The accelerating digitization of healthcare has amplified the demand for secure, interoperable, and privacy-preserving information systems capable of managing sensitive patient data across diverse institutions. Traditional Health Information Systems (HIS) often struggle with fragmentation, data breaches, and lack of trust, posing significant barriers to integrated care and real-time medical decision-making. Blockchain technology—characterized by its decentralized architecture, cryptographic security, and immutability—offers a transformative paradigm for healthcare data management. This paper explores the development and deployment of Blockchain-Powered Health Innovation Information Systems (BHIIS), focusing on their potential to enable secure, verifiable, and scalable exchange of electronic health records (EHRs) across providers, payers, and public health institutions. By combining distributed ledger technology with smart contracts, BHIIS can automate data-sharing permissions, enhance patient control over personal health data, and ensure traceable access logs that comply with regulatory standards such as HIPAA and GDPR. This study examines architectural frameworks that integrate blockchain with interoperable health data standards (e.g., HL7 FHIR), enabling seamless communication among heterogeneous systems without compromising privacy. We evaluate consensus mechanisms, off-chain storage strategies, and identity management schemes that address scalability and data ownership concerns in real-world healthcare networks. Furthermore, the paper analyzes emerging use cases—including pandemic response, clinical trials, and chronic disease management—where blockchain-enhanced systems have demonstrated tangible benefits in accuracy, transparency, and trust. Ethical and infrastructural considerations, such as stakeholder governance, energy consumption, and digital divide challenges, are also discussed. By presenting a roadmap for implementing BHIIS, this work contributes to shaping next-generation health IT ecosystems that prioritize patient-centricity, resilience, and innovation.
Hiago Vinícius Benedito dos Santos, Raissa Rosa dos Santos Januario, Ravelly Carvalho Zanatta, Saulo Neves Matos · 5 authors
In recent years, blockchain technology has established itself as an effective, secure, and transparent data storage solution. In this context, smart contracts play a fundamental role by enabling the automated execution of agreements without intermediaries. With the advancement of language models, the opportunity to automatically generate these contracts has emerged, raising concerns about their reliability and potential vulnerabilities. This article proposes a comparative analysis of the available language models for developing smart contracts using Ethereum Virtual Machine’s contracts as a case study. Experiments were made using various Large language models using different metrics to evaluate the susceptibility to vulnerabilities and computational cost. After comparing various models, ChatGPT appears to be the most suitable for generating smart contracts due to its higher compilation rate and, consequently, a larger sample size, despite detecting more vulnerabilities.
This study presents a comparative analysis of trademark protection in the metaverse and the registration of virtual goods and non‐fungible tokens (NFTs) across three distinct legal systems: those of the United States, the United Kingdom, and South Korea. Drawing on recent case law and evolving administrative guidelines, this study examines how traditional trademark doctrines—such as the likelihood‐of‐confusion standard in the U.S. under the Lanham Act, source-identifying function under the UK Trade Marks Act 1994, and proactive legislative reforms implemented by the Korean Intellectual Property Office—are being adapted to address the challenges posed by digital and virtual environments. Specifically, this study analyzes landmark cases such as Hermès International v. Rothschild and Yuga Labs, Inc. v. Ripps , which illustrate the extension of trademark protection to NFTs and other digital assets, as well as the interplay between trademark rights and freedom of expression. It also evaluates recent updates to international classification frameworks—including the 2024 Nice Classification and the Madrid Protocol—and discusses their implications for ensuring uniformity and effective enforcement of trademarks in a borderless digital market. The findings reveal that while each jurisdiction applies its own legal traditions to metaverse trademark disputes, all share a common policy objective: to prevent consumer confusion and safeguard brand integrity in an increasingly digital economy. Ultimately, the study advocates for proactive registration of trademarks as virtual goods and NFTs to streamline enforcement and enhance legal certainty, thereby fostering innovation and facilitating global trade in virtual environments.
