The use of blockchain technology in smart contracts is changing the way businesses are run in the modern world, with the ability to perform automated, transparent and additional contractual terms without being tampered with. This paper examines how smart contracts can be utilised with real-time business performance analytics to improve the efficiency of operations, accountability and strategic decision-making. Smart contracts can be utilized to validate transactions in a decentralized system by including a set of rules that ensure that the transactions are automatically validated immediately, minimize the reliance on intermediaries and minimize human error. Through the integration of the real-time analytics tools, the organizations will be able to track the key performance indicators (KPI), financial operations, supply chain operations and compliance indicators more precisely and promptly. The integration of these technologies promotes integrity of data, reinforcement of audit trails and predictive insights due to the continuous flow of data stream. Moreover, the study points to its use in finance, supply chain management, healthcare and digital services, noting that it has better transparency, cost reduction in operations and the trust of stakeholders. In spite of the benefits, there are still issues like scalability, interoperability, regulatory unpredictability and data privacy. The paper comes to a conclusion that a combination of smart contracts and real-time analytics offers a strategic platform to data-driven companies, which make agility decisions and sustainable competitive edges in dynamic digital markets.
This study identifies a key distinction between digital and traditional supply chain finance (SCF): technology-empowered financial service providers (FSPs) are no longer completely uninformed parties in financing markets. By examining an underexplored digital SCF scenario involving upstream focal firms and financially constrained downstream dealers, we reveal the effective signals that enable downstream small and micro enterprises (SMEs) to access SCF, and explore how and when FSPs use different screens to refine financing decisions. Using a dataset from MYBank, a leading Chinese big tech lender with nationwide coverage and a dominant market share in digital SCF, we find that a dealer's procurement amount from focal firms is a strong signal, particularly when credit limits are higher. Meanwhile, FSPs utilize stakeholder cues and digital footprints within their ecosystems to refine financing decisions. Three key stakeholder groups—owners, focal firms, and peer dealers—and regional digital financial inclusion influence the effectiveness of procurement signals. The impact of distinct signaling-screening mechanisms varies across firm size, platform registration duration, and regional marketization. Robustness tests, including IV-2SLS regressions, the Heckman two-step method, stringent fixed effects and subsample analysis, validate our findings. The study enriches the SCF literature by revealing the role of supply chain data in credit creation and identifying various signaling-screening mechanisms. It also extends screening theory by illustrating the contingent nature of screens. The findings respond to China's recent policy initiatives on decentralized supply chain loans and provide guidance for SCF practitioners.
The rapid expansion of digital banking ecosystems has intensified the demand for real-time fraud detection architectures capable of operating at cloud scale.As financial transactions increasingly traverse mobile platforms, API-driven services, embedded finance channels, and cross-border payment networks, fraud patterns have become more dynamic, decentralized, and behaviorally complex.This shift has exposed the limitations of legacy rule-based systems, which lack the adaptability, latency tolerance, and threatintelligence integration required to counter emerging risks.To address these challenges, cloud-native risk orchestration layers have emerged as a foundational component of next-generation fraud detection, delivering high-throughput data ingestion, elastic compute, and intelligent decisioning frameworks suited for modern digital banking environments.At a broader level, cloud-native risk orchestration unifies distributed event streams, machine-learning scoring engines, and policy-management modules within a scalable, microservices-based architecture.This enables fraud systems to process high-velocity transactional, behavioral, and device-identity signals with millisecond latency.As the narrative narrows, the paper explores how real-time fraud detection leverages cloud services such as serverless functions, container orchestration, distributed caching, and streaming analytics to enable adaptive detection pipelines.It further examines how federated intelligence, feature stores, and continuous learning loops enhance model accuracy while maintaining compliance with privacy and data-residency requirements.At its core, the proposed framework emphasizes explainability, risk transparency, and operational resilience incorporating alert-triage routing, anomaly-suppression mechanisms, decision traceability, and integration with case-management workflows.By combining cloud-native design principles with advanced fraud analytics, the paper outlines a comprehensive blueprint for financial institutions seeking to modernize their risk-management stack.This unified approach offers a path toward scalable, real-time, and intelligence-driven fraud prevention that adapts to evolving threats while supporting regulatory compliance and customer trust.
