% and a compliance adaptability score of 91. In the case study, a $150 million liquidity supply achieved a 19.7% annualized return and an 8.3% maximum drawdown, successfully passing SEC compliance review. This research fills the gap in institution-oriented pricing models for V4, improves the institutional extension of Automated Market Maker (AMM) pricing theory, and provides a risk-controllable and compliance-adaptable pricing tool for U.S. institutions participating in DeFi, promoting the transformation of the DeFi ecosystem towards standardization and institutionalization. By aligning the V4 Hook mechanism with U.S. regulatory frameworks, this research provides a scalable technical standard for institutional DeFi adoption, reinforcing the competitive advantage of the U.S. Web3 financial ecosystem.
this study rigorously scrutinizes the revolutionary impact of artificial intelligence (AI) on banking organizations within India. It definitively analyses the transformative effects of AI on the Indian financial industry by reviewing pertinent literature, compelling case studies, and empirical data. The paper first establishes the major ways. AI unequivocally alters the financial sector. It then details how Indian Banking institutions effectively deploy AI across critical areas such as customer service, algorithmic trading, risk management, fraud detection, credit scoring, and regulatory compliance. The integration of AI into India’s financial ecosystem is highlighted through examples from major banks, fintech companies, and regulatory agencies, showcasing the methods used and the outcomes achieved. Furthermore, this study explores the impacts and challenges associated with AI implementation in the Indian banking industry [14]. It delves into the cultural factors, current regulations, data availability, talent acquisition, and regulatory frameworks that shape the application of AI in Indian banks. The combination of Decentralized finance and AI offers a revolutionary partnership that might completely change the sector, increase its flexibility, and lay the foundation for long-term viability. In recent years, AI and Decentralized finance have become prominent advances in technology that have attracted a lot of interest and acceptance. In conclusion, this study comprehensively analyses AI's effects on India’s banking sector. This research paper is based on secondary data with the help of various journal and websites. Researcher paper benefits to many Policymakers, practitioners, and scholars will find invaluable insights contributing to the growing literature on technology-driven transformations. The recommendations provided will enable stakeholders to effectively harness AI’s capabilities while proactively addressing inherent risks and challenges, thereby enhancing the resilience, efficiency, and customer-centric focus of financial institutions in India and ensuring their competitiveness in an increasingly digital landscape. This research highlights the need to adopt a-worthy strategies for the prevention of active fraud, eventually contributes to the integrity of financial systems.
Open access
Innovations and Analysis in Business and Education
Aijie Shu, Wenbin Wu, Gbenga Ibikunle, Fengxiang He
Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thus, a shock to one token may result in significant and uncontrolled contagion effects. As the DeFi ecosystem becomes increasingly linked with traditional financial infrastructure through instruments, such as stablecoins, the risk posed by this dynamic demands more powerful quantification tools. We introduce DeXposure-FM, the first time-series, graph foundation model for measuring and forecasting inter-protocol credit exposure on DeFi networks, to the best of our knowledge. Employing a graph-tabular encoder, with pre-trained weight initialization, and multiple task-specific heads, DeXposure-FM is trained on the DeXposure dataset that has 43.7 million data entries, across 4,300+ protocols on 602 blockchains, covering 24,300+ unique tokens. The training is operationalized for credit-exposure forecasting, predicting the joint dynamics of (1) protocol-level flows, and (2) the topology and weights of credit-exposure links. The DeXposure-FM is empirically validated on two machine learning benchmarks; it consistently outperforms the state-of-the-art approaches, including a graph foundation model and temporal graph neural networks. DeXposure-FM further produces financial economics tools that support macroprudential monitoring and scenario-based DeFi stress testing, by enabling protocol-level systemic-importance scores, sector-level spillover and concentration measures via a forecast-then-measure pipeline. Empirical verification fully supports our financial economics tools. The model and code have been publicly available. Model: https://huggingface.co/EVIEHub/DeXposure-FM. Code: https://github.com/EVIEHub/DeXposure-FM.
Quang Nghĩa Nguyễn, Tuyen Vu, Minh Thông Phạm, Kien Nguyen · 5 authors
Existing smart contract vulnerability datasets exhibit over 34% train–test overlap due to repeated function-level code, causing models to favor structural memorization over semantic generalization. To mitigate this issue, we construct a benchmark dataset with zero function overlap between the training and test partitions. Furthermore, we introduce GraphFusionDetect (GFD), a novel approach that integrates fine-tuned CodeBERT embeddings with Graph Neural Networks (GNNs) to capture inter-function dependencies. GFD achieves F1-scores of 80% for detecting reentrancy vulnerabilities and 89% for timestamp dependency vulnerabilities, surpassing baseline methods and enabling more robust and generalizable vulnerability detection.
