This paper discusses the obstacles to the capitalization of data elements, such as the difficulties in confirming data ownership, trust deficit, privacy breaches, and inefficiency of transactions, through a distributed solution based on blockchain technology. First, a data ownership confirmation mechanism based on a consortium blockchain is established by using the Merkle tree and PBFT (Practical Byzantine Fault Tolerance) consensus algorithm to achieve transparency and traceability of data ownership. Second, a multi-dimensional data value evaluation and RF-BP (Random Forest-Back Propagation) dynamic pricing mechanism are established by using machine learning algorithms to evaluate the value of data assets in a scientific manner. Third, a smart contract is established for pricing and payment, in order to achieve transaction automation and clearing and settlement. Finally, ZKP (Zero-Knowledge Proof) technology is applied to develop a mechanism for verifying compliance and privacy of data under the proposition of public review and "visible, invisible". Experimental results show that the proposed method reduces the average leakage risk and defense success rate under various attacks to 8.57 % and 97.1%, respectively. In terms of transaction efficiency, the proposed method achieves a throughput of 1250 TPS (Transactions Per Second) with a latency of 120 milliseconds at a 50-node scale. Overall performance is demonstrated with a confirmation and transaction success rate of 99.2% and 97.8%, respectively. The suggested framework provides reliable confirmation of data elements, scientific pricing, efficient trading and transaction processes, and privacy protection. It can support the vision of developing a secure, transparent and efficient market for data element circulation with technical feasibility and performance.
The convergence of blockchain and financial technology (FinTech) is changing the face of finance globally by offering safe, transparent, and affordable services to serve underserved groups of people. The study provides a systematic literature review, covering Payments, Asset Management, Financial Inclusion, and Other Innovations. A bibliometric analysis has the annual publication tendencies indicates the tendency of the increasing academic interest, according to a steep rise of eight articles in 2020 to 35 articles in 2025. The areas of research in Asia and the large journals (Sustainable Finance and World Sustainability Series) support the propagation of knowledge. The analysis of citations demonstrates the work that was foundational in the field of decentralized finance and the AIFinTech symbiosis. The thematic mapping of FinTech and blockchain also points to these two themes as the most important ones, with recent developments of interest in digital identity and regulatory compliance. The international system of cooperation revolves around India, with major collaborations occurring across the continent. These findings can provide researchers and practitioners a mechanism overview of current research dynamics and thematic developments, unlock the inclusive digital finance through blockchain-based Fintech innovations.
Md Imran Khan, Ahmad Raza, Abdulrahman Alomair, Abdulaziz S. Al Naim
This study provides a comprehensive contribution to the current understanding of blockchain technology and non-fungible token (NFTs). Blockchain technology is a revolutionary data storage and management tool that records data shared across a network of computers globally, making it safe, transparent, and decentralized. Non-fungible token is a specific type of token built on a blockchain, enabling the authentication of digital assets and safeguarding them against copying or fabrication. The research employed information on 3760 abstract data collected for the period from January 1, 2017, to June 03, 2025. The search criteria for data retrieval are based on the following keywords: “blockchain”, “non-fungible token”, and “token”. The data sources are Scopus and Web of Science. The study highlights the multi-dimensional and evolving discussion around blockchain and NFTs, including elements of technology, security, money, digital rights, and decentralization. The Wordcloud indicates a strong and growing innovation ecosystem, proposing new study avenues in trust mechanisms, smart contract development, and tokenized economies. The correlation graph visualizes that AI, data, finance, and blockchain show their mutual dependence has revolutionized our view of autonomy and governance. The study highlights authors who actively research blockchain and NFTs, as well as correlations between them. China leads the way in this research area and USA leads in terms of citation. The study’s finding informs evidence-based decision-making regarding the regulation and governance of blockchain and NFT technologies. For industry practitioners, the study’s insights can guide the development of innovative applications and solutions leveraging blockchain and NFTs. By examining a vast dataset of academic papers, the inquiry illuminates the key themes, emerging trends, and potential research gaps within this rapidly evolving field.
