Abstract Purpose â The study evaluates the connectedness among the less riskier Digital Assets by investigating the functions of gold backed cryptocurrency alongwith Fan Tokens, Non-fungible Tokens and Real estate tokens, as a new alternative asset class that can be utilized by portfolio managers and investors alongwith policy makers. Design/methodology/approach â This study uses Quantile Vector Auto Regression analysis to measure the quantile cohesiveness among Islamic Cryptocurrencies, Non-Fungible Tokens, Fan Tokens and Real Estate Tokens, recommended by Ando et al., (2022) given extreme quantiles, which specify tail features among different markets performing under extreme conditions. The quantile cohesiveness proposed by Ando et al. (2022) is the blend of quantile vector auto regression with Diebold and Yilmaz (2012) methodology of spillovers for measuring the cohesiveness of the volatilities of markets for extreme higher (95th) and extreme lower (5th) quantiles. Findings â The findings of the QVAR divides spillover in two market condition i.e. median and extremes. In median market condition or we can say normal conditions findings of QVAR shows that Fan token is the major transmitter of shocks while Islamic crypto like X8X is major receiver of the shocks. In extreme condition the major transmitter remains the same i.e. Fan Tokens but major receiver of shock is Real Estate Tokens. Originality/ Value: â Study offer valuable insights to policy makers, portfolio managers and individual investors. For instance study enable the portfolio managers and investors to understand spillovers among Islamic Crypto, Non-Fungible Tokens, Fan Tokens and Real Estate tokens, which will help them in making suitable portfolios. By investigating the function of Islamic gold-backed cryptocurrencies as a new and alternative asset class that can be utilized by both portfolio managers and investors, looking to invest in Islamic Products, to lower their risk of investment. Research Implications: - This study brings novel insight for portfolio optimization and diversification. The findings of this study will have implications for global investor, researcher and policy makers.
This study identifies major approaches in token design for founders in the cryptocurrency/web3/blockchain space. The high failure rate of blockchain companies means that successful long-term performance will depend greatly on well-designed tokens. This study will integrate all prior research to highlight the most important aspects of structured tokenomics, including token utility, governance, and security. The study also contributes to the literature by introducing the Business Model Canvas as a conceptual framework that enables the integration of best practices for token design, drawing on both academic and industry literature. The results indicate significant gaps in the literature. This study offers new and practical insights for founders to enhance stakeholdersâ engagement, improve regulatory compliance, and ensure project viability in the volatile cryptocurrency market. Furthermore, this research generates new knowledge that bridges the gap between the theory and practice of tokenomics, laying the groundwork for future research to develop and refine token design strategies.
This paper explores how social influence and peer networks shape the adoption of cryptocurrencies and decentralized finance (DeFi) platforms. Drawing from qualitative interviews and social theory, the study examines how interpersonal communication, social media influence, and online communities impact user behavior. Findings reveal that peer endorsement and communal learning are strong drivers of trust and experimentation in the crypto space, especially in regions with limited institutional trust. Peer networks act as informal but powerful educational structures, providing newcomers with advice, emotional support, and real-time market insights. In many cases, peer encouragement is what propels hesitant individuals to take the first step toward using crypto wallets or engaging in DeFi protocols. However, the influence of peers can also perpetuate hype-driven narratives, misinformation, and herd behavior, leading to poor financial decisions or susceptibility to scams. The paper concludes with recommendations for leveraging peer networks in designing effective crypto awareness and onboarding strategies. These include integrating community leaders into education campaigns, offering platform incentives for verified peer mentorship, and collaborating with trusted influencers to communicate risks and best practices. Understanding the dynamics of social influence can help policymakers, educators, and platforms foster more ethical, inclusive, and informed crypto adoption pathways globally.