Najmus Sakib Sizan, Md. Abu Layek, Khondokar Fida Hasan
To improve crop forecasting and provide farmers with actionable data-driven insights, we propose a novel approach integrating IoT, machine learning, and blockchain technologies. Using IoT, real-time data from sensor networks continuously monitor environmental conditions and soil nutrient levels, significantly improving our understanding of crop growth dynamics. Our study demonstrates the exceptional accuracy of the Random Forest model, achieving a 99.45\% accuracy rate in predicting optimal crop types and yields, thereby offering precise crop projections and customized recommendations. To ensure the security and integrity of the sensor data used for these forecasts, we integrate the Ethereum blockchain, which provides a robust and secure platform. This ensures that the forecasted data remain tamper-proof and reliable. Stakeholders can access real-time and historical crop projections through an intuitive online interface, enhancing transparency and facilitating informed decision-making. By presenting multiple predicted crop scenarios, our system enables farmers to optimize production strategies effectively. This integrated approach promises significant advances in precision agriculture, making crop forecasting more accurate, secure, and user-friendly.
Iulia Cristina Iuga, Raluca Andreea Nerişanu, Larisa-Loredana Dragolea
This study investigates the risk spillover between clean and dirty cryptocurrencies and their impact on green finance indexes (solar, wind, and nuclear energy) and regional economic indexes (Baltic Dry Index and CRB Index), with data processed using the diagonal BEKK model. The results identify several dirty cryptocurrencies such as: Ethereum Cash (ETC), Litecoin (LTC), and Bitcoin (BIT) as potential diversifiers and hedges with specific green energy and economic indexes. Our findings show that news from the cryptocurrency markets predominantly have a positive, significant effect on the covariance with green finance indices. The study also presents the covolatility spillover effect, showcasing the impact of a return shock in one market, such as the cryptocurrency market or the green finance market, on the co-volatility between markets, including regional economic indices like the Baltic Dry Index and CRB Index. The analysis reveals differential spillover patterns between clean and dirty cryptocurrencies and various green finance indices, highlighting the complexity of their interactions and the varying degrees of influence on regional economic indicators.
This paper examines how blockchain technology and the Metaverse can address persistent challenges in corporate compliance, with a focus on mitigating criminogenic asymmetries—such as regulatory arbitrage and opacity in cross-border transactions—through decentralized, transparent solutions. By contrasting the U.S. and Italian legal frameworks, we highlight the limitations of retrospective compliance evaluations and propose blockchain-enabled innovations, including immutable audit trails, smart contracts for automated enforcement, and Decentralized Autonomous Organizations (DAOs) to decentralize governance and embed compliance into protocol design. The Metaverse offers a simulated environment for stress-testing compliance protocols against emerging risks, while criminological theories (e.g., global anomie, legal-illegal interfaces) contextualize regulatory gaps in digital economies. We argue that DAOs, as digital-native entities, could revolutionize compliance by replacing hierarchical oversight with algorithmic governance, though challenges like jurisdictional fragmentation and identity verification persist. The study underscores the need for adaptive regulatory frameworks to harness these technologies while balancing transparency, accountability, and privacy.
The integration of blockchain technology into smart city governance frameworks is revolutionizing the way urban management systems are structured, enabling secure, transparent, and decentralized decision-making processes. This chapter explores the transformative potential of blockchain-enabled decentralized governance models in enhancing accountability, reducing bureaucratic inefficiencies, and promoting citizen participation. The application of smart contracts and distributed ledgers is examined as a means to automate governance functions, facilitate real-time resource distribution, and ensure trust among stakeholders. Through detailed case studies and critical analysis, the chapter highlights practical implementations of blockchain in local governments, such as energy trading, public service automation, waste management, and participatory budgeting. In addition, the challenges of citizen engagement, legal compliance, and technological integration are addressed with forward-looking strategies for overcoming these barriers. This chapter contributes a comprehensive understanding of how blockchain can reshape urban governance ecosystems, offering scalable, resilient, and inclusive solutions for future smart cities.
