Mailson Teles-Borges, Rafael Z. Frantz, José Bocanegra, Sandro Sawicki · 5 authors
Smart cities take advantage of digital services to enhance citizens’ experiences. Integration processes facilitate interactions between these services, providing or improving functionalities. The integration can operate under specific restrictions, which can be represented through smart contracts deployed in a blockchain. Monitoring systems track interactions between integration processes and digital services by recording events from communication ports. In this paper, we argue that current monitoring tools lack the ability to observe these ports or invoke smart contracts. We propose a monitoring system to track integration processes, capture port-reported events, and invoke smart contracts on a blockchain platform.
This research examines the risk profiles of XRPUSD and ADAUSD cryptocurrencies through Value at Risk (VaR) analysis with Monte Carlo simulation, providing quantitative risk assessments for both individual assets and a diversified portfolio. Analyzing historical price data from January 2016 to November 2024, the study identifies distinctive risk characteristics between these cryptocurrencies: ADAUSD exhibited marginally higher historical returns (1.44% monthly) compared to XRPUSD (1.42%), but with notably higher volatility (standard deviation of 5.41% versus 4.65%). The Monte Carlo simulation with 1,000 iterations generated VaR estimates at multiple confidence levels, revealing that XRPUSD consistently demonstrated lower downside risk than ADAUSD across all confidence thresholds. At the 99% confidence level, ADAUSD showed a Mean VaR of -10.97%, indicating potential monthly losses exceeding $10.97 million on a hypothetical $100 million investment, while XRPUSD's lower Mean VaR of -9.52% translated to potential losses of approximately $9.52 million. The most striking finding emerged from the portfolio analysis, which revealed dramatic risk reduction through diversification—the equally-weighted portfolio achieved a Mean VaR of merely -2.22% at the 99% confidence level, representing an approximately 80% reduction in potential losses compared to ADAUSD alone. These results demonstrate that cryptocurrency diversification can substantially mitigate extreme downside risk while maintaining exposure to the digital asset class. The significant risk reduction achieved through a simple two-asset allocation validates the application of modern portfolio theory principles to cryptocurrency investments despite their unique characteristics and underscores the critical importance of diversified approaches rather than concentrated positions for risk-conscious cryptocurrency investors. This research contributes to both theoretical understanding of cryptocurrency risk dynamics and practical portfolio construction approaches, providing quantitative evidence for the value of diversification strategies in navigating the substantial volatility inherent in digital asset markets.
Purpose The integration of blockchain technology and artificial intelligence (AI) is reshaping the financial services industry, offering transformative solutions in areas such as risk management, fraud detection, regulatory compliance and operational efficiency. Design/methodology/approach This paper presents a systematic literature review of over 100 peer-reviewed studies published between 2020 and 2024, analyzing the benefits, challenges and future directions of blockchain-AI applications in financial services. Our findings reveal that while blockchain enhances data integrity, security and transparency, AI drives predictive analytics, automation and decision-making efficiency. Findings The synergy of these technologies holds significant potential yet faces critical challenges related to scalability, interoperability, regulatory compliance and ethical AI governance. We identify key research gaps, including the lack of standardized regulatory frameworks, limited real-world case studies and technical barriers to integration. To address these gaps, we propose a comprehensive theoretical framework linking technological advancements to regulatory and ethical considerations. This study contributes to both academic discourse and industry practice, offering actionable insights for financial institutions, technology developers and policymakers navigating the rapidly evolving FinTech landscape. Research limitations/implications The rapidly evolving nature of blockchain and AI technologies may limit the long-term applicability of some findings. The study primarily focuses on published academic literature, potentially overlooking some industry-specific developments. Future research should address the identified gaps, particularly in cross-chain interoperability, ethical AI frameworks, and long-term economic impacts. Empirical studies and case analyses could further validate the theoretical insights presented in this review. Originality/value This study provides a novel, comprehensive synthesis of blockchain and AI applications in financial services, offering valuable insights for both academics and practitioners. By critically examining the synergies and challenges of these technologies, it presents a unique perspective on their transformative potential in FinTech. The proposed research agenda addresses crucial gaps in current knowledge, guiding future investigations. The findings contribute to a deeper understanding of the complex interplay between technological innovation, regulatory frameworks and ethical considerations in the evolving landscape of financial services.
