Abstract This study provides a comprehensive bibliometric analysis of FinTech research spanning from 1968 to 2025, using 2760 articles indexed in the Web of Science database. It aims to uncover major publication trends, core theoretical frameworks, emerging topics, and the intellectual structure of FinTech scholarship. Employing VOSviewer and Harzing’s Publish or Perish software, this study maps co-occurrence networks, citation structures, and thematic clusters. It analyzes document types, source distribution, geographical contributions, keyword evolution, and the top 10 most cited papers in FinTech literature. The analysis reveals a significant surge in FinTech research since 1968, driven by the growing impact of digital finance innovations. The top three countries contributing to FinTech publications are the USA, England, and China. Dominant publication outlets include the International Journal of Bank Marketing and the Journal of Financial Services Marketing. Key research themes have evolved across three distinct periods: early banking and innovation (1968–1999), customer satisfaction and trust (2000–2011), and bank performance and digital adoption (2012–2025). Emerging topics include blockchain, mobile banking, crowdfunding, and Internet banking. The Technology Acceptance Model (TAM), along with its extended versions (TAM2, TAM3, UTAUT), is identified as the foundational theoretical framework in this field. The co-citation and keyword cluster analysis confirm the centrality of trust, risk, satisfaction, and performance in shaping FinTech outcomes. These findings not only synthesize FinTech’s academic development but also inform future research by identifying intellectual gaps and high-impact trends. The study highlights the growing integration between FinTech and consumer behavior and calls for deeper exploration into regulatory, ethical, and cybersecurity issues affecting FinTech adoption. Beyond the banking sector, the thematic patterns uncovered particularly in areas such as blockchain-based supply chain finance, crowdfunding ecosystems, and AI-enabled embedded financial services signal substantial strategic implications for non-financial firms. These include enhanced liquidity management, decentralized capital access, and data-driven business model innovation across diverse industries such as manufacturing, retail, and digital commerce.
Scientific knowledge production is undergoing a dual transformation. On one front, Decentralized Science (DeSci) leverages blockchain-based infrastructures to reconfigure how research is funded, verified, and governed, disintermediating legacy gatekeepers through tokenized incentives and distributed provenance. On the other, Artificial Intelligence (AI) is automating core dimensions of science, from hypothesis generation to experimental execution and model validation. This paper introduces DeScAI, a theoretical framework that unifies these domains into a recursive, self-verifying epistemic system governed by autonomous agents operating within decentralized, trust-minimized networks. We present a five-stratum architecture for DeScAI, hypothesizing that its integration enables epistemic acceleration, pluralistic inquiry, and cryptographically auditable trust. Methods include a structured literature synthesis (2018–2025), conceptual modeling, and descriptive analysis of 14 projects. Three hypothetical trajectories for future empirical investigation are proposed concerning cycle-time compression, epistemic pluralism, and reproducibility amplification. We conclude that DeScAI is not speculative: its core components are already deployed. What remains is orchestration, stitching together decentralized ledgers, incentive protocols, self-sovereign scientific agents (SSA), and cryptographic infrastructures into a single, recursive system. If successful, DeScAI could radically reduce the latency between hypothesis and verification, reconfigure scientific legitimacy as a live, contestable signal, and transform the incentive structure of research itself.
The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions.
The use of Enterprise Data Warehouse (EDWs) has been experienced as the analytical backbone of risk management, financial reporting and regulatory reporting of the data in very regulated sectors like banking, insurance, and capital markets. They were based on batch-oriented Extract Transform Load (ETL) paradigms, tight coupled schema and monolithic governance models that are better suited to stability than agility. Nevertheless, the increasing regulatory complexity, impacts of the near-real time risk visibility requirements, and increasing cost of infrastructure have emanated inherent weaknesses of the legacy EDW architectures. At the same time, the emergence of hybrid cloud platforms, scalable object storage, distributed query engines, and workflow orchestration system has made it possible to make the paradigm shift toward Extract–Load–Transform (ELT), domain-driven data products, and decentralized ownership models. In spite of these developments, in numerous organizations, the pressure to modernize reporting pipes based on strong backward compatibility criteria, audit limitations and the operational risks of massive data migrations makes this a challenge. This paper gives a detailed blueprint of modernization in the process of moving the old EDW centric ETL architectures to the hybrid cloud ELT platforms to suit the risk, finance, and regulatory reporting. Its proposed solution integrates domain-driven data products and ELT pushdown transformations orchestrating control planes and explicit data contracts that is applied in an incremental fashion with a strangler pattern. The framework focuses on retrogressively compatible schemas, reconcilability determinacy, the rollback safety nets, and regulated cutover plans to provide continuous regulatory compliance. Using a well-organized migration roadmap, cost and performance metrics and an official risk register, the paper will show how organizations can shorten report delivery cycles, enhance service-level agreement (SLA) compliance and minimize the overall cost of ownership without sacrificing auditability and strict governance. The findings have shown that hybrid cloud ELT systems may cut the latency in report by more than 40%, cut compute expenditure by up to 35, and become much more responsive to regulatory cases without infection of information integrity or resilience.