Using transaction cost economics (TCE) and agency theory, this paper examines how blockchain, smart contracts, and decentralized autonomous organizations (DAOs) reconfigure financial services across payments, wealth management, real estate, and corporate governance. Three research questions are addressed: (1) What are the quantifiable efficiency gains from blockchain-based real-time settlement compared with legacy systems? (2) How do blockchain technologies reduce intermediation and agency costs in wealth management and real estate? (3) Finally, to what extent do DAOs resolve or transform traditional corporate governance problems? By combining a present-value model calibrated to U.S. Automated Clearing House (ACH) data ($86.2 trillion in annual volume), comparative institutional analysis, and synthesis of empirical evidence from pilot implementations and on-chain governance metrics, this paper makes three principal contributions. First, real-time settlement yields approximately $12 billion in annual opportunity cost savings at the baseline 7.5% discount rate, with sensitivity analysis producing a range of $8â15 billion. The majority of gains accrue from moving to same-day or within-hour settlement. Second, tokenization and smart contract escrow substantially reduce real estate intermediation costs, blockchain-based digital identity streamlines wealth management onboarding, and a stablecoin taxonomy classifies fiat-collateralized, crypto-collateralized, and algorithmic designs by risk profile. Third, on-chain data reveal persistent governance token concentration (Gini > 0.98) and low voter participation (typically below 10%), exposing a gap between DAO theory and practice. Blockchain-specific risks are mapped to National Institute of Standards and Technology (NIST) Cybersecurity Framework 2.0, and mechanism design solutions, such as quadratic voting and AI-assisted proposal evaluation, are proposed to address whale dominance. Effective adoption requires hybrid architecture combining on-chain automation with off-chain structures for accountability and regulatory compliance.
Artificial intelligence (AI)-powered technology integration in social fintech has transformative potential to advance social responsibility and support sustainable development. This research examines a Blockchain-based lending mechanism that integrates centralized exchanges (CEX) and decentralized exchanges (DEX) to facilitate seamless financial transactions and equitable resource allocation. AI-driven tools are utilized to enhance transparency, accuracy, and security, while smart contracts facilitate the efficient management and verification of loan distribution. The proposed system focuses on helping underserved communities, poor regions, and green businesses, promoting fair and sustainable finance in line with the Sustainable Development Goals (SDGs). The hybrid ecosystem combines the liquidity and regulatory compliance of centralized exchanges with the autonomy and reduced intermediary involvement of decentralized exchanges. AI enhances loan processing, reducing biases and inefficiencies. This framework with smart contracts is to provide scalable, auditable lending aligned with sustainable goals. Machine Learning (ML) algorithms verified loan eligibility with the borrower dataset. The performance of Random Forest algorithms is good due to their robustness and ensemble learning features. Then, Optuna enhanced model tuning, and SHapley Additive exPlanations (SHAP) identified key parameters. Finally, Smart contracts ensured secure, autonomous execution of green loans based on ML verification and sustainability criteria.
This study determines whether Bitcoin enhances portfolio diversification and serves as a valuable investment asset during the COVID-19 crisis. In particular, we evaluate the significance and magnitude of the risk price associated with Bitcoinâs returns based on the ICAPM and NARDL models. Three methodological approaches were employed. First, we use the Intertemporal Capital Asset Pricing Model (ICAPM) to assess the effect of Bitcoin on a portfolio comprising 25 Fama-French portfolios. Second, a Nonlinear Autoregressive Distributed lag (NARDL) model explores Bitcoinâs impact on cross-sectional variation within the Fama-French portfolios, capturing potential asymmetric responses to price changes. Finally, we determine Bitcoinâs risk premium using the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model (FF3), and the Fama-French five-factor model (FF5). Bitcoin fails to provide significant diversification benefits for profitability factor (RMW), and exhibit insensitivity to value (HML) and investment (CMA). The NARDL model indicates a potential hedging role only during crypto market downturns. The factor models reveal that Bitcoin behaves differently than traditional assets, exhibiting low sensitivity to market risk and a negative relationship with the size premium, further supporting its potential for diversification within specific portfolio contexts. Our finding shows that Bitcoin can protect the 25 Fama-French portfolio when Bitcoin loses value.