The evolution of supply chain finance has entered a transformative phase driven by data analytics, artificial intelligence, and digital technologies. This article examines how these innovations are reshaping financial relationships between buyers, suppliers, and financial institutions in supply chains worldwide. It explores the core data technologies revolutionizing supply chain finance, including ERP integration, advanced analytics, and automated financing platforms. The article further investigates how artificial intelligence and machine learning applications enhance credit risk assessment, enable dynamic pricing models, improve fraud detection, and streamline document processing through natural language processing. Additionally, it analyzes the impact of blockchain and distributed ledger technologies in automating payments through smart contracts, expanding access to financing through tokenization, and providing end-to-end traceability. While highlighting the significant benefits of these technologies, the article also addresses implementation challenges related to data quality, system integration, and change management requirements, offering insights for organizations seeking to optimize their supply chain finance operations in an increasingly digital ecosystem.
Purpose This study aims to analyse the academic literature on cryptocurrency regulation using a combined bibliometric and systematic literature review approach, focusing on research trends, influential contributions and thematic clusters from 2018 to 2024. Design/methodology/approach This study reviews 62 journal articles published between 2018 and 2024. A combined bibliometric and systematic literature review approach is used to analyse key articles, journals, authors and countries contributing to the field. Thematic clusters such as crowdfunding, FinTech, blockchain vulnerabilities, Central Bank Digital Currencies (CBDCs) and economic forecasting in developing countries are identified. Findings The analysis reveals emerging trends and significant advancements in cryptocurrency regulation, highlighting key contributors in the field. Thematic clusters show a focus on blockchain vulnerabilities, the rise of CBDCs, and regulatory challenges in developing economies. These themes represent the most pressing areas in cryptocurrency market regulation. Practical implications The findings offer insights for policymakers, researchers and industry practitioners to shape effective regulatory frameworks, addressing critical issues such as blockchain security and central bank digital currencies. Social implications This research contributes to the development of robust regulatory frameworks, promoting market stability and transparency, which will ultimately benefit global financial markets and stakeholders in the cryptocurrency ecosystem. Originality/value To the best of the authors’ knowledge, this study is the first to integrate bibliometric and systematic literature review methods to examine cryptocurrency regulation, providing a comprehensive overview of the research landscape.
Mahmood A. Bazel, Fathey Mohammed, Mazida Ahmad, Abdullah O. Baarimah · 5 authors
Blockchain technology has gained significant attention in several sectors owing to its distributed ledger, decentralized nature, and cryptographic security. Despite its potential to reform the healthcare industry by providing a unified and secure system for health records, blockchain adoption remains limited. This study aimed to identify the factors influencing the intention to adopt blockchain in healthcare by focusing on healthcare providers. A theoretical model is proposed by integrating the Technological-Organizational-Environmental framework, Fit-Viability Model, and institutional theory. A quantitative approach was adopted and data were collected through an online survey of 199 hospitals to evaluate the model. The collected data were analysed using PLS-SEM. The results indicated that technology trust, information transparency, disintermediation, cost-effectiveness, top management support, organizational readiness, partner readiness, technology vendor support, fit, and viability significantly and positively influenced the intention to adopt blockchain-based Health Information Systems in hospitals. Conversely, coercive pressure from the government negatively affects adoption decisions. Moreover, the study found that the hospital ownership type did not moderate the relationship between the identified factors and blockchain adoption. This study provides valuable insights into the various factors that influence blockchain adoption in hospitals. The developed model offers guidelines for hospitals, blockchain providers, governments, and policymakers to devise strategies that promote implementation and encourage widespread adoption of blockchain in healthcare organizations.
Recently, Edge-Cloud Collaborative (ECC) has emerged as an efficient and promising technique to empower various computation-intensive applications in Digital Twin Network (DTN). The integration of ECC and DTN serves to bridge the gap between data analysis and physical states. In ECC, a reliable and optimal task offloading scheme is required to maximize resource utilization and provide satisfying services to End Users (EU). However, existing offloading schemes still face significant challenges, such as the instability and complexity of network topologies, the intricacies of massive data, and the lack of trust among EU. In this paper, we propose anenhancinGedge-clOud collaboraTion wiTh blockchain-assistEd digital twin intelligence offloadiNgscheme (GOTTEN) which transmits large-scale tasks generated by DTs to Edge Station (ES) or Cloud Station (CS) in dynamic DTN scenarios. We first formulate this resource allocation and task offloading problem and provide an appropriate initial solution which guarantees that tasks generated by DTs can be accurately mapped to physical entities, while optimizing block allocation and reducing the decision space of task offloading. Then, we employ the Lagrange Multiplier based Distributed Island model-enhanced Genetic Algorithm (LM-DIGA) to transform our formulated problem into a convex form and achieve an optimal resource allocation under a specific scheme. Additionally, our proposed architecture also leverages blockchain verification mechanisms to enhance system stability, strengthening privacy protection for DT data as well. Finally, extensive simulation results demonstrate that, compared with seven baselines, our proposed scheme achieves a 10 percent the total system delay and privacy overhead with regard to other schemes in ECC.