Ethereum-Smart Contracts verwalten häufig erhebliche finanzielle Werte. Da sie praktisch unveränderlich sind und häufig böswilligen Akteuren ausgesetzt sind, die durch finanziellen Gewinn motiviert sind, stellt die semantische Korrektheit eine zentrale Sicherheitsanforderung dar. Etablierte Testmethoden reichen oft nicht aus, um die Korrektheit über alle möglichen Ausführungspfade hinweg zu gewährleisten. Daher stellt die formale Verifikation ein wesentliches Mittel dar, um solche Sicherheitsgarantien zu stärken. Diese Arbeit untersucht die auf symbolischer Ausführung basierende Verifikation von Ethereum-Smart-Contracts unter Verwendung des KEVM-Frameworks sowie zweier darauf aufbauender Werkzeuge auf höherer Abstraktionsebene: ACT und Kontrol. Diese Arbeit behandelt Fragestellungen hinsichtlich der Ausdrucksstärke und Konstruktion von Beweisen sowie der Nutzbarkeit und Interpretierbarkeit sowohl von Beweisdefinitionen als auch von generierten Beweisartefakten. Es wird untersucht, ob und welche praktischen Herausforderungen bei der Verwendung von KEVM und zugehörigen Werkzeugen auftreten, einschließlich der Syntax, der verfügbaren Debugging-Werkzeuge sowie der Analyse von Beweisen und Gegenbeweisen. Anschließend erfolgt eine Evaluierung, wie semantische Eigenschaften über alle Werkzeuge hinweg spezifiziert werden können und wie präzise diese spezifiziert werden, wobei insbesondere die Zielkonflikte zwischen unterschiedlichen Abstraktionsebenen hervorgehoben werden. Darüber hinaus verifizieren wir semantische Eigenschaften von ERC20-Token-Smart-Contracts mit besonderem Fokus darauf, ob bestimmte Einträge in der Common Vulnerabilities and Exposures (CVE)-Datenbank tatsächlich korrekt sind oder mithilfe von KEVM widerlegt werden können. Zu diesem Zweck analysieren wir die gemeldete Schwachstelle, formulieren ein formales Argument gegen die behauptete Verletzung und konstruieren darauf aufbauend einen Beweis unter Verwendung von Kontrol. Dabei zeigen wir, wie semantische Eigenschaften innerhalb des Frameworks formuliert und verifiziert werden können. Abschließend untersuchen wir die Community-Aktivität rund um KEVM und dessen Ökosystem. Dazu werden GitHub-Repository-Metriken sowie Kommunikationsdaten aus Discord ausgewertet, um Entwicklungsaktivität, Dynamiken der Beitragenden sowie Muster im Nutzer-Support zu analysieren. Diese kombinierte Perspektive aus technischer und empirischer Sicht liefert eine ganzheitliche Betrachtung von KEVM sowohl als formales Verifikationsframework als auch als Entwickler-Ökosystem.
The rapid advancement of blockchain network protocols has positioned decentralized finance (DeFi) as a key distributed application ecosystem in modern digital infrastructure. These distributed network systems are reshaping traditional financial paradigms by leveraging peer-to-peer protocols for accessible, transparent, and efficient services. However, the underlying network infrastructure faces significant security challenges, particularly concerning transaction manipulation within the framework of Maximal Extractable Value (MEV). MEV has emerged as a critical network security vulnerability due to its exploitation of transaction-ordering mechanisms in blockchain consensus protocols. Despite extensive research on MEV, critical gaps remain in understanding and securing distributed ledger networks against these vulnerabilities across various blockchain platforms. In this paper, we present a comprehensive survey of MEV within DeFi ecosystems through a multi-faceted approach. We provide a detailed taxonomy of MEV attack strategies targeting network protocol vulnerabilities. Furthermore, we offer a categorization of security countermeasures spanning consensus protocols, base-layer network design, and application-level defenses. Additionally, we present an empirical analysis of MEV dynamics across different blockchain networks, quantifying their impact on network performance, security, and fairness. This study contributes to enhancing the security of distributed network applications and advancing more robust and equitable network protocols for decentralized systems.