The rapid expansion of Financial Technology (FinTech) is fundamentally reshaping financial systems, yet its role as a source of systemic risk and its dynamic connectedness with traditional energy and macroeconomic markets remain critically underexplored. This paper employs an integrated time-frequency framework to model financial spillover networks and demonstrates its utility in analyzing the connectedness between emerging FinTech sub-sectors, energy markets, and macroeconomic uncertainty. Using the Diebold and Yilmaz (2012) spillover index in the time domain and the BarunĂk and KĹehlĂk (2018) spectral decomposition in the frequency domain, we uncover a highly interconnected system: total connectedness reaches 43.62% for returns and 40.65% for volatility, showing that price shocks propagate more strongly than risk shocks. During the COVID-19 period, interconnectedness surged above 70%, highlighting how external shocks intensify contagion. We find that key FinTech indices such as Kensho Future Payments, KBW FinTech, and Kensho Alternative Finance act as major net transmitters, while the Distributed Ledger index, geopolitical risk, U.S. policy uncertainty, Brent oil, and U.S. 10-year Treasury yields are net receivers, signaling that within the financial network, shock propagation is now led by FinTech rather than emanating primarily from traditional macroeconomic indicators. Frequency results add important insight: volatility spillovers are mainly short-term (44.57%), reflecting transient fear contagion, while return spillovers are more persistent. Overall, our findings challenge the macro-driven spillover view and offer a time-sensitive framework for effective hedging and regulation. FinTech emerges as a key short-term shock transmitter, with clear implications for investorsâ hedging strategies and regulatorsâ systemic risk monitoring.
Mbonigaba Celestin, Jerryson Ameworgbe Gidisu, M. Vasuki & A. Dinesh Kumar
We examine how legal governance structures influence the reliability of blockchain based commercial transactions within emerging digital markets. We develop and empirically evaluate the Blockchain Legal Transaction Integrity Model using the Global Blockchain Regulation and Smart Contract Adoption Dataset covering the period 2020 to 2025 across major blockchain adopting jurisdictions including the United States, the United Kingdom, Singapore, Estonia, and Ghana. The model links regulatory clarity, compliance enforcement mechanisms, and legal recognition of smart contracts with commercial transaction integrity while accounting for institutional legal capacity as a conditioning factor. Quantitative analysis shows that stronger regulatory clarity, active enforcement supervision, and legally recognized smart contracts significantly improve transaction transparency, contract execution reliability, fraud reduction, and business trust in blockchain systems. Institutional legal capacity amplifies these effects by strengthening regulatory interpretation and dispute resolution capability. The results demonstrate that blockchain markets achieve reliable digital commerce not only through technological design but through coordinated legal governance structures. The findings advance institutional governance theory and provide policy guidance for regulators seeking to strengthen digital financial ecosystems and cross border blockchain commerce.
Alexander Kropiunig, Svetlana Kremer, Bernhard Haslhofer
Crypto Key Opinion Leaders (KOLs) shape Web3 narratives and retail investment behaviour. In volatile, high-risk markets, their credibility becomes a key determinant of their influence on followers. Yet prior research has focused on lifestyle influencers or generic financial commentary, leaving crypto KOLs' understandings of motivation, credibility, and responsibility underexplored. Drawing on interviews with 13 KOLs and self-determination theory (SDT), we examine how psychological needs are negotiated alongside monetisation and community expectations. Whereas prior work treats finfluencer credibility as a set of static credentials, our findings reveal it to be a self-determined, ethically enacted practice. We identify four community-recognised markers of credibility: self-regulation, bounded epistemic competence, accountability, and reflexive self-correction. This reframes credibility as socio-technical performance, extending SDT into high-risk crypto ecosystems. Methodologically, we employ a hybrid human-LLM thematic analysis. The study surfaces implications for designing credibility signals that prioritise transparency over hype.