Ângela Filipa Oliveira Gonçalves, Shafik Faruc Norali, Clemens Bechter
The paper investigates current and future pricing models in the European healthcare sector. European countries follow a universal healthcare system, whereas the United States rely on a mix of private insurers, government programmes, and private payments. It is becoming obvious that the European “free” healthcare systems are not sustainable in the long run. The authors propose a private Buy-Now-Pay-Later (BNPL) alternative. BNPL is common practice in retailing but highly unusual in healthcare. The authors suggest to enhancing BNPL further by adding AI and blockchain/crypto technology. However, there are three hurdles to overcome, namely, cryptocurrency volatility, regulatory uncertainty, and adoption barriers. Our field research investigated the acceptance barriers especially whether European medical service providers would accept cryptocurrency payments and the BNPL model in general. Our survey is based on 366 European medical service providers, mainly medical doctors. The results show that there is willingness to accept cryptocurrencies. As recommendation we outline how a fully integrated AI-powered BNPL model with cryptocurrency payments and smart contracts including BNPL Tokenisation in a decentralised financial market could work to the benefit of all stakeholders.
The integration of artificial intelligence (AI) into Internet of Things (IoT) systems has outpaced the development of mechanisms to explain and audit automated decisions, creating a transparency gap. This paper addresses the research problem of establishing immutable audit trails for AI-driven IoT decisions to enhance trust, accountability, and regulatory compliance. We propose a blockchain-based framework that logs each AI inference and its provenance data (inputs, model parameters, and outputs) on a tamper-proof distributed ledger, ensuring every decision is traceable and auditable. The technical method- ology centers on a permissioned blockchain ledger deployed alongside IoT infrastructure. IoT devices and edge nodes commit decision records via smart contracts, producing an im- mutable, timestamped log resistant to manipulation. This approach leverages blockchain’s decentralization and cryptographic integrity to guarantee non-repudiation and data integrity. We detail how the system design balances transparency with privacy (e.g. hashing personal data) to remain compliant with data protection norms. The solution aligns closely with emerging regulatory frameworks such as the EU AI Act’s mandate for automated decision logs and traceability, and GDPR’s accountability and transparency requirements (e.g. maintaining audit logs of AI decisions for explainability). We demonstrate the frame- work’s applicability across domains: healthcare IoT, to log diagnostic AI recommendations for accountability; and industrial IoT, to track autonomous control actions - showing that our approach generalizes to diverse high-stakes environments. The paper’s contributions include a novel architecture for AI decision provenance in IoT, a detailed implementation on a blockchain ledger to securely record AI decision-making processes, and an evaluation of its performance and compliance benefits. By providing a reliable, immutable audit trail for AI in IoT, this work enhances transparency and trust in autonomous systems and offers a timely solution for auditable AI in an era of increasing regulatory scrutiny.
The Engineering, Procurement, and Construction (EPC) industry faces significant financial management challenges due to the complexity of project financing, milestone-based payments, and multi-stakeholder collaboration. Traditional on-premise ERP financial systems are often inefficient, leading to delays in financial reporting, security vulnerabilities, and regulatory compliance difficulties. This study explores the development of cloud-based financial solutions tailored to the EPC industry, examining the benefits, challenges, and applicability of existing models such as Software as a Service (SaaS), Platform as a Service (PaaS), and Blockchain-based decentralized finance (DeFi). A Hybrid Cloud-Based Financial Framework is proposed, integrating SaaS for accounting, PaaS for customization, and Blockchain for secure transactions. Experimental validation demonstrates that cloud adoption reduces financial processing time by 87.5%, enhances cash flow visibility, improves security, and increases regulatory compliance efficiency by 40%. This paper highlights the importance of AI-driven predictive analytics, automated compliance, and hybrid cloud models in modern EPC finance and proposes strategies for overcoming integration challenges, cybersecurity risks, and workforce adoption barriers. Future research should focus on scaling hybrid cloud solutions globally and integrating AI-powered risk assessment tools.