Muhammad Usman Malik, Muhammad Junaid, Muhammad Aqeel, Muhammad Wasim · 5 authors
This study investigates how blockchain technology can fundamentally transform public value accounting by addressing persistent challenges of transparency, accountability, and stakeholder engagement inherent in traditional systems. Conventional public accounting frameworks are frequently characterized by structural inefficiencies, deliberate opacity, and perverse incentive structures that collectively undermine public trust and hinder effective governance. The research seeks to establish whether blockchain's distinctive technological attributes can provide viable solutions to these systemic problems while creating new paradigms for public value measurement and distribution. The research employs a rigorous mixed-methods methodology that combines computational modeling of blockchain architectures with empirical stakeholder analysis. Quantitative methods include simulation of tokenized governance models and network analysis of transaction transparency in test environments. Qualitative components incorporate in-depth interviews with public sector stakeholders, focus group discussions with citizens, and case study analysis of early blockchain implementations in municipal accounting. The study specifically evaluates three key blockchain features - immutable distributed ledgers, self-executing smart contracts, and programmable tokenization - as foundational elements for next-generation accountability frameworks. The comprehensive analysis yields several significant findings. First, blockchain implementation demonstrates measurable improvements in financial transparency, reducing audit times by an average of 40% in pilot programs. Second, smart contract automation eliminates discretionary interpretation of public spending rules, decreasing compliance violations by 62%. Third, tokenized participation mechanisms correlate with a 35% increase in stakeholder engagement metrics. Most importantly, the research identifies specific design principles for blockchain systems that successfully mitigate value distortion in public accounting while creating alignment between institutional actions and community expectations. This research makes multiple novel contributions to both academic literature and practical governance reform. The study develops the first comprehensive framework for applying blockchain's decentralized architecture to public value accounting, complete with empirically validated design specifications. It introduces the innovative concept of "dynamic tokenization" for real-time value tracking in public goods provision. The work also bridges important theoretical gaps between distributed ledger technologies and public administration theory, offering concrete pathways for implementing more equitable, transparent, and participatory governance models. These findings have significant implications for governments seeking technological solutions to persistent accountability challenges in an increasingly digital public sphere.
Financial technology (FinTech) has emerged as a transformative force in the global financial landscape, integrating advanced digital technologies like Artificial Intelligence and distributed ledger systems into traditional services. Since the early 21st century, it has fundamentally reshaped how payments, credit, investments, and risk management are handled. At the vanguard of this revolution are blockchain and cryptocurrencies, which provide decentralized and borderless alternatives to conventional banking. This research explores the evolution of these technologies, examining how smart contracts and automated systems drive efficiency and foster global financial inclusion. However, alongside these advancements, the study highlights the emergence of significant risks, particularly in the realms of cybersecurity, consumer protection, and the complex challenges of cross-border regulatory compliance. The paper further analyzes the strategic responses of traditional financial institutions and central banks, specifically focusing on the rise of Central Bank Digital Currencies (CBDCs) as a stable counter-narrative to private digital assets. Through various global case studies, the research illustrates the diverse regional adoption patterns influenced by local economic and cultural factors. Looking toward the future, the study predicts a trend of increased interoperability, where decentralized finance (DeFi) and programmable money integrate into mainstream economic structures. Ultimately, the paper argues that while the digital transformation of money offers immense potential for efficiency, its long-term success is contingent upon robust international governance frameworks and collaborative regulatory efforts to ensure trust and stability in the evolving global market.
Blockchain technology has emerged as a pivotal and transformative force, establishing transparent, secure, and decentralized frameworks for transaction management. Its core strengths include immutability, data decentralization, and consensus validation, alongside the automation provided by self-executing smart contracts. This review examines its foundational technologies, diverse applications, and associated challenges. Blockchain demonstrates profound potential across sectors like finance (e.g., Anti-Money Laundering and fraud reduction), education (credential verification), healthcare (secure record management), and the Metaverse (verifiable digital asset ownership via non-fungible tokens). However, adoption is significantly hindered by critical issues, including scalability bottlenecks, the energy inefficiency of protocols like Proof of Work, and security risks stemming from smart contract flaws, with case-based testing revealing up to 40% of public contracts have exploitable vulnerabilities. Recent advancements in high-throughput rollups and formal verification mitigate these risks. This coincides with a 2025 shift toward structured legal mandates, such as the EU’s MiCA, India’s VDA policy, and the U.S. GENIUS and CLARITY Acts. Therefore, future research must prioritize enhancing smart contract verification, developing energy-efficient consensus mechanisms, cross-chain interoperability, and fostering the continued alignment of supportive legal and regulatory frameworks.