Artificial Intelligence (AI) has become a critical driver of firm survival in the banking industry, particularly for deposit money banks (DMBs) facing increasing challenges such as economic volatility, regulatory compliance, cybersecurity threats, and rising customer expectations. This study explores the role of AI in enhancing operational efficiency, risk management, fraud detection, customer experience, and financial resilience in the banking sector. AI-powered technologies, including machine learning, predictive analytics, robotic process automation (RPA), and natural language processing (NLP), are transforming how banks analyze financial risks, detect fraudulent transactions, automate operations, and provide personalized banking services. Research findings indicate that AI adoption has led to a 35% reduction in loan defaults, a 40% improvement in operational efficiency, and a 60% decline in financial fraud cases, highlighting its transformative potential in ensuring the survival and competitiveness of DMBs. Despite these advancements, AI adoption in the banking sector is hindered by high implementation costs, cybersecurity vulnerabilities, workforce resistance, and regulatory uncertainties. Many banks, particularly in developing economies like Nigeria, struggle with legacy banking systems, lack of AI governance frameworks, and concerns over algorithmic bias in lending decisions. Additionally, AI-driven financial innovations, such as blockchain integration, decentralized finance (DeFi), and AI-powered ESG compliance solutions, are reshaping the banking industry, yet require strategic policy alignment and investment to maximize their benefits. The study identifies gaps in existing literature, including the need for empirical research on AIâs long-term impact on firm survival, its role in financial inclusion, and the ethical challenges of AI governance in banking. To bridge these gaps, future research should focus on developing AI implementation models suited to the challenges of emerging economies, exploring AIâs potential in expanding financial access to underserved populations, and strengthening AI-driven sustainability and ESG compliance frameworks in banking. As AI continues to evolve, deposit money banks must embrace a balanced approach that integrates AI innovation with regulatory oversight, cybersecurity safeguards, and workforce upskilling to ensure long-term survival and competitiveness in the digital financial landscape
Cryptocurrency and blockchain technology have emerged as important innovations in the global financial system. Cryptocurrency is a digital form of money that uses cryptographic techniques to ensure secure financial transactions. Blockchain technology acts as a decentralized and transparent ledger that records all transactions in a secure manner. The rapid growth of digital payments, financial technology, and global connectivity has increased the importance of cryptocurrency and blockchain in modern finance. This research paper examines the role of cryptocurrency and blockchain in transforming financial markets, improving transparency, and reducing transaction costs. The study is based on secondary data collected from financial reports, academic journals, and international organizations. The analysis indicates that blockchain technology has the potential to revolutionize financial systems by increasing efficiency, security, and accessibility in financial transactions.
The global financial landscape is experiencing significant transformation driven by technological advancements and evolving market dynamics. Moreover, blockchain technology has become a pivotal platform with widespread applications, especially in finance. Cross-border payments have emerged as a key area of interest, with blockchain offering inherent benefits such as enhanced security, transparency, and efficiency compared to traditional banking systems. This paper presents a novel framework leveraging blockchain technology and smart contracts to emulate cross-border payments, ensuring interoperability and compliance with international standards such as ISO20022. Key contributions of this paper include a novel prototype framework for implementing smart contracts and web clients for streamlined transactions and a mechanism to translate ISO20022 standard messages. Our framework can provide a practical solution for secure, efficient, and transparent cross-border transactions, contributing to the ongoing evolution of global finance and the emerging landscape of decentralized finance.
The rapid convergence of the Internet of Things (IoT) and decentralized finance (DeFi) is reshaping the digital economy by enabling autonomous, trustless, and value-driven interactions among connected devices. This paper provides a comprehensive survey of the emerging paradigm that combines IoT's pervasive sensing and communication capabilities with DeFi's programmable financial infrastructure. We first discuss the motivation behind this convergence and explore key opportunities, including autonomous machine-to-machine (M2M) payments, decentralized data marketplaces, and trustless IoT service provisioning. Despite its potential, IoT-DeFi integration introduces significant security and privacy challenges related to smart contract vulnerabilities, consensus protocol risks, oracle manipulation, and constrained device capabilities. We review existing mitigation approaches such as lightweight cryptography, secure contract design, and decentralized identity management, and critically assess their limitations in heterogeneous, resource-limited environments. Building on this analysis, identify research gaps and propose future directions emphasizing formal verification of IoT-integrated smart contracts, robust oracle design, interoperability frameworks, and privacy-preserving trust models. This survey systematically maps opportunities, threats, and open issues. In doing so, it guides researchers and practitioners toward building secure, scalable, and energy-efficient IoT-DeFi ecosystems for next-generation decentralized applications.