In the modern financial landscape, cryptocurrency investments have gained substantial traction among both seasoned and novice investors. However, given the complexity, volatility, and risk associated with digital currencies, financial literacy plays a fundamental role in shaping an individual’s investment decisions. This study explores the intricate relationship between financial literacy and cryptocurrency investment behavior, analyzing how knowledge of financial principles influences an investor’s ability to assess risk, formulate strategies, and make informed decisions in the highly speculative crypto market. This research adopts a mixed-methods approach, combining both qualitative and quantitative data collection techniques. Surveys and structured interviews were conducted among cryptocurrency investors of various demographics, ranging from experienced market participants to first-time investors, to assess their understanding of financial concepts and their influence on investment strategies. Additionally, secondary data was sourced from financial reports, academic journals, and regulatory analyses to contextualize the findings within broader financial literacy frameworks. The results of the study indicate that individuals with a higher level of financial literacy are more likely to engage in thorough research before investing, effectively utilize risk management techniques, and demonstrate a more disciplined approach to cryptocurrency trading. Conversely, a subset of investors, despite having adequate financial knowledge, continues to engage in speculative trading driven by social trends, herd mentality, and market hype, often leading to irrational financial decisions. This suggests that while financial literacy is crucial, external factors such as psychological influences, peer recommendations, and media narratives can significantly impact investment behavior. The study further highlights the role of financial education in mitigating impulsive investment decisions. It emphasizes the need for targeted educational programs that equip investors with the analytical skills required to navigate the complexities of digital asset investments. By understanding key financial concepts such as market volatility, asset diversification, and risk assessment, investors can make more informed decisions and minimize exposure to financial losses. In conclusion, this study provides valuable insights into the role of financial literacy in shaping investment behaviors in the cryptocurrency space. The findings contribute to the ongoing discussion on financial education and its implications for emerging markets, digital assets, and investment decision-making processes. The study also serves as a foundation for further research on how investor psychology, regulatory frameworks, and technological advancements intersect with financial literacy in the evolving cryptocurrency ecosystem.
Financial services enterprise systems are at a critical inflection point as traditional monolithic architectures struggle to meet evolving market demands, customer expectations, and regulatory requirements. This article explores the transformative potential at the intersection of artificial intelligence, cloud-native microservices, and intelligent data processing for building next-generation financial systems. It examines how these technological paradigms can be leveraged to overcome legacy challenges and regulatory pressures while creating more resilient, compliant, and innovative enterprise architectures. It provides a comprehensive roadmap for transformation, including assessment strategies, incremental modernization patterns, and DevSecOps implementations tailored to financial services. Through case studies of successful implementations and analysis of common challenges, the article offers practical insights for financial institutions navigating this complex evolution. Looking ahead, It identifies quantum-ready architecture, decentralized finance integration, and ambient computing as key developments that will shape future financial enterprise systems, emphasizing the importance of strategic preparation in an increasingly digital financial landscape.
This study explores the optimized application of combining blockchain (Blockchain) and artificial intelligence (AI) in the intelligent risk control of decentralized finance (DeFi). Although the decentralization and transparency of DeFi have driven financial innovation, they have also introduced risks related to market manipulation, smart contract vulnerabilities, and liquidity. Traditional centralized risk control approaches struggle to adapt. This research proposes a blockchain+AI-based intelligent risk control framework. Blockchain’s tamper-resistance enhances transaction security, while AI’s intelligent learning capabilities improve risk identification. Experimental results show that this model outperforms traditional solutions in detection accuracy (94.1%), false alarm rate (2.1%), and detection latency (180ms), and it remains robust under high market volatility. The findings suggest that combining blockchain and AI can effectively strengthen DeFi risk control, enhance system transparency and security, and provide theoretical and practical directions for future intelligent and automated risk management.