This study addresses key challenges in tax governance for multinational enterprises, including data silos, delayed risk identification, and insufficient privacy protection. It proposes a collaborative governance framework that integrates blockchain-based smart contracts and multimodal machine learning, termed the Blockchain-Enhanced Multimodal Risk Assessment Framework. The framework employs a dual-layer architecture that combines blockchain technology with federated feature engineering. At the data processing layer, it utilizes a multi-chain coordination mechanism based on Adaptive Proof of Stake, enhanced by a node reputation-based dynamic evaluation algorithm to significantly improve consensus efficiency and data transmission security. At the risk prediction layer, it integrates temporal dependency mining with heterogeneous graph neural networks, applying a composite embedding method to extract 89 highly discriminative features from an initial 1,423-dimensional feature space.
Blockchain, artificial intelligence (AI), and the Internet of Things (IoT) are becoming more connected, creating new opportunities for building smarter, more secure, and automated systems. One area where this combination can be really helpful is environmental monitoring. In this paper, we present a smart contract system that uses a trained machine learning model to predict air quality based on real-time data and runs on the IOTA blockchain. The goal is to make smart contracts more intelligent by enabling them to make predictions and send alerts without needing a centralized server. The model was trained off-chain and then integrated into a smart contract deployed on the IOTA Wasp chain. The system was evaluated using standard regression metrics like the R2score, MAE, and MSE. The results show that the model fits the data well and the whole system runs efficiently, making it suitable for real-time use in IoT environments.
This research uses an Ethereum blockchain dataset that contains transactional data, metadata, registry logs, payments and invoices to investigate how Extreme Gradient Boosting (XGBOOST) and Merkle Tree Blockchain can be integrated to optimize Supply Chain Finance (SCF) operations. This will improve SCF processes by guaranteeing data integrity, transparency and security. In order to forecast monetary flows and effectively detect the anomalies, the researchers use a robust method that begins with preprocessing using One-Hot Encoding. After the preprocessing step, the Feature Extraction takes place and is done by Independent Component Analysis (ICA) to identify independent components from the dataset. Then the optimization is done by XGBOOST. Moreover, by comparing the Merkle Tree Blockchain method with the existing Practical Byzantine Fault Tolerance (PBFT), the proposed Merkle Tree Blockchain guarantees safe encoding, decoding and hashing processes while drastically lowering latency and raising throughput that improves the system’s overall efficiency. Furthermore, a robust SCF architecture is supported by network performance monitoring that guarantees scalability, low latency and high throughput. The proposed XGBOOST technique outperforms the current techniques in financial forecasting and fraud detection, reaching 99.96% accuracy, 99.05% precision, 98.61% recall and 99.54% of the F1-score. By enhancing the cash flow, this integration ensures sustainability and operational efficiency by fostering collaboration and trust among the supply chain partners. Thus, this research demonstrates the revolutionary potential of blockchain technology and powerful Machine Learning (ML) in transforming SCF operation by providing a more secure, transparent and effective way to manage financial transactions.
This research suggests a groundbreaking methodology for the optimization of Supply Chain Finance (SCF) by integrating Deep Belief Networks (DBN) and Proof of Stake (PoS) blockchain techniques. The research collects business details, financial tactics, and supplier information from the Kaggle dataset. This paper uses Tokenization for the process of preprocessing, and it uses Locally Linear Embedding (LLE) method to reduce the dimensionality and in the protection of limited structures. Then, the optimization is done by DBNs, which will improve the method’s accuracy, whereas the PoS secures the data with encryption, decryption, and hashing. Moreover, the method’s effectiveness is evaluated using performance analyses such as computational time, efficiency ratio, error ratio, data authentication, and data management ratio. The proposed DBN-PoS is then compared with some existing methods like Self-adaptive Tasmanian Devil Optimization (SA-TDO), Federated Learning (FL), and Deep Convolutional Neural Network (Deep CNN) to provide a higher accuracy of about 100% and F1-Score of about 99.90%. Furthermore, blockchain integration improves transparency, protects the details of transactions, and safeguards from fraud actions using the SCF system. This research addresses progressing SCF optimization by integrating AI and Blockchain, providing a climbable, well-organized, and protected resolution for real-world entities.
Chang Su, Jun Deng, Xiaoyang Li, Jiayi Ma · 7 authors
Purpose Urban public safety is increasingly important and engineering safety risk monitoring is a key link in this area, where information asymmetry exists. Given the technological advantages of blockchain, it is of great significance for engineering safety supervision. This study aims to reveal the behavioral evolution patterns of multiple entities in the process of engineering safety risk monitoring under blockchain technology and to discuss the mechanism of action of blockchain technology on engineering safety risk monitoring. Design/methodology/approach By integrating prospect theory and mental accounting, a game model involving construction companies, third-party monitoring units and government regulatory departments under blockchain technology is established. With numerical analysis, a comparative analysis is conducted before and after the introduction of blockchain technology. Findings The results show that under the influence of complexity factors, blockchain technology effectively resolves the problem of information asymmetry and creates a stricter regulatory environment. The introduction of blockchain technology changes the strategic preferences and decision-making speed of the game participants, thereby enhancing the effectiveness and efficiency of engineering safety risk monitoring. In addition, countermeasure suggestions, marginal contributions and future prospects are also presented. Originality/value The possible marginal contributions of this paper are mainly in three aspects: First, it expands previous studies on decision-making behavior in engineering safety risk monitoring, constructs an evolutionary game model among construction enterprises, third-party monitoring institutions and government regulatory departments under blockchain technology and analyzes the stability of different strategies. Second, it uses prospect theory and mental accounting to depict the psychological motivations and subjective emotions behind the decision-making behavior of construction enterprises, third-party monitoring institutions and government regulatory departments, profoundly showing the evolutionary trends of multi-agent decision-making behavior under blockchain. Third, it establishes game analysis models, compares the differences in multi-agent decision-making behavior before and after the introduction of blockchain and further proposes countermeasure suggestions.
Blockchain technology has emerged as a transformative force across various industries, including the energy sector. Energy Service Companies (ESCOs) play a crucial role in promoting energy efficiency and sustainability. This paper explores the potential impact of blockchain technology on ESCOs, focusing on how decentralized finance (DeFi) and blockchain can enhance operational efficiency, transparency, and trust in energy transactions. By leveraging blockchain, ESCOs can streamline energy audits, improve contract management, and facilitate peer-to-peer energy trading. This paper also discusses the challenges and opportunities associated with the adoption of blockchain technology in the ESCO sector, providing a comprehensive analysis of its potential to revolutionize energy services.
Abstract In digital higher education, digital transformation is mandatory. Blockchain technology, with its unique features of distributed ledgers, consensus mechanisms, smart contracts, and traceability, provides a new perspective for digital educational resource platforms. In this study, a blockchain-based design was proposed for an open service platform for digital education resources in universities. The platform addresses challenges such as limited openness, complex resource copyright certification, and difficulty in effectively ensuring resource security and quality. The platform offers resource publishing, resource trading, operation management, and interface management ensuring data security using the distributed ledger of blockchain. Consensus mechanisms and smart contracts are used to ensure fairness and efficiency in platform operation and automate resource transactions. Traceability is utilized to ensure the certification and protection of resource copyrights.
As global economic integration deepens, supply chain finance plays a crucial role in optimizing corporate cash flow and promoting the coordinated development of industrial chains. However, issues such as information asymmetry and credit assessment difficulties in traditional models have hindered its growth. Blockchain technology, with its decentralized nature, data immutability, and automated smart contracts, offers innovative solutions for credit risk management in supply chain finance. This article systematically analyzes the application logic, typical scenarios, and implementation effects of blockchain technology in credit risk management within supply chain finance. It also explores the technical bottlenecks, regulatory challenges, and coordination issues faced by the practical implementation of these technologies, and propose targeted optimization strategies. The aim is to provide theoretical support and practical references for the deep integration of blockchain technology with supply chain finance.
Currently, urban renewal projects primarily rely on traditional financing methods such as government fiscal support, bank loans, and corporate self-funding. However, these models exhibit significant limitations. As urban renewal progresses towards high-quality development, there is an urgent need to explore innovative financing m echanisms t hat o ffer g reater efficiency, transparency, and sustainability. In this context, the introduction of blockchain technology presents a novel breakthrough for urban renewal financing. The decentralized, immutable, and smart contract-enabled features of blockchain can optimize capital flow, e nhance c redit s ystems, reduce fi nancing co sts, an d attract more social capital participation. By leveraging blockchain and Interplanetary file s ystem ( IPFS) t echnologies, i t i s p ossible to achieve full traceability of fund flows among government entities, financial institutions, enterprises, and investors, thereby reducing information asymmetry and strengthening mutual trust. Real-time on-chain recording of fund utilization ensures dedicated use of funds, prevents misappropriation or abuse, and improves the efficiency o f b oth fi scal re sources an d so cial ca pital. Moving forward, it is essential to integrate policy support with technological pilots to gradually construct a "blockchain + urban renewal" digital financial i nfrastructure, p roviding n ew m omentum for high-quality urban development.
Rural and underprivileged areas continue to grapple with the availability of affordable and dependable healthcare funding.Community based health insurance mechanisms are rife with inefficiencies including long delays in claim processing, fraudulent reporting, lack of transparency, and weak guardrails.This paper suggests a smart village-focused blockchain-based system on community health insurance.The platform is built on top of permissioned blockchain, smart contracts, and decentralized identity management to secure policy issuance, transparent premium collection, automated claims settlement, and community-based network governance.A prototype was implemented based on Hyperledger Fabric with off-chain storage of medical data and a mobile app for rural access.A simulation study was implemented to evaluate the performance of the proposed framework on a synthetic claim data, which have shown to significantly decrease the claim processing time, increase the rate of fraud detection, and raise the trust perception by all the involved users.Contribution -The proposed study aims to contribute towards designing and implementing (blockchain-enabled) healthcare financing models to pave the way for the promotion of sustainable healthcare for equitable benefits in rural smart village ecosystems.
K. Nirmala Devi, Lakshmi Narasimha, N. Sujatha, Y. Geetha · 6 authors
The integration of blockchain technology into financial markets has sparked significant scholarly interest, particularly in the context of stock market prediction. This bibliometric analysis aims to provide a comprehensive overview of research trends, influential publications, and emerging themes within this interdisciplinary domain from 2018 to 2025. Drawing data from Scopus the study utilizes bibliometric tools such as Biblioshiny and VOSviewer to analyse publication outputs, citation patterns, co-authorship networks, and keyword co-occurrence. The findings reveal a consistent growth in academic contributions, especially after 2019, reflecting blockchain’s increasing relevance in financial prediction and its convergence with machine learning, deep learning, and artificial intelligence. Key research clusters identified include algorithmic trading, decentralized finance (DeFi), cryptographic modelling, and predictive analytics. The analysis also highlights leading journals, authors, and institutions contributing to the advancement of this field. However, certain limitations are acknowledged. The focus on selected databases may have excluded valuable contributions from platforms such as IEEE Xplore, SSRN, or non-indexed proceedings. Additionally, the keyword-based search strategy may have overlooked studies using alternative terminologies. The temporal scope may also bias the analysis toward recent developments while underrepresenting foundational research. The study offers a valuable reference point for scholars and practitioners, mapping the intellectual structure and thematic progression of blockchain-based stock prediction research. Future studies are encouraged to adopt multi-database approaches, combine quantitative and qualitative methods, and explore regulatory and regional variations to enrich understanding and guide practical implementation.
Blockchain technology(BCT) offers transformative opportunities, particularly in cross-border trade finance ($T_{F}$) and securities settlement in capital markets. Conventional capital markets face challenges such as high operational costs, lengthy settlement times, and susceptibility to fraud. This paper explores how the decentralized ledger and smart contract system of BCT can address these challenges. By examining key technological components such as consensus mechanisms, network design, and transaction validation methods, this study highlights blockchain's potential to streamline processes, reduce intermediaries, and ensure immutable record-keeping. The research also examines the impact of blockchain in securities settlement, focusing on atomic settlement models that eliminate counter-party risks and enhance liquidity management. Through a comprehensive analysis of use cases and implementation strategies, this research underscores blockchain's role in revolutionizing capital markets. The findings suggest that blockchain adoption can drive significant improvements in cross-border transactions, fostering a more resilient and efficient financial ecosystem. Future research directions include exploring interoperability challenges and regulatory considerations for broader adoption.
Against the backdrop of integrating the dual carbon strategy with the digital economy, retail enterprises' green supply chains face challenges such as difficult-to-trace carbon emission data, low efficiency in low-carbon collaboration, and imperfect green supplier certification mechanisms. This study leverages blockchain technology to empower retail enterprises, advancing their supply chains toward low-carbon and green development pathways. By utilizing distributed ledgers, it enables real-time sharing and traceability of carbon data across the entire chain, addressing issues of chaotic and distorted data collection. leveraging smart contracts to predefine emission reduction rules and allocate benefits, thereby balancing divergent objectives among supply chain participants to enhance collaborative efficiency; utilizing consensus mechanisms and immutability to establish a transparent, traceable green supplier certification and dynamic oversight system, tackling certification fraud and regulatory loopholes. This provides a feasible solution for the green and low-carbon transformation of retail enterprises, supporting their journey toward sustainable development.
T Devi., Saef Thallal, N. Srinivasan, G. Durgadevi · 5 authors
Currently, the Medical Supply Chain (MSC) involves production and distribution of medical products which requires precise tracking, authentication, and coordination among multiple stakeholders. Blockchain (BC) technology provides a decentralized ledger to improve transparency and trust. However, existing BC-based models are unable to monitor environmental conditions because of the lack of real-time integration with Internet of Things (IoT) sensors, which limits their ability to detect and respond to violations proactively. Hence, this research proposes a BC and IoT-based Smart Contract Model (BCIoT-SCM) that allows real-time tracking of critical parameters for MSC. Initially, the secure medicine is registered onto the BC, and then a QR code is generated for digital-physical linkage. Then, IoT sensors are used to monitor the conditions during shipment, where the SCs validate these sensor readings against preset thresholds by triggering alerts when violations occur. Further, the verification is enabled by stakeholders through QR code scanning by displaying the product history, and this process concludes with consensus-driven validation as well as anomaly logging. The proposed BCIoT-SCM achieved better results in terms of throughput$(130 \text{Tps})$than the existing multilayered BC framework.
Many industries, including banking, government, energy, healthcare, etc., have taken an interest in blockchain technology since its inception in the last decade. A comprehensive overview of blockchain's potential uses in the healthcare industry is provided in this article. Research in this field is indeed progressing at a breakneck pace. Thus, we have discovered several cutting-edge applications of blockchain technology, such as medicine supply chain management, electronic medical record sharing, remote patient monitoring, etc. We have also highlighted the shortcomings of the methods that have been examined, and we have wrapped off by delving into some unanswered questions and potential spots for more study. The new Internet of Things applications in Industry 4.0 include blockchain technology, which is immutable, cryptographically secure, distributed ledgers, and a component of decentralized systems. Various entities or parties maintain and distribute exact copies of a succession of transaction lists using this technology. Blockchain technology's capacity to link disparate systems and improve the accuracy of electronic health data, together with a patient-centric approach to healthcare, make it an area with enormous promise.
Ali Fakhri Abbas, Mustafa Radif, Haider Mahdi Rammoo, Salah Mahdi Saleh Alkhafaji · 6 authors
The rapid digitalization of financial services has made FinTech loan processing highly susceptible to security threats and inefficiencies. An innovative contract-based blockchain framework is proposed to ensure secure, transparent, and automated loan processing. Current FinTech loan systems often rely on centralized architectures, leading to risks such as data breaches, fraud, lack of transparency, and delayed loan disbursement due to manual verifications. These issues hinder trust and operational efficiency in digital lending platforms. To overcome these limitations, the proposed framework employs the Blockchain-Based Layered Framework (B-BLF), which provides a structured approach to develop and evaluate technological artifacts. Through iterative problem identification, design, and evaluation phases, B-BLF ensures the solution is both innovative and effective. The smart contract-based solution uses the Ethereum blockchain to automate loan approval, disbursement, and repayment processes. It validates borrower information, executes contractual terms autonomously, and stores transactions immutably, thereby reducing human intervention and operational risks. The findings reveal that the proposed method enhances data integrity of 97.2%, speeds up loan approval of 95.6% and disbursement, reduces fraud, and improves user trust over 98.2% in FinTech services. The framework proves to be a scalable and secure solution for modernizing financial loan systems.