Over the past decade, the explosion of digital assets, including cryptocurrencies, non-fungible tokens (NFTs), and cloud-based accounts, has introduced complex legal questions that conventional inheritance regimes struggle to address. In Muslimâmajority jurisdictions and among Muslim communities worldwide, these questions intersect with the requirements of Islamic personal law, particularly the farÄâiḠ(obligatory heirsâ shares) and waᚣiyya (testamentary bequests). This study undertakes original empirical and doctrinal research to chart a path toward a unified fiqhâgrounded framework for digital asset succession. By combining doctrinal analysis of classical juristic sources, contemporary fatwas, and statutory developments with semiâstructured interviews among scholars, estate planners, and digital asset owners across Malaysia, Indonesia, Pakistan, and the United Kingdom, the research shows that digital assets are increasingly recognized as mal mutaqawwim (valuable property) but lack standardized protocols for identification, valuation, and transfer. The study reveals that differences in platform terms of service and crossâborder jurisdiction complicate heirsâ access to private keys and cloud accounts, exacerbating existing gender and socioâeconomic disparities. It proposes a model of âcustodial key trustsâ and eâwills that integrate digital asset inventories with farÄâiḠdistributions, allowing compliance with both shariah and civil laws. The paper argues that without coordinated legal reforms and educational initiatives, vast wealth stored in digital forms risks being lost or misappropriated, undermining the objectives of Ḽifáş alâmÄl (preservation of wealth) and social justice.
Samar Alsulaimani, Ming Zhao, Farookh Khadeer Hussain
⢠Innovative Fractional Ownership Framework: The Fractional Digital Asset Ownership (FDAO) model uses fractional NFTs (FNFTs) to facilitate the co-ownership of digital assets, focusing on software code. ⢠Addressing Ownership Management Challenges: Building on FNFT and blockchain technology, this study proposes an intelligent solution for fractional digital asset ownership that ensures the accurate tracking of ownership rights through the integration of FNFTs with blockchain technology. ⢠Practical Prototype Development: This study demonstrates the FDAO frameworkâs capability to securely and transparently manage handling digital asset transactions using FNFTs and smart contracts implemented through Remix and OpenZeppelin. ⢠Empirical Evaluation of FNFT Application: This research examines the effectiveness of FNFT frameworks in supporting fractional ownership, highlighting their potential for real-world digital asset applications. ⢠Market Accessibility and Inclusivity: By enabling fractional ownership, the FDAO model increases accessibility to digital assets and supports ownership democratisation. ⢠Identification of Limitations and Future Directions: The study discusses the challenges related to regulatory compliance, scalability, and costs associated with FNFTs and other blockchain platforms and outlines compliance strategies that may support a broad range of applications. A new generation of digital assets is being managed using blockchain technology and non-fungible tokens (NFTs), which introduce novel opportunities for verifying ownership rights and establishing provenance. This paper presents an innovative framework called Fractional Digital Asset Ownership (FDAO), which aims to create NFTs for digital artifacts, such as software code, and extend their functionality through fractionalized NFTs (FNFT). Leveraging the Model-View-Controller (MVC) design pattern, FDAO enables effective co-ownership tracking across the lifecycle of digital assets, providing a structured and efficient mechanism for defining and managing co-ownership. A system prototype has been developed and tested in an integrated development environment (IDE) using decentralised applications (DApps) and smart contracts. Unlike existing NFT-based models, FDAO incorporates an intelligent, automated fractionalization and verification mechanism that combines the ERC-1155 and ERC-20 standards to enhance co-ownership management and scalability. This integration addresses the critical challenges related to transparency, security, and lifecycle management in digital asset co-ownership. The prototype, implemented using Remix and OpenZeppelin, demonstrates how FDAO enables secure, transparent, and efficient transfer and management of digital assets. By integrating FNFT functionality with smart contracts, the framework provides a robust, scalable, and intelligent method for managing digital assets. It also maintains transparency and trust throughout the asset lifecycle.