Purpose This study examines how entrepreneurial experience shapes perceptions of the ideal investor in the technology-based sector. While previous research has primarily focused on how investors evaluate entrepreneurs, this study shifts the lens to explore how entrepreneurs assess investor attributes. It investigates how experience in securing funding and building ventures influences expectations around value-added contributions beyond financial investment. Specifically, the study explores whether experience leads entrepreneurs to adopt a more strategic and values-driven approach, placing greater emphasis on ethical alignment, expertise, and relational quality, while placing less importance on operational involvement and financial oversight. Design/methodology/approach This study adopts a quantitative research design using survey data from 195 entrepreneurs in the technology-based sector. Participants were recruited through entrepreneurial and investor networks across multiple countries. The survey captured key aspects of entrepreneurial experience, including fundraising and venture development, alongside expectations of investor roles and attributes. Factor analysis identified dimensions of value-added investor support, and k-means clustering was used to group entrepreneurs based on preference profiles. Multinomial logistic regression and OLS regression analyses were conducted to examine how different types of experience influence entrepreneurs' preferences for specific investor attributes and types of support. Findings The results show that entrepreneurial experience plays a significant role in shaping expectations of investor involvement. Entrepreneurs with more experience in fundraising and venture development tend to prioritize ethical conduct, strategic input, and relational alignment over traditional factors like financial returns or past performance. They value investor support focused on strategy, networks, and governance, while placing less importance on operational or financial oversight. Cross-sector experience further reinforces a preference for strategic-driven supports. Overall, the findings suggest that experience increases entrepreneurs' confidence and selectivity, encouraging a more strategic approach to building investor relationships. Research limitations/implications This study has several limitations. First, the data were collected primarily from entrepreneurs in developed countries with well-established venture capital markets, which may limit the generalization of the findings to emerging or less mature ecosystems. Second, the target population is difficult to define precisely, given the informal and decentralized nature of entrepreneurial networks. Third, the reliance on self-reported survey data introduces the possibility of response bias. Additionally, the cross-sectional design limits the ability to draw causal inferences. Future research could benefit from longitudinal data and broader geographic representation to better capture variation across different entrepreneurial contexts. Practical implications The findings provide actionable insights for both entrepreneurs and investors. As entrepreneurs gain experience, they become more selective, favouring investors who offer strategic guidance, ethical alignment, and relational support over purely financial backing. For investors, this highlights the importance of articulating non-financial value, such as expertise, governance input, and network access, to appeal to more experienced founders. Investors who position themselves as collaborative partners rather than controllers may build stronger, longer-lasting relationships. Entrepreneurial support programs, including accelerators and incubators, can also use these insights to prepare founders to identify and engage with strategically aligned investors. Social implications This study highlights the growing importance of trust, ethical conduct, and shared values in shaping effective entrepreneurial ecosystems. As entrepreneurs gain experience, they increasingly prioritize relational quality and strategic alignment in their investor relationships. This signals a broader shift toward more collaborative, purpose-driven engagement between founders and investors. Such a shift has the potential to foster healthier power dynamics, reduce misalignment and conflict, and support the formation of long-term partnerships grounded in mutual respect and shared vision. These findings contribute to ongoing discussions around responsible entrepreneurship and the sustainability of venture growth. Originality/value This study offers a novel contribution by shifting the focus from how investors assess entrepreneurs to how entrepreneurs evaluate potential investors. It addresses an under explored area in entrepreneurial finance, particularly highlighting the role of ethical behaviour and strategic alignment in investor selection. By examining how experience shapes these expectations, the study adds to the limited literature comparing novice and experienced entrepreneurs in their interactions with external stakeholders. It advances understanding of founder–investor dynamics and offers fresh insights into how entrepreneurial learning influences decision-making in the context of venture growth and funding relationships.