Sheshadri Chatterjee, Tomáš Klieštik, Zuzana Rowland, Martin Bugaj
Research background: Internet of Things devices and sensors, artificial intelligence-based digital asset trading and digital twin-based extended reality technologies, and autonomous robotic and enterprise resource planning systems can be leveraged in 3D semantic scene completion and metaverse-based commercial transactions across Internet of Things-based business environments. Distributed ledger and enterprise business technologies, shop-floor digital twin synthetic data, and 3D simulation and visualization systems configure integrated multi-physics workflows in hyper-realistic immersive industrial environments for artificial intelligence-based business value. Digital twin-based Internet of Robotic Things, robotic swarm and multi-modal machine learning algorithms (with regard to enterprise total factor productivity), and virtual and augmented reality simulation technologies are pivotal in spatial planning processes. Industrial product data and manufacturing value chain management support digital twin-based virtual factory modeling in collaborative immersive 3D visualization environments. Purpose of the article: We show that interconnected business process management and metaverse economic organizational structures, immersive economic and entrepreneurial knowledge image-based modeling (for big data-driven product development processes), and remote autonomous equipment control and monitoring integrate digital twin-enabled 6G Tactile Industrial Internet of Things, deep reinforcement learning and image processing algorithms, and event-driven signal processing for collaborative economic value co-creation. Deep learning-based visual recognition and industrial extended reality technologies, 3D production management modeling, and Internet of Things industrial and mobile sensing networks are pivotal in production operation management, as deep learning-based multi-source data fusion assists autonomous industrial manufacturing processes across interactive 3D immersive business and synthetic manufacturing environments. Collaborative robotic cyber-physical production and generative Artificial Intelligence of Things-based systems (in terms of managerial business value), artificial intelligence-based perceptual and cognitive technologies, and spatial mapping and machine intelligence algorithms enhance manufacturing process visualization, as industrial big data sharing and interoperability are functional in 3D semantic scene completion for sustainable business and economic growth across big data-driven immersive virtual industrial manufacturing environments. Methods: We inspected Tracxn (the Industrial Metaverse section) for the first 100 companies in terms of Tracxn score for X-corn status (i.e., Minicorn, Soonicorn, or none), total equity funding (USD), and company stage (i.e., Seed, Funding Raised, Unfunded, Public, Acquired, Acqui-Hired, and Series A, B, C, D), and identified three main topics for analysis that would lead to tangible business outcomes. We examined the performance management of shop floor virtualization: connected digital twins increase production and logistics process optimization in production environments across the industrial metaverse, facilitating photorealistic production system 3D modelling and simulation. We appraised integrated diagnostic functionalities of real-time simulation implementation for error elimination and machine parameter adjustment in immersive planned production lines by synthetic image data sets and collaborative workflows. We determined digital twin-based data synthesis operational procedures and interconnected use cases across industrial scalable infrastructures for value chain efficiency. Findings & value added: We identified the specific integrated operational simulation functions and production tasks, key performance indicators of shop floor autonomous and value creation systems, and industrial process parameters for predictive quality and fault detection, resulting in production loss reduction by use of industrial metaverse technologies. By use of operational data with regard to the technological management of the selected companies, quantitative analysis determines how immersive collaborative business process and extended reality-driven industrial metaverse technologies lead to economic value co-creation across 3D digital twin factories and cyber-physical manufacturing enterprises. The main value added derived from our research is that cloud-based collaborative 3D visualization and neuromorphic computing systems, 6G sensing and holographic simulation technologies, and machine intelligence and environment awareness algorithms (for business performance and productivity) can be leveraged in machine vision-based defect prediction, detection, diagnosis, and management. Virtual reality space convergence and object connection operate in Internet of Things-based sensing device performance monitoring across Internet of Things-based business environments. Virtual assembly lines and manufacturing enterprises necessitate machine learning-based production forecasting techniques, 3D object detection and tracking, and industrial autonomous and cyber-physical production systems, supporting spatial computing and predictive maintenance algorithms in collaborative immersive virtual environments.
This article explores the integration of artificial intelligence into fintech risk management frameworks, examining how predictive analytics are revolutionizing risk assessment and mitigation capabilities across the financial services industry. It investigates the evolution of risk management within the rapidly changing fintech landscape, highlighting how traditional approaches prove increasingly inadequate in addressing complex challenges like real-time fraud detection, cybersecurity threats, alternative credit assessment, cryptocurrency volatility, and decentralized finance liquidity risks. The article presents a comprehensive analysis of AI-powered solutions across key risk domains, including credit risk assessment, fraud detection, and market risk modeling, demonstrating their superior performance compared to conventional methods. It further outlines a structured framework for enterprise AI implementation, addressing the critical dimensions of data infrastructure, model development, operational integration, and continuous adaptation. The article also examines significant implementation challenges related to regulatory compliance, model explainability, data quality, and talent requirements. Finally, it explores emerging trends that will shape the future of AI-driven risk management, including federated learning, quantum computing, automated risk mitigation, and ecosystem-wide risk intelligence capabilities.