The rapid expansion of digital twin (DT) applications in industrial settings has created a pressing need for robust interoperability solutions, particularly for blockchainsupported DT teams operating across heterogeneous platforms. Current blockchain frameworks struggle with seamless data exchange and operational efficiency due to interoperability limitations. In this study, we introduce an innovative cross-chain framework that leverages a relay-chain mechanism and dynamic non-fungible tokens (NFTs) to overcome these challenges. Through experimental validation on Ethereum and Hyperledger Fabric, we demonstrate the framework's effectiveness in ensuring scalability, security, and cost efficiency. The evaluation reveals minimal network latency for cross-chain transactions with a negligible impact on overall throughput. The comparative analysis further highlights the advantages of our approach over existing cross-chain solutions, offering enhanced interoperability while maintaining low deployment costs. By addressing key barriers to blockchain deployment, this work provides a practical solution for improving cross-chain collaboration and advancing industrial DT integration. Future research will focus on refining relay-chain consensus mechanisms and extending support to private blockchains to enhance adoption in multi-stakeholder environments.
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.
Investment management firms face the critical challenge of maintaining accurate, timely, and consistent data across multiple business functions, with particular emphasis on two fundamental systems: the Investment Book of Record (IBOR) and the Accounting Book of Record (ABOR). While IBOR provides real-time investment intelligence for front-office decision-making, ABOR serves as the official financial record supporting regulatory compliance and reporting. The operational separation between these systems creates significant challenges including data reconciliation complexities, timing mismatches, valuation methodology differences, and corporate action processing issues. Leading firms are pursuing integration strategies such as unified data architecture, near real-time accounting, automated reconciliation tools, API-based integration, and cloud-based solutions. Emerging technologies including artificial intelligence, distributed ledger technology, and outsourcing models are reshaping how investment operations address the IBOR-ABOR divide. These innovations occur against a backdrop of increasing regulatory demands and expanding multi-asset class strategies, driving a transformation in investment data management.
The evolution of regulatory compliance in the financial sector has transformed enterprise data systems from basic record-keeping tools into critical strategic assets. Financial institutions face mounting regulatory requirements across jurisdictions, necessitating sophisticated technological solutions to ensure compliance while maintaining operational efficiency. This article explores how integrated regulatory reporting systems consolidate disparate data sources, real-time monitoring capabilities enable proactive compliance management, and data lake architectures provide comprehensive audit trails. It examines blockchain and distributed ledger technology's role in enhancing transparency and traceability across various compliance domains, including KYC/AML processes, securities settlement, trade finance, and cross-border payments. The article also addresses integration challenges through API-first architectures, data governance frameworks, regulatory change management, and cloud-based platforms. Finally, it explores emerging innovations such as AI-powered regulatory intelligence, predictive analytics, regulatory-as-a-service models, and cross-institutional compliance networks that represent the future of enterprise data systems in regulatory
Denis Shakhov, Руслан Вахаевич Баташев, Ilyоs Abdullayev
This study investigates the transformative impact of artificial intelligence on corporate communications, focusing on AI-powered personalization systems in business environments. Through a systematic literature review (2019-2024), the research establishes an empirical framework for evaluating these systems’ technological infrastructure. The findings reveal distinct sector-specific performance variations: the retail sector showing 58% enhanced engagement metrics, while B2B segments demonstrated 28% improvement in key performance indicators. The technological foundation comprises machine learning algorithms, natural language processing frameworks, and high-performance computing systems enabling real-time personalization. The methodology integrates the adaptive personalization framework (APF) with the multidimensional personalization model (MPM) to elucidate machine learning mechanisms. This framework supports user profiling, navigation optimization, and behavioral pattern modification, secured through distributed ledger technologies. Empirical analysis reveals the complementarity between AI and human capabilities. While AI systems excel in response velocity (mean: 4.92), human interactions demonstrate superior responsiveness (5.27) and professional competency metrics (5.32 vs. 4.87), suggesting the optimality of a hybrid model. The study culminates in a conceptual framework balancing communication scalability with personalized relevance while adhering to ethical imperatives of data protection, algorithmic fairness, and transparency protocol.