This study conducts a scientometric analysis of global financial risk management research to map its intellectual structure, thematic trends, and collaboration networks over the period 2000–2025. Data were retrieved from the Scopus database using a comprehensive search strategy and analyzed with VOSviewer to visualize co-authorship patterns, country collaborations, keyword co-occurrences, thematic clusters, and temporal developments. The results indicate that risk management, risk assessment, and financial markets remain the most influential and frequently studied topics, while emerging themes such as sustainability, decentralized finance, cryptocurrency, and supply chain resilience reflect the field’s adaptation to evolving technological, economic, and environmental challenges. Collaboration analysis highlights the dominance of countries such as China, the United Kingdom, and India, alongside increasing participation from emerging economies. The study offers practical implications for policymakers and financial practitioners to align strategies with current research priorities, and theoretical contributions by identifying conceptual linkages and emerging research fronts. Limitations include reliance on a single database and the inherent biases of citation-based analysis.
Purpose: Enterprise Resource Planning (ERP) systems, such as SAP (Systems, Applications, and Products in Data Processing), are critical to modern enterprises, enabling the integration of core business functions and the management of essential data. Ensuring their availability, reliability, and adaptability is paramount, as disruptions can result in significant operational and financial consequences. Traditional Knowledge Management (KM) approaches emphasize the preservation of ERP-related knowledge but often lack responsiveness to emergent risks. This study introduces a novel framework grounded in the concept of antifragility—where systems grow stronger under stress—by simulating disruptions to enable continuous knowledge evolution and system adaptation. Methodology: A mixed-methods research design combines simulation-based inquiry with Design Science Research (DSR) to investigate antifragile KM within ERP environments. Artificial Intelligence (AI) tools are integrated into the KM system to analyse ERP failures, generate runbooks, and proactively manage recovery knowledge. Controlled simulations of kernel upgrades and failure scenarios—modelled on ITIL 4 incident typologies—serve as structured stressors to expose vulnerabilities. Lightweight LLMs, Retrieval-Augmented Generation (RAG) pipelines, and semantic search tools are employed to codify procedural knowledge and enhance the responsiveness of ERP operations. Findings: The results demonstrate that embedding antifragile principles into ERP KM improves organizational learning, responsiveness, and recovery capabilities. Transitioning from static knowledge repositories to dynamic, AI-enabled systems allows for autonomous decision-making, decentralized knowledge flow, and adaptive documentation. Each disruption becomes a learning event, reinforcing the resilience and self-improvement of the ERP knowledge ecosystem. Implications: Empirical insights suggest that AI-driven antifragile KM transforms ERP disruptions into opportunities for growth, rather than threats to stability. The proposed framework supports the development of systems that not only recover from failure but also become progressively more robust and adaptive through structured experimentation and continuous learning.
This chapter proposes a formal alternative to blockchain-based ledgers by reconstructing the logic of bilateral exchange relationships using projective geometry and categorical methods. We show that the normative identity of a financial contract can be faithfully embedded into a projective elliptic curve, yielding an algebraic structure isomorphic to double-entry bookkeeping. This geometric realization enables compositional transaction modeling through the elliptic group law and supports structured reasoning about contract compliance, reversibility, and balance. In contrast to distributed ledger technologies, which often fail to preserve bilateral symmetry and internal control logic, our framework enforces normative integrity by construction. We analyze the limitations of blockchain systems in supply chain transparency and auditing and present a category-theoretic model that resolves these deficiencies through local contract verification and structured composition. The resulting framework extends naturally to multi-agent reasoning, tiered supply chains, and digital audit systems, offering a mathematically rigorous foundation for trustworthy and scalable accountability infrastructures.
Supply chain finance (SCF) plays a key role in easing financing difficulties for small and medium-sized enterprises, but it also comes with risks such as information asymmetry, fraud involving pledged assets, and delays in credit evaluation.In this study, we introduce a dynamic risk management framework driven by IoT and enhanced by the integration of multiple technologies.Built on a four-layer IoT structure, comprising perception, network, processing, and application layers, the framework combines blockchain for secure and trusted data sharing, federated learning for collaborative data processing, and digital twin models for real-time risk simulation.At the perception level, 5th-Generation Mobile Communication Technology (5G)enabled low-power sensors ensure comprehensive and tamper-proof data collection.The network layer uses blockchain techniques such as sharding and zero-knowledge proofs to safeguard data privacy and institutional trust.In the processing layer, federated learning combined with edge and cloud computing enhances credit evaluation.On the other hand, the application layer employs smart contracts and feedback mechanisms to enable real-time responses and adaptive risk strategies.To put this framework into practice, we propose a phased approach: first building a real-time data ecosystem, then deploying secure risk control systems, optimizing distributed computing, and finally integrating a closed-loop risk control mechanism.This modular, collaborative strategy ensures that technological systems align with actual business needs.Ultimately, the research demonstrates how IoT, blockchain, and AI can work together to create a scalable and practical model for managing risk dynamically in SCF.
Pri razvoju decentraliziranih aplikacij (dApps) se tradicionalni razvojni procesi pogosto izkažejo za nezadostne. Tovrstne rešitve zahtevajo večji poudarek na tehničnih, varnostnih in uporabniških vidikih kakovosti aplikacij, kot smo jih sicervajeni pri razvoju klasičnih rešitev. Ker je spreminjanje pametnih pogodb po namestitvi v omrežje verig blokov zahtevno oziroma nemogoče, sta temeljito testiranje ter presoja programske kode ključnega pomena za uspešen razvoj tovrstnih rešitev. Optimizacija stroškov goriva, nujna za izvrševanje programov v javnih omrežjih, predstavlja enega ključnih razvojnih izzivov, ki ga je potrebnoustrezno obravnavati. Poleg tega specifično okolje omrežij veriženja blokov zahteva ustrezne ukrepe za obvladovanje tveganj povezanih z ranljivostmi aplikacij in morebitnimi povezanimi finančnimi izgubami. Nespremenljivost, stroški goriva in zagotavljanje varnosti so le nekateri izmed ključnih razvojnih izzivov, ki jih je treba uspešno nasloviti pri izgradnji kakovostnih in stabilnih decentraliziranih aplikacij. Prispevek obravnava izzive, sodobne pristope in strategije razvoja decentraliziranih aplikacij ter podaja priporočila za njihov zanesljivejši in učinkovitejši razvoj, s čimer naslavlja ključne izzive uvajanja tehnologij veriženja blokov v industrijska okolja ter razvoja pametnih pogodb. Poseben poudarek je namenjen pametnim pogodbam, ki temeljijo na omrežju Ethereum.
<ns3:p>The emergence of Web 3.0 and the Metaverse marks a transformative shift in the evolution of the internet and digital ecosystems. This paper explores the foundational principles of decentralization, user autonomy, and data transparency that underpin Web 3.0 technologies, including blockchain, smart contracts, and digital wallets. We analyze how these innovations are reshaping business models, enabling new forms of value creation, and redefining digital ownership and governance. In parallel, we examine the Metaverse as a virtual, immersive environment integrating Web 3.0 infrastructure, and its potential to revolutionize sectors such as logistics, education, finance, and data management. The study also highlights the critical role of a holistic framework encompassing technological, economic, and legal pillars. A special focus is given to data provenance, privacy-preserving computation, and the need for coherent regulatory strategies in light of GDPR, the AI Act, and the Data Act (European Parliament, 2016; European Parliament, 2023; European Parliament, 2024). Finally, we identify emerging challenges related to NFT authenticity, system sustainability, and user experience, proposing a multidisciplinary and lean governance approach to guide future developments.</ns3:p>
Purpose: This article proposes and applies the 6V Framework to conceptualize and evaluate next-generation marketing channels in the digital economy.It aims to understand how emerging formats-such as voice commerce, immersive AR/VR environments, retail media networks, and Web3-based platforms-are reshaping customer engagement, brand experience, and value creation.Design/Methodology/Approach: Building on an extensive literature review and theoretical synthesis, the paper introduces the 6V Framework, consisting of six analytical dimensions: Value, Velocity, Visibility, Verifiability, Virtuality, and Vulnerability.The framework is applied to an in-depth case study of Nike .Swoosh, supported by a comparative evaluation of other leading platforms (e.g., Adidas, Gucci, Starbucks) to illustrate strategic patterns and innovation trajectories.Practical Implication: The article provides marketers, strategists, and digital transformation leaders with a practical framework for analyzing, designing, and governing complex marketing environments.It supports decision-making regarding channel investments, user experience design, and ethical risk management in data-rich, technology-driven contexts.Originality/Value: In contrast to legacy models focused on linear transactions and control, the 6V Framework captures the dynamic, participatory, and decentralized nature of modern marketing channels.It offers a novel conceptual lens for assessing strategic and operational implications of digital channel innovation.
This study investigates blockchain technologies and blockchain related researches from various sectors considering sectoral applications including food, healthcare, automotive, supply chain, information security, banking and quality management issues associated with these sectors. This study provides comparisons of various industries considering blockchain technology features. The aim of this study is to present an overview to intelligent quality management system based blockchain. This study examines standards for blockchain and distributed ledger technologies and discusses quality challenges for blockchain applications.
With the globalization of the software industry, requirements traceability has become increasingly critical in the software development process. However, the development of large-scale, complex software systems by cross-organizational research teams often faces challenges due to diverse organizational backgrounds, multi-site environments, conflicting objectives, and organizational boundaries. These factors can lead to trust issues, complicating the implementation of requirements traceability. To address these challenges, this study proposes a Smart Contract-Based Requirements Traceability (SCRT) framework. Smart contracts, which are executable code deployed on a blockchain, exhibit properties such as enforceability, tamper resistance, and verifiability. These characteristics empower the SCRT framework to enhance collaboration, communication, and trust among stakeholders while potentially improving the efficiency and quality of software development. Within the SCRT framework, a novel Requirements Traceability Information Model (RTIM) is introduced, which categorizes the links between new and existing artifacts. This model serves as a guide for the smart contract module, delineating which software artifacts to trace and the relationships to establish.
Ranjit Kannappan, Julien Hatin, E. Bertin, Noël Crespi
The Digital Product Passport (DPP) is a key enabler of the European Union’s vision for a circular economy. Achieving the full potential of DPP requires addressing the challenges of traditional product lifecycle systems (PLM). Traditional PLM focuses on streamlining data management and decision making. However, their centralized architecture limits transparent, crossorganizational collaboration, impacting the circular economy efforts. This paper proposes a blockchain based framework, tailored to support DPP implementation by enabling the creation and sharing of lifecycle data using digital twin technology. The proposed architecture implements two types of digital twins - Component Digital Twin and Product Digital Twin modeled using the Asset Administration Shell (AAS) standard to ensure interoperability. The architecture leverages Ethereum smart contracts for blockchain interaction and IPFS for off-chain decentralized storage. Two approaches for secure data sharing are implemented: Direct and Signature-based data sharing. Performance evaluation shows low latency for key operations like twin creation (167 ms) and data sharing (64 ms). By leveraging decentralization in DPPs, the proposed framework fosters collaboration, transparency, and circular economy practices, empowering stakeholders to access and share critical product data throughout the lifecycle.
Rocsana Bucea-Manea-Țoniş, Andrei Gabriel Antonescu, Constanța Mihăilă
Blockchain technology is reshaping the sports industry by enhancing transparency, data security, and fan engagement through applications such as smart contracts, tokenized sponsorships, and decentralized ticketing. This study investigates blockchain adoption in Romanian team sports, specifically football and basketball, through a comparative analysis based on a survey of 293 sports professionals (213 from football and 80 from basketball). Using structural equation modeling (SEM) with SmartPLS and cluster analysis in SPSS, the study explores the perceived benefits of blockchain and its relationship with athlete performance. The findings reveal distinct adoption patterns: football shows higher use of blockchain in ticketing and fan engagement, while basketball leads in performance analytics and financial support mechanisms. Statistically significant differences were confirmed through MANOVA, and clustering revealed varied stakeholder perceptions across professional roles. Benchmarking against sectors like finance and healthcare highlights transferable best practices for blockchain integration in sports.
The counterfeit medication infiltration within global supply chains poses a major public health threat. To address this, a collaborative effort among governments, regulators, and pharmaceutical companies is essential to secure the global/local supply chain. This paper proposes a novel approach that leverages blockchain technology, polymorphic encryption, and cloud storage to tackle security risks and privacy concerns in medication supply chains. The framework integrates a drug supply chain decentralized application (also called SCMapp) within the Ethereum blockchain, enabling functionalities like secure supplier onboarding, encrypted data management, cloud storage integration, and efficient data retrieval. This approach aims to revolutionize drug supply chain management by enhancing security, transparency, and overall efficiency, ensuring adherence to global health regulations. A safe and effective method for managing drug supply chains is provided by the suggested Drug Supply Chain Management System. The proposed model outperformed existing solutions in terms of security, efficiency, and traceability. The combination of encryption, blockchain, and cloud storage provided a comprehensive approach to address the challenges of drug supply chain management. The comparison analysis highlighted the unique advantages of the proposed model over other methods.
This research paper explores the extension of COBIT 2019 into a decentralized governance framework, specifically tailored for multi-finance companies in the fintech industry. The fintech sector faces rapid technological advancements, dynamic regulatory environments, and scaling cybersecurity threats; hence, conventional governance models often do not have the flexibility and scalability to cope with these challenges effectively. It addresses these lapses by proposing a framework for governance incorporating the use of decentralized autonomous organizations, blockchain technology for transparency, and artificial intelligence for predictive risk management. The model shall be endowed with smart contracts that guarantee enforcement of compliance in an automated manner, blockchain maintenance of an unalterable audit trail, and the use of AI in finding and mitigating emerging risks in real time. These innovations are also in tune with critical COBOT 2019 domains such as MEA02 (Auditability), APO12 for Risk Management, and DSS05 for Security Services, giving them an all-encompassing and adaptive governance approach. The testing of the proposed model demonstrates significant improvement in operational resilience, regulatory compliance, and stakeholder confidence, especially within high stakes fintech environments. These findings show that incorporating emergent technologies into COBIT 2019 provides added value in governance practices and positions a scalable and future-oriented solution to help navigate the complexities of the fintech sector. This research has contributed to the development of IT governance by showing how a decentralized and technology-integrated framework can transform governance practices in ensuring agility and security within multi-finance operations.
Cloud identity management has evolved from a purely technical concern into a fundamental pillar of digital society, creating profound impacts that extend far beyond organizational boundaries. Modern cloud-based identity and access management systems serve as critical infrastructure enabling access to essential services including healthcare, education, government benefits, and financial services. These systems incorporate advanced technical mechanisms such as multi-factor authentication, single sign-on, zero trust architecture, and artificial intelligence-driven fraud detection to establish secure and inclusive digital environments. The transformation to cloud-based architectures addresses traditional limitations of on-premises systems while introducing new capabilities for digital inclusion through device-agnostic authentication, accessibility-first design, and multilingual support. However, this evolution presents significant challenges including privacy concerns arising from data aggregation, potential government surveillance, and algorithmic bias in automated decision-making systems. Strategic implementation through public-private partnerships, investment in open source components, and adoption of emerging technologies such as quantum-resistant cryptography and distributed ledger integration shapes the societal impact of these systems. The technical decisions made in designing and implementing cloud identity infrastructure have far-reaching implications for social equity, democratic participation, and economic opportunity in an increasingly digital world.
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.
Ruba Islayem, Ahmad Musamih, Khaled Salah, Raja Jayaraman · 5 authors
Medical digital twins (MDTs) are rapidly emerging as transformative tools in healthcare. They provide virtual representations of medical devices and systems that facilitate real-time analysis and enhance decision-making. However, challenges such as secure data management, access control, and the lack of immersive and intelligent patient interactions limit their effectiveness. In this paper, we propose a solution integrating blockchain technology, Non-Fungible Tokens (NFTs), and Large Language Models (LLMs) within a metaverse environment to enhance MDT functionality. Blockchain and NFTs ensure secure ownership and access control, while the metaverse offers an engaging platform for user interaction. An LLM-powered non-player character (NPC) enables intelligent real-time user interactions and personalized insights. We develop two blockchain smart contracts for user registration, NFT ownership, and access control, and utilize decentralized InterPlanetary File System (IPFS) storage for the metaverse, MDT metadata, and interaction logs. We present the system architecture, sequence diagrams, and algorithms, along with the implementation and testing details. We conduct cost, security, and response time analyses to evaluate the smart contracts and LLM performance and compare our solution with existing approaches. We discuss practical implications, as well as challenges and limitations of the proposed solution. Finally, we explore the generalization of our system for various applications. The smart contract code and metaverse files are publicly available on GitHub.
Ameer Ahmed, Asjad Shahzad, Afshan Naseem, Shujaat Ali · 5 authors
Blockchain technology is widely used in almost every domain of life nowadays including healthcare sector. Although there are existing frameworks to govern healthcare data but they have certain limitations in effectiveness of data governance to ensure security and privacy. This study aimed to evaluate effectiveness of health care data governance frameworks, examining security and privacy concerns and limitations within the existing frameworks of ISO Standards, GDPR, and HIPAA. In this study quantitative research approach was followed. A sample of 250 participants from Islamabad, Lahore and Karachi based healthcare experts, IT specialist, blockchain research and developer, administrator was selected. The collected data was analyzed though frequencies and descriptive statistical tests with the help of SPSS. The results revealed un-satisfaction for data governance frameworks, i.e., ISO standards, GDPR, and HIPAA in terms of security concerns, i.e., data encryption, access controls, audit trails, interoperability and standards, smart contracts for compliance, data integrity, regulatory compliance monitoring and privacy concerns, i.e., consent management, anonymization and pseudonymization, data minimization. The participants agreed that there is a need of integration of reliable data governance framework in health care data management. Various personalized governance techniques, targeted security upgrades, and continuous improvement in the specific customized data governance framework has been presented based on the findings of the study. An implementation of blockchain-based systems is recommended in order to ensure and expand the security and privacy of healthcare data management.
The reliability and precision of stock market forecasting are of paramount importance to investors, regulatory authorities, and financial institutions.Traditional centralized systems for data processing and model deployment have been found to suffer from critical vulnerabilities, including susceptibility to tampering, single points of failure, and a lack of verifiability.To address these limitations, a novel hybrid framework has been developed that integrates advanced deep learning models with decentralized blockchain infrastructure to ensure both predictive accuracy and data integrity in financial time series forecasting.Temporal dependencies in market dynamics are captured through the use of recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures, which have been extensively trained to model non-linear and non-stationary behaviors in high-frequency financial data.In parallel, a private Ethereum-based blockchain has been deployed to record cryptographic hashes of input datasets, model parameters, and forecasting outputs, thereby ensuring transparency, auditability, and immutability across the data lifecycle.To enable computational scalability, deep learning operations have been executed off-chain, while on-chain mechanisms are utilized for secure checkpointing and traceability.Empirical validation has been conducted using real-time data from the Borsa stanbul (BIST), demonstrating significant improvements in forecasting accuracy when compared with baseline statistical and machine learning (ML) models.Moreover, the integration of blockchain technology has enabled a verifiable audit trail for all predictive operations, enhancing trust in the data pipeline without compromising computational efficiency.The proposed framework represents a significant advancement towards secure, transparent, and trustworthy artificial intelligence (AI) in financial forecasting, with potential implications for the broader decentralized finance (DeFi) ecosystem and regulatory-compliant AI deployments in capital markets.
Financial institutions increasingly rely on sophisticated database architectures to gain competitive advantages in high-frequency trading and analytics environments. This article examines optimal database technologies for financial applications, comparing in-memory, columnar, time-series, and distributed ledger architectures across standardized financial workloads. Multiple case studies demonstrate how different architectures excel in specific contexts: in-memory processing delivers superior performance for order processing, columnar storage enables faster analytical queries for market analysis, while time-series databases efficiently handle pattern recognition for fraud detection. Performance bottlenecks, consistency trade-offs, regulatory compliance challenges, and security considerations are explored in depth. The results indicate that no single architecture provides optimal performance across all financial application requirements; instead, financial institutions must select technologies based on specific use cases, with heterogeneous architectures often delivering superior results. The article concludes by examining emerging technologies with potential to transform financial database landscapes, including persistent memory, hardware acceleration, specialized indexing structures, AI-integrated engines, and hybrid blockchain solutions.