Özlem Sayılır, Ahmet Özkul, Mehmet Balcılar, Ronald Kuntze
Using blockchain adoption (BCA) data for 81 leading public companies in 2021, this study examines the impact of blockchain adoption on organizations’ environmental, sustainability, and governance performance. Employing the 2022 ESG scores from LSEG (Refinitiv) Database, which assess corporate sustainability performance across environmental, social, and governance dimensions, we regress ESG scores against blockchain adoption levels, company size, and various financial performance metrics. The results from the regression analysis reveal that blockchain adoption is significantly and positively associated with two sub-dimensions of environmental sustainability performance: resource usage and emissions. Additionally, firms exhibiting higher profitability and greater financial leverage appear to more effectively control blockchain adoption to enhance their corporate sustainability performance. These findings support the notion that blockchain adoption offers eco-efficient solutions that contribute to improved corporate sustainability performance, particularly through improved resource management and emissions control, while also offering actionable recommendations for policymakers and industry leaders.
Scott Shackelford, Michael Mattioli, Jeffrey P. Prince, João Marinotti
Abstract The chapter explores the economic implications of the Metaverse, focusing on its underlying economic mechanisms, consumption patterns, supply and demand dynamics, and the potential coexistence with the physical world. It discusses the concept of scarcity in the digital realm, where some goods and services may exhibit scarcity due to physical constraints or costs, while others may not be scarce, due to digital replication, non-fungible tokens (NFTs), etc. The chapter also delves into the supply and demand of the Metaverse, distinguishing between infrastructure and virtual goods/services within it, and considers the potential emergence of one or multiple Metaverses. Potentially impactful factors include technological challenges, economies of scale, barriers to entry, and regulatory considerations. Additionally, it examines how the Metaverse and physical world may interact as complements, substitutes, or independently in terms of products and services.
Gopal Krishan Prajapat, S. Pradeep, Dharmendra Kumar Yadav
Blockchain is the technology which greatly attracted the industries as well as the academics of the educational system because of its variety of applications and innovations around the globe. Smart contract is one of the most highly used technological move in the blockchain technology which increased its attention among the researchers. A smart contract has been embedded in the blockchain as an agreement that does not need any third-party intervention and executes automatically to perform different sophisticated tasks. Significant research has been done in the area of smart contract in blockchain in recent years. The smart contract has its impact in many industrial applications like supply chain management, digital identity, IOT, business processes etc. This paper aims to review the recent work that has been done in the area of smart contracts in different domains. We will present a comparative study of smart contract platforms, languages and applications under different categories like security, management, social application needs, etc.
The significant progress in information technology has accelerated the rapid development of social manufacturing (SM), making performance monitoring a crucial aspect of SM management. Nonetheless, it encounters issues regarding low trust among participants and centralization in the management platform. Blockchain is a new decentralized infrastructure and distributed computing paradigm that verifies and executes business logic based on smart contracts. Although blockchain has been applied to SM to ensure credibility and decentralization, there is a lack of research on smart contracts for blockchain systems to achieve performance monitoring in SM. Therefore, in the paper, a smart contract model was designed to meet performance monitoring in SM, specifically, a manufacturing promise model was developed to define the specific composition of relative elements in the performance promise between participants, and then a state transfer rule model was established to describe state change rule for the manufacturing promise. Next, a smart contract model for performance monitoring in SM was designed based on the established models. Finally, the model is validated through a case study of SM to produce air-conditioning compressor valves, the results show that the smart contract model is efficient in monitoring the performance states in SM. The model can help the managers in SM monitor the real-time performance conditions and ensure the production plan is completed on schedule.