Abstract Realizing its overarching potential in achieving digital transformation, leading organizations, such as Air New Zealand, have recently employed blockchain technology to manage 3D printing innovation. Despite the potential benefits, including the key role of blockchain as a supporting technology in Industry 5.0, these applications of blockchain technology in 3D printing are still at a nascent stage in practice and mostly limited to producer innovation. Additionally, 3D printing and blockchain technologies are often independently revolutionizing value capture and value creation, whereas the theoretical implications of their combined impacts on innovation management remain an underexplored domain. Limited digital transformation efforts have been made to employ blockchain technology to create a synergy between producer innovation and community innovation in relation to 3D printing innovation. To solve these issues, we introduce a novel concept, distributed ledger innovation platform (DLIP), which refers to a network of firms and individuals that create and capture value on a blockchain through digital transformation efforts. In this research, we draw upon transaction cost and social production theories to investigate the potential benefits of blockchain to solve the digital transformation challenges associated with 3D printing innovation. We utilize these perspectives to propose the concept of DLIP as a new governance structure for managing digital transformation activities with blockchain. Additionally, we examine illustrative digital transformation efforts via 3D printing innovation and applications of blockchain technology. Then, we present future research directions for innovation management scholars to investigate the benefits of DLIP in digital transformation efforts via 3D printing. We conclude by discussing the potential applications of DLIP to manage other enabling technologies of digital transformation, including big data, artificial intelligence, and the metaverse.
This study critically reviews the literature on metaverse technologies, developing an integrative framework to explore their sector-specific implications and transformative impact on business management. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and machine learning-based BERTopic modeling, the study identifies nine key themes, reflecting the diverse ways augmented reality (AR), virtual reality (VR), extended reality (XR), digital twins, and decentralized finance (DeFi) influence industries. These themes include the metaverse as a tool for economic and environmental policy experiments, navigating financial risk and regulatory dynamics, adapting human resource development to VR-driven environments, Industry 4.0 applications of VR and digital twins, digital twin applications in manufacturing and supply chain optimization, AR and VR in digital marketing and customer experience, AR in enhancing retail and consumer experiences, exploring user interaction and affordances in the metaverse, and VR and AR in tourism experience and engagement. The framework highlights drivers, constraints, and cross-sector linkages, addressing practical challenges such as high implementation costs, regulatory uncertainties, interoperability barriers, cybersecurity risks, and ethical concerns surrounding data privacy and inclusion. The study critically evaluates contradictions in metaverse adoption, such as the tension between sustainability goals and energy-intensive technologies like blockchain, the gap between immersive training potential and workforce adaptation challenges, and the disparity between metaverse-driven economic models and real-world policy implementation hurdles. Research propositions suggest integrating metaverse technologies into business operations while balancing ethical dimensions, psychological impacts, cost limitations, and accessibility barriers. Additionally, the study advocates for expanding theoretical frameworks such as the Resource-Based View (RBV), Technology Acceptance Model (TAM), and experiential learning to account for the dynamic capabilities, risks, and industry-specific constraints of metaverse adoption. Policymakers and practitioners are encouraged to address regulatory and ethical challenges, sectoral disparities, and the unintended consequences of metaverse-driven digital transformation, ensuring operational efficiency, resilience, and consumer engagement while fostering sustainable and inclusive adoption. This research offers actionable insights for strategic implementation, interdisciplinary theoretical expansion, and ethical progress in business management.