The rapid rise in cryptocurrencies has created an investment environment marked by unprecedented levels of information volume, fragmentation, and volatility. While prior research has examined drivers of trust and adoption in crypto markets, far less is known about the psychological consequences of information overload on investor decision-making. This study addresses this gap through nineteen semi-structured interviews with individual cryptocurrency investors, analyzed using an inductive, manually conducted thematic approach. Findings reveal four interconnected dynamics: decision fatigue and paralysis, heuristic reliance on influencers and peers, emotional strain characterized by anxiety and fear of missing out (FOMO), and diverse coping strategies ranging from selective filtering to withdrawal. These results demonstrate that crypto investing is not only a financial process but also a cognitively and emotionally taxing experience. By linking investor narratives to broader theories of decision fatigue, bounded rationality, and consumer vulnerability, the study contributes to interdisciplinary debates in marketing, behavioral finance, and consumer psychology. Practically, the findings highlight the need for clearer communication strategies, supportive platform design, and financial education initiatives that help investors manage cognitive strain and decision fatigue. In a market where credibility is fluid and decisions are often made under conditions of overload, understanding the psychological dimensions of investment behavior is essential.
Central Bank Digital Currency (CBDCs) are becoming a new digital financial tool aimed at financial inclusion, increased monetary stability, and improved efficiency of payment systems, as they are issued by central banks. One of the most important aspects is that the CBDC must offer secure offline payment methods to users, allowing them to retain cash-like access without violating Anti-Money Laundering and Counter-terrorism Financing (AML/CFT) rules. The offline CBDC ecosystems will provide financial inclusion, empower underserved communities, and ensure equitable access to digital payments, even in connectivity-poor remote locations. With the rapid growth of Internet of Things (IoT) devices in our everyday lives, they are capable of performing secure digital transactions. Integrating offline CBDC payment with IoT devices enables seamless, automated payment without internet connectivity. However, IoT devices face special challenges due to their resource-constrained nature. This makes it difficult to include features such as double-spending prevention, privacy preservation, low-computation operation, and digital identity management. The work proposes a privacy-preserving offline CBDC model with integrated secure elements (SEs), zero-knowledge proofs (ZKPs), and intermittent synchronisation to conduct offline payments on IoT hardware. The proposed model is based on recent improvements in offline CBDC prototypes, regulations and cryptographic design choices such as hybrid architecture that involves using combination of online and offline payment in IoT devices using secure hardware with lightweight zero-knowledge proof cryptographic algorithm.
As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bankâyear observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m.
The concept of alternative finance is explored from a narrow and broad perspective. The latter defines it as segments of "gray" financial markets, outside the scope of regulation and traditional finance. "Dark" liquidity poolsâtrading transactions of major players in securities and currencies, operating anonymously, opaquely, and hidden from the public in the over-the-counter space through automated digital trading platformsâare presented as one element of the alternative finance system. The advantages and disadvantages of "dark" pools for financial market participants and exchange infrastructure are discussed. The problem of liquidity fragmentation caused by "dark" pools is highlighted, a problem inherent in decentralized finance, where liquidity is not concentrated on a single platform or trading system, but distributed among many. Emphasis is placed on the insufficient or complete lack of oversight and regulation of this alternative financial market segment. Examples of legislative and regulatory acts in a number of countries are provided.
Abstract This study analyzes the progression of the Financial Technology (FinTech) sector and its basic technological drivers in the United States, emphasizing investment trends and the entrepreneurial impact on the digital financial landscape. The research employs a descriptive-analytical approach: the descriptive component outlines the evolution of the FinTech ecosystem, while the analytical component examines investment trends and technology drivers shaping the sector. The factors for technology investment were recalibrated by reassessing the compound annual growth rate (CAGR) using benchmark values from secondary market research. The resulting dataset presents smoothed trend estimations rather than separately recorded annual values, offering a solid empirical basis for the ensuing statistical models. The results indicate rapid growth in the FinTech sector, with the United States retaining its leading global position due to strong technological infrastructure and substantial venture capital support, largely driven by the digital payments segment. The empirical study reveals remarkably robust and consistent positive correlations, with Pearson correlation coefficients (r) surpassing 0.978 (p < 0.01) in all models. Cloud computing demonstrated the strongest correlation (r = 0.9856), closely followed by AI (r = 0.9854). The computed regression models exhibited exceptional explanatory power, with coefficients of determination (R 2 ) ranging from 0.9579 to 0.9714. Blockchain technology yielded the largest marginal regression coefficient (β = 1101.47), highlighting its significant potential to transform conventional financial intermediation through decentralized finance (DeFi) ecosystems. The study indicates that the high correlation coefficients (r > 0.97) predominantly reflect a fundamental structural co-movement of technological investment cycles within the U.S. FinTech sector, which is intrinsically associated with the employed smoothed trend estimations. The report ultimately promotes strategic collaboration between traditional financial institutions and FinTech startups, emphasizing the need for adaptive regulatory frameworks that effectively reconcile entrepreneurial innovation with systemic financial stability and digital financial inclusion.