This paper aims to assess the current state of research landscape of the role of FinTech in the digitalization of financial services through a bibliometric analysis using scientometric software (VosViewer). We analyzed a dataset of 585 documents as indexed by Scopus, published between 2015 and 2025 to generate network maps and identify emerging trends in the field. The bibliometric analysis delves into various key areas within financial services, including digital transformation, decentralized finance, artificial intelligence, and blockchain technology. The results revealed a notable rise in the publication volume throughout the years, reflecting the role of modern technologies in transforming financial systems and enhancing user experiences. Geographically, certain countries represent the highest number of publications in the field of FinTech and the digitalization of financial services such as India, China and the United States. These findings provide a foundation for researchers to foster blockchain, artificial intelligence, and decentralized finance, to drive the development and transformation of financial services.
The rapid development of automation and artificial intelligence (AI) is causing a significant upheaval in the banking sector.These technological advancements are boosting client experiences, increasing financial efficiency, and altering the way banks function.With an emphasis on topics like fraud detection, risk management, customer service (think chatbots and virtual assistants), personalized banking, and automating repetitive processes, this study examines how banks are presently utilizing AI and automation.While highlighting the major advantages-such as reducing expenses, reducing mistakes, and expediting decision-making-it also addresses the drawbacks, including concerns about data privacy, maintaining regulatory compliance, and the effect on employment.According to the study, further integration of technologies such as robotic process automation (RPA), machine learning, and natural language processing is anticipated in the future, which will increase the intelligence and adaptability of banking systems.Also, it looks at new developments that have the potential to drastically change the sector, such as open banking, decentralized finance (DeFi), and AI-powered predictive analytics.As the report concludes, banks must carefully consider ethical issues, make investments in staff upskilling, and figure out how humans and computers can collaborate efficiently, even though AI and automation present enormous prospects for innovation and expansion.Although the banking industry has a bright future, maximizing the potential of new technologies will require careful planning.
The study analyzes the global regulatory landscape for blockchain assets, particularly cryptocurrencies and non-fungible tokens, focusing on the motivations behind policymaker actions, the diversity of regulatory approaches, the challenges posed by decentralized technologies and provide future regulatory pathways. The study uses a conceptual and mixed-method approach, combining qualitative and quantitative content analysis of 59 peer-reviewed articles selected through the PRISMA framework. Findings reveal that regulation is primarily driven by concerns over consumer protection, financial stability, anti-money laundering, taxation, and environmental sustainability. Regulatory responses vary widely, ranging from the harmonized MiCA framework in the EU to the fragmented enforcement model in the U.S., along with diverse strategies across Asia. Stablecoins, DeFi, and CBDCs emerge as major regulatory frontiers. The study recommends adopting regulatory sandboxes, promoting international coordination, enforcing environmental standards, and building regulatory capacity in emerging economies to balance innovation with risk mitigation. It also highlights the importance of industry self-regulation and technology-assisted compliance in decentralized finance. The limitation of this study is that it relies solely on secondary data sources, which may limit the accuracy of real-time policy impact assessments. Future research should focus on empirical validation and dynamic policy modeling to enhance global governance of digital assets.
In the modern era of digitalization, integration with blockchain and machine learning (ML) technologies is most important for improving applications in healthcare management and secure prediction analysis of health data. This research aims to develop a novel methodology for securely storing patient medical data and analyzing it for PCOS prediction. The main goals are to leverage Hyperledger Fabric for immutable, private data and to integrate Explainable Artificial Intelligence (XAI) techniques to enhance transparency in decision-making. The innovation of this study is the unique integration of blockchain technology with ML and XAI, solving critical issues of data security and model interpretability in healthcare. With the Caliper tool, the Hyperledger Fabric blockchain's performance is evaluated and enhanced. The suggested Explainable AI-based blockchain system for Polycystic Ovary Syndrome detection (EAIBS-PCOS) system demonstrates outstanding performance and records 98% accuracy, 100% precision, 98.04% recall, and a resultant F1-score of 99.01%. Such quantitative measures ensure the success of the proposed methodology in delivering dependable and intelligible predictions for PCOS diagnosis, therefore making a great addition to the literature while serving as a solid solution for healthcare applications in the near future.
Open access
Impact of AI and Big Data on Business and Society
FinTech, Crowdfunding, Digital Finance
Artificial Intelligence in Healthcare and Education