In the dynamic landscape of e-commerce, fostering customer loyalty is critical for sustainable growth and profitability, given the ease with which consumers can switch platforms and the high cost of acquiring new customers. This study explores multifaceted strategies for enhancing customer retention, including loyalty programs, gamification, customer lifetime value (CLV) and churn analytics, and community-based approaches. It examines how data-driven personalization, psychological reward systems, and emotional connections through brand communities drive loyalty. Examples such as Amazon Prime, Sephora’s Beauty Insider, and Nike Run Club illustrate the effectiveness of tailored rewards, gamification, and social engagement. The integration of CLV and churn analytics enables businesses to optimize resources by targeting high-value customers and predicting churn risk. Community strategies, leveraging social media, user-generated content, and events, foster a sense of belonging, particularly among younger demographics. Ethical considerations, including data privacy and transparency, are highlighted as essential for maintaining trust. The study underscores the evolving role of technology, such as AI and Web3, in shaping innovative, customer-centric loyalty strategies for both large and small e-commerce businesses.
The paradigm of enterprise analytics is undergoing a fundamental shift from centralized, reactive reporting to distributed, proactive intelligence. This review article evaluates the integration of machine learning models within SAP business intelligence frameworks operating across multi-cloud and hybrid environments. We analyze how the transition toward a federated data architecture, facilitated by SAP Datasphere, enables the deployment of high-performance neural networks without the traditional constraints of data replication. The study specifically examines the efficacy of Long Short-Term Memory units for temporal forecasting in SAP Integrated Business Planning and the role of unsupervised learning models in real-time financial anomaly detection. Furthermore, we explore the rise of augmented analytics and natural language processing in democratizing data access, alongside the operational necessity of MLOps to mitigate model drift in volatile global markets. The review also addresses critical technical and strategic barriers, including data latency across distributed cloud nodes, the harmonization of structured and unstructured data, and the evolving landscape of global data sovereignty. By synthesizing current performance benchmarks with future directions such as agentic intelligence and the integration of carbon accounting through the green ledger, this research provides a roadmap for architecting autonomous analytical ecosystems. We conclude that the convergence of machine learning and distributed cloud infrastructure is the primary catalyst for transforming raw enterprise data into a strategic, self-optimizing asset.
The growing reliance on digital financial services necessitates a secure, efficient, and privacy-centric approach to identity verification and Know Your Customer (KYC) compliance. Traditional identity management systems rely on centralized databases, making them susceptible to data breaches, inefficiencies, and regulatory constraints. Over 10 billion identity records have been exposed in centralized KYC breaches, leading to a 60% increase in financial fraud cases. The rise of Decentralized Finance (DeFi) has further complicated KYC compliance, requiring innovative solutions that balance privacy and regulatory requirements. This paper proposes a Web3-powered decentralized identity framework that leverages blockchain technology, self-sovereign identity (SSI), verifiable credentials (VCs), and zero-knowledge proofs (ZKPs). By eliminating reliance on centralized authorities, our system enhances data privacy, reducing personally identifiable information (PII) disclosure by 80% while ensuring compliance with AML and GDPR regulations. The integration of zk-SNARKs enables trustless identity verification with an average proof generation time of 12.5 seconds, significantly reducing the 3–5 day verification period required by traditional systems. Smart contract-based KYC automation eliminates intermediaries, cutting compliance costs by 40% and reducing fraud risk by 60%. Through comparative analysis, we highlight that decentralized KYC improves security, cost-effectiveness, and scalability compared to traditional models. Performance evaluation confirms that transaction throughput remains within acceptable blockchain limits, with gas costs stabilized at 35,000–55,000 Gwei per verification request. Despite challenges in regulatory adaptation and zk-SNARK