The financial sector is experiencing rapid transformation due to emerging technologies. Blockchain offers a decentralized, transparent, and immutable framework for secure transactions, while Artificial Intelligence (AI) enables advanced data analytics, predictive modeling, and intelligent automation. When combined, these technologies create a powerful synergy that is reshaping finance by enhancing fraud detection, improving credit evaluation, optimizing decentralized finance (DeFi) platforms, and automating compliance processes. This paper explores the combined benefits of blockchain and AI, highlighting practical applications such as AI-enabled fraud detection within blockchain networks, adaptive smart contracts, and blockchain-secured digital identity verification. It also addresses challenges in merging these technologies, including scalability limitations, regulatory ambiguity, interoperability concerns, and ethical considerations. The study underscores the potential future of autonomous financial systems, decentralized autonomous organizations (DAOs), and AI-driven sustainable finance solutions. Ultimately, the integration of blockchain and AI is seen as a transformative force capable of significantly improving transparency, efficiency, and inclusiveness in global financial systems.
The rise of blockchain and the metaverse has promoted the arrival of Web 3.0, a new era in which users can generate and trade valuable digital content like artworks and game items on decentralized platforms in the form of Non-fungible tokens (NFTs), and how to trade NFTs across different metaverses is receiving more and more attention. Existing third-party solutions compromise decentralization and anonymity, contradicting the core principles of Web 3.0. To solve this challenge, we propose the Universal Metaverse Trading Platform (UMTP), a cross-metaverse virtual asset trading platform designed around Self-Sovereign Identity (SSI). Unlike traditional notary schemes that rely on centralized identity management, UMTP pioneers integrating SSI into notarization protocols to enable SSI-based anonymous credential–protected election, enabling committee members to operate using DIDs while maintaining accountability. In simulations, UMTP’s final cleanup rate is 13 percentage points higher than PageRank’s. Against the Long-History Prediction Attack and the Recent-Driven Prediction Attack, UMTP improved security by \(63.7\%\) and \(64.8\%\) . Second, some user-oriented secure trading functions are introduced to better meet the diversified needs of users; finally, the Identity Restoration System provides additional insurance for users’ virtual assets. We demonstrate the effectiveness and scalability of our proposed scheme through simulations.
This study examines the joint influence of environmental factors and U.S. financial markets on the returns of Bitcoin (BTC) and Ethereum (ETH), shedding light on sustainability-driven crypto valuation. The analysis integrates CO₂ emissions, green innovations, ESG scores and financial indicators, including the S&P 500, NASDAQ, Dow Jones, gold and oil prices, using monthly data from January 2019 to February 2025. A robust econometric framework is employed to assess both the long-term cointegration and the short-term sensitivities of BTC and ETH returns. The findings suggest that BTC exhibits a strong positive correlation with environmental innovations and ESG scores, indicating an alignment with investors focused on sustainability. In contrast, ETH exhibits weaker sensitivity to environmental factors despite its adoption of a more energy-efficient Proof-of-Stake mechanism. Both cryptocurrencies respond positively to gold and oil prices, reinforcing their potential as alternative hedging assets. By jointly evaluating environmental and financial drivers, this study contributes to the fields of sustainable finance and digital asset research, bridging the gap between ESG studies and cryptocurrency market analysis.
This study explores the impact of Bitcoin on the Indonesian banking sector, emphasizing both the innovative opportunities and the challenges it presents. The research highlights Bitcoin's potential to enhance financial inclusion and drive technological growth while also identifying significant hurdles such as regulatory issues, security risks, and market volatility. Utilizing a mixed-methods approach, the study provides a nuanced analysis of Bitcoin's dual role as both a beneficial and threatening force within the financial landscape. It categorizes research variables into dependent, independent, and control groups to better understand their interactions and influence on traditional banking systems. The paper identifies a critical gap in existing literature regarding Bitcoin's specific effects on Indonesian banking operations, offering an empirical foundation for future research. The findings underscore the evolving regulatory frameworks and Bitcoin's complex role in the banking sector, highlighting the need for strategic management and careful regulation to harness its potential benefits while mitigating associated risks.
This study has been undertaken in the burgeoning intersection of financial technology (Fintech) and Environmental, Social, and Governance (ESG) paradigms, a domain that serves the purpose of redefining capital allocation in the 21st century. The research investigates the "Digital-Sustainability Convergence" theory, which posits that digital innovations serve the purpose of democratizing green finance and enhancing transparency. However, a critical review of the literature reveals a phenomenon termed the "Green Mirage," where the digital representation of sustainability obscures a lack of tangible ecological impact. Utilizing a bibliometric analysis based on VOS viewer logic, this paper examines a dataset of academic literature from 2015 to 2025. The findings indicate that while publication volume is on a rise, particularly in China and the United Kingdom, the intellectual structure is fragmented. The analysis identifies a significant gap between technological implementation—such as blockchain and artificial intelligence (AI)—and genuine sustainability outcomes. It is important to note that concepts like "token washing" and "digital greenwashing" have emerged as pivotal retention factors for critical scholarship, suggesting that the sector faces an important challenge in aligning "proof of stake" with "proof of impact." The study concludes that while Fintech serves the purpose of mobilizing retail capital, with 81.5% of investors considering ESG factors, the prevalence of managerial myopia and data asymmetry poses a challenge for the integrity of the ecosystem. Thus, it is important that regulators and practitioners move beyond symbolic compliance to address the structural disconnects identified.
Devika T D, Sangeeth Karunakaran, Basudev Balachandran, S Shinas · 5 authors
Managing crypto investments for retail investors is often hindered by high volatility, poor timing (buying at peaks and selling at lows), and the inherent risks of centralized platforms. This project introduces a decentralized, automated SIP model for crypto investments, offering a non-custodial and multi-asset investment protocol to limit these challenges. The system automates crypto investing like a Systematic Investment Plan (SIP). All SIP rules (amount, frequency, maturity) are enforced automatically by smart contracts, ensuring trustless and transparent execution. Users maintain full custody of their funds in non-custodial wallets like MetaMask, and investments are made directly using stablecoins (USDT/USDC) into crypto pools (BTC, ETH, SOL, BNB). The purchased assets are stored in a smart contract vault until maturity, promoting structured long-term investing and verifiable on-chain transparency. By leveraging smart contracts and dynamic frequency validation, the system provides a consistent, reliable, and non-custodial solution for long-term wealth building in the decentralized Web3 space.
ABSTRACT Block chain technology has rapidly evolved from a crypto currency backbone to a transformative infrastructure for financial services. Coupled with Artificial Intelligence (AI), it promises to revolutionize how financial institutions operate—enhancing transparency, security, efficiency, and compliance. We employ a mixed-method approach using qualitative interviews, quantitative performance analysis, and case studies to explore the scope of this technological convergence. Our results highlight significant operational gains and outline challenges that must be navigated for successful adoption. KEYWORDS Blockchain Technology, Artificial Intelligence,Fraud Detection,Decentralized Finance, Smart Contracts
Abstract This study empirically assesses the viability of Bitcoin as an alternative investment asset within the Egyptian context from 2011 to 2023. We conduct a comparative analysis of Bitcoin’s risk-return characteristics against traditional Egyptian investment vehicles: the EGX30 stock index, physical Gold, and the USD/EGP exchange rate. Utilizing historical daily data sourced from Coinbase, Bloomberg, Yahoo Finance, and the Central Bank of Egypt, we employ standard financial metrics including annualized returns, volatility (standard deviation), and Sharpe ratios. Correlation analysis is performed to evaluate Bitcoin’s diversification potential. Furthermore, we examine asset performance during significant periods of socio-economic stress: the 2011 Egyptian Revolution, the COVID-19 pandemic (2019-2020), and the EGP devaluation period (2022-2023). Our findings reveal Bitcoin’s exceptionally high volatility ( $$\sigma \approx 3.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>σ</mml:mi> <mml:mo>≈</mml:mo> <mml:mn>3.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> daily) and potential for substantial returns, yet yielding a surprisingly negative cumulative return over the entire sample period. Gold demonstrated characteristic stability ( $$\sigma \approx 1.0\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>σ</mml:mi> <mml:mo>≈</mml:mo> <mml:mn>1.0</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> daily), while the EGX30 offered moderate growth amidst volatility ( $$\sigma \approx 1.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>σ</mml:mi> <mml:mo>≈</mml:mo> <mml:mn>1.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> daily). Correlation analysis suggests limited diversification benefits between Bitcoin and traditional assets during certain periods. Event analysis highlights varying asset reactions, with Gold often acting as a safe haven, while Bitcoin exhibited mixed behavior. While Bitcoin presents diversification potential, its extreme volatility, negative long-term cumulative return within this sample period, and the prevailing regulatory uncertainty in Egypt necessitate careful consideration for investors seeking alternative assets in a challenging macroeconomic environment characterized by inflation and currency depreciation.