Abstract Cryptocurrency has been the subject of heightened regulatory and investor attention in recent years, and regulators and policymakers across the globe are deliberating on how to account for, regulate, tax, and oversee digital assets and cryptocurrency marketplaces. Yet researchers have a limited understanding of key attributes of those who deal in crypto assets, such as whether their financial sophistication differs from that of other investors. Using U.S. administrative data, we provide evidence on (i) the attributes of taxpayers reporting cryptocurrency sales to the IRS, (ii) how these attributes are evolving, and (iii) how investors treat cryptocurrency versus other financial assets in certain settings. The results suggest that average reporting cryptocurrency sellers exhibit demographic attributes generally associated with less financial sophistication and are more likely to trade in meme stocks. Overall, we provide timely evidence that can inform cryptocurrency policy deliberations by highlighting the characteristics of taxpayers who appear to report cryptocurrency sales.
Smart contracts underpin high-value ecosystems such as decentralized finance (DeFi), yet recurring vulnerabilities continue to cause losses worth billions of dollars. Although numerous security analyzers that detect such flaws exist, real-world attacks remain frequent, raising the question of whether these tools are truly effective or simply under-used due to low developer trust. Prior benchmarks have evaluated analyzers on synthetic or vulnerable-only contract datasets, limiting their ability to measure false positives, false negatives, and usability factors that drive adoption. To close this gap, we present a mixed-methods study that combines large-scale benchmarking with practitioner insights. We evaluate six widely used analyzers (i.e., Confuzzius, Dlva, Mythril, Osiris, Oyente, and Slither) on 653 real-world smart contracts that cover three high-impact vulnerability classes from the OWASP Smart Contract Top Ten (i.e., reentrancy, suicidal contract termination, and integer arithmetic errors). Our results show substantial variation in accuracy (F1 = 31.2 to 94.6%), high false-positive rates (up to 32.6%), and runtimes exceeding 700 seconds per contract. We then survey 150 professional developers and auditors to understand how they use and perceive these tools. Our findings reveal that excessive false positives, vague explanations, and long analysis times are the main barriers to trust and adoption in practice. By linking measurable performance gaps to developer perceptions, we provide concrete recommendations for improving the precision, explainability, and usability of smart-contract security analyzers.
Abstract The rapid growth of digital financeâincluding FinTech platforms, online payment gateways, and e-commerce marketplacesâhas revolutionized global financial systems while significantly expanding the cyber-attack surface. Sophisticated attacks such as AI-generated deepfakes, automated malware, ransomware, and synthetic identity fraud now threaten financial transactions. In response, cybersecurity strategies are evolving toward decentralized models, AI-enabled detection systems, Zero Trust architectures, and quantum-safe cryptography. This paper synthesizes recent academic research and industry developments (2025â2026), covering threat taxonomies, defensive strategies, emerging attack vectors, and regulatory enhancements in payment authentication. The integration of these trends underscores the necessity of robust, AI-driven, and compliance-aware security architectures for securing modern financial ecosystems.
Smart contract is a type of contract that exercised automatically if requirements are met in trades, the data on chains is available at all time and no edit or central authority intervene is allowed. In China, SMEs often face high requirement of lending from bank, information asymmetry and region difference when financing. In this research, it is proved that smart contracts reduce SME financing cost via lowering human labour and spend time, which is one of reasons that smart contracts and blockchain are welcomed in SMEs. The government should set related regulations on smart contracts and technical designers need to improve systems in the future so that more SMEs could get benefits during financing programs.