Blockchain platform performance is critically important for financial transaction applications. This article presents a case study comparing Ethereum, a public blockchain, with Hyperledger Fabric, a permissioned blockchain, for modeling financial transactions. Key performance metrics evaluated include throughput, latency, transaction cost, and finality. Our findings show that the Ethereum network achieved approximately 15 transactions per second (TPS) with a latency of ~12 seconds and incurred transaction fees of a few U.S. dollars. In contrast, Hyperledger Fabric sustained ~2000 TPS with sub-second latency and negligible cost. Fabric’s deterministic consensus also provides near-instant finality (~1–2 s), contrasting with Ethereum’s probabilistic finality, which requires ~1 minute. The detailed empirical results in Figures and summary Tables comparing core metrics reveal that Hyperledger Fabric offers superior throughput and efficiency for enterprise financial scenarios, while Ethereum’s performance is constrained by its decentralized consensus overhead. All measurements are based on an internal case study deployment without simulation. These insights inform platform selection for financial applications requiring high transaction volume and low latency.
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.
Financial data analytics has become a critical tool for businesses seeking to drive growth, enhance fraud prevention, and mitigate risks in dynamic markets. By leveraging large datasets, advanced algorithms, and real-time analytics, organizations can make more informed financial decisions, improve operational efficiency, and enhance compliance with regulatory frameworks. This review explores how financial data analytics contributes to business growth by improving revenue forecasting, identifying market trends, and optimizing financial planning. Companies can leverage predictive models and artificial intelligence to gain competitive advantages through better risk assessment and investment decision-making. Fraud prevention is another key area where financial data analytics plays a transformative role. Machine learning algorithms, anomaly detection systems, and real-time transaction monitoring help identify and prevent fraudulent activities before they cause significant financial losses. Businesses and financial institutions can use automated risk-scoring models to strengthen security in banking, payments, and investment transactions. Risk mitigation in financial markets is also enhanced through data analytics. By employing predictive modeling, scenario analysis, and stress testing, businesses can assess potential market fluctuations and develop strategies to minimize financial exposure. Moreover, analytics-driven regulatory compliance mechanisms improve transparency and reporting, ensuring adherence to legal and industry standards. Despite its advantages, financial data analytics faces challenges such as data privacy concerns, integration with legacy systems, and the need for skilled professionals. However, emerging technologies, including blockchain, AI, and decentralized finance (DeFi), present new opportunities for strengthening financial security and business resilience. This review concludes that financial data analytics is a vital asset for modern businesses, offering strategic insights that drive profitability, enhance fraud detection, and strengthen risk management. Companies must continue to invest in data-driven solutions to stay competitive in an increasingly digital financial landscape. Keywords: Financial data, Business growth, Fraud prevention, Markets.
The chapter explores the dynamic and rapidly evolving landscape of financial technology (FinTech), highlighting key trends and innovations reshaping the financial services industry. This chapter delves into the transformative impact of technologies such as blockchain and decentralized finance (DeFi), emphasizing their role in driving efficiency, enhancing customer experiences, and fostering financial inclusion. Key trends discussed include the increasing adoption of blockchain technology and the growing importance of open banking. The chapter also examines the integration of FinTech solutions into traditional financial systems. These developments are contextualized within the broader framework of digital assets, illustrating how FinTech innovations are enabling new forms of value transfer, asset management, and financial intermediation. By linking FinTech trends with financial digital assets, this chapter underscores the symbiotic relationship between technological advancements and the digital asset ecosystem.
Abdullah Ayub Khan, Aftab Ahmed Shaikh, Asif Ali Laghari, Mazhar Ali Dootio · 9 authors
Agriculture is one of the vital factors in living, social, and economic stability. The current lifecycle of food safety is multifaceted and unsecured as more stakeholders are involved and sharing resources and related information. This poses serious issues for agriculture industries to provide food safety, transparency, industrial-related information integrity, reliability, trustworthiness in the supply chain, and food livestock against the evolving threats growing in Industry 4.0. This chapter bridges these divergences by enabling a mixture of the “Internet of Things” (IoT) and Blockchain-based novel and secure smart business agricultural transparent supply chain and forecasting architecture. Precision Business Agriculture is a blockchain-IoT-enabled FIWARE cloud-based platform to collect current and previous node transactions and deploy them to the hosts&s; analytics modules. In this proposed architecture, participating stakeholders create a public ledger (Ethereum) network to share and agree on distinct supply chain and business agriculture forecasting-related activities before preserving the IoT-Blockchain ledger. We have conceived and created smart (digital) contracts, implemented them using pseudo-algorithms, and represented them through sequence diagrams to handle stakeholders&s; interaction in the supply chain and forecasting process. This proposed solution delivers system integrity, provenance, transparency, preservation, and a robust security mechanism to store immutable agriculture-business-related information in a permissionless hash (SH-256) encrypted IoT-smart contract distributed ledger.
A decentralized autonomous organization (DAO) is a type of enterprise that operates on a decentralized structure where all members have equal contribution, right, and decision-making in the organization. This organization makes use of smart contracts, a software where the rules and policies of the organization are embedded. They are susceptible to threats and fraudulent activities which compromise their security. A flaw in the DAO’s smart contract could lead to exploitation by hackers. This research is aimed at developing a fraud detection system using machine learning models and evaluating the performance of the system using standard performance metrics. Machine learning algorithms were employed to detect frauds in the DAO platform built under the Ethereum blockchain using a dataset of transactions, consisting of fraudulent and non-fraudulent transactions. Algorithms employed were the logistic regression, XGBoost, and random forest. These models were built and trained, and hyper parameter tuning was carried out on them. The results obtained from the evaluation metrics show that random forest and XGBoost give better results when compared to logistic regression. Logistic regression had accuracy and precision of 82.07% and 55.53%. Random forest had 70 accuracy and precision of 98.52% and 96.49%. For XGBoost, its accuracy and precision are 98.12% are 93.33%. Other evaluation metrics were used in carrying out analysis, showing the best performing models, the random forest and the XGBoost. At the end of this research, a model for predicting threats in DAO was developed for the ecosystem. The developed system could be utilized in making predictions from past information like patterns of transactions and other available features, to ascertain if the account is fraudulent or not, that account can be terminated.
Amid growing global pressures to combat climate change, enterprises are reengineering their supply chains to align with carbon-neutral objectives while maintaining operational agility and competitiveness. This study explores the transformative potential of integrating Quantum Artificial Intelligence (QAI), Business Intelligence (BI), and autonomous decision-making technologies in building intelligent, sustainable supply chains capable of minimizing environmental impact. By leveraging the computational advantages of quantum algorithms, machine learning, real-time analytics, and decentralized control systems, organizations can address the increasing complexity of emissions management, logistics optimization, and sustainability forecasting. A comprehensive systematic literature review was conducted following the PRISMA 2020 guidelines, encompassing 97 peer-reviewed articles published between 2010 and 2024 across fields including supply chain management, artificial intelligence, quantum computing, and sustainability analytics. The review reveals that QAI significantly enhances the efficiency of solving combinatorial problems such as routing, scheduling, and emissions prediction, outperforming classical AI in both speed and scalability. BI platforms have evolved from retrospective reporting tools to intelligent systems that facilitate real-time carbon monitoring, dynamic scenario modeling, and sustainability-focused KPI visualization. In parallel, the deployment of autonomous systems—supported by IoT, RFID, edge computing, and AI agents—has enabled decentralized, self-optimizing decision-making across manufacturing, logistics, and procurement functions. Real-world case studies from industry leaders like Siemens, IBM, Honeywell, and John Deere illustrate the tangible impact of these technologies in achieving emissions reductions and improving system-wide sustainability performance. This study provides a comprehensive understanding of how the convergence of QAI, BI, and autonomous systems is shaping the future of carbon-conscious supply chains, offering both theoretical advancement and practical relevance for businesses committed to environmental responsibility and technological innovation.
General Background: Blockchain technology has gained significant global attention due to its potential to enhance transparency, security, and efficiency in various domains, including business forecasting. Specific Background: The integration of blockchain into forecasting mechanisms can improve supply chain efficiency, inventory management, and market demand prediction. Knowledge Gap: Despite its potential, limited research has systematically examined blockchain's role in forecasting capabilities, particularly through a bibliometric analysis approach. Aims: This study employs R Studio and VOSviewer to analyze bibliometric data from Scopus, aiming to identify trends, influential publications, and research gaps in blockchain-based forecasting. Methods: A systematic bibliometric analysis was conducted on 287 relevant articles published between 2015 and 2023, focusing on citation networks, keyword co-occurrence, and thematic clustering. Results: The findings indicate that forecasting is a dominant research theme, with China contributing the most publications. Key studies highlight blockchain's role in cryptocurrency prediction, supply chain management, and decentralized finance. Novelty: This research provides the first comprehensive bibliometric mapping of blockchain-based forecasting, revealing emerging trends and future directions. Implications: The study informs businesses, policymakers, and researchers on leveraging blockchain for predictive analytics, offering insights for enhancing decision-making in finance, trade, and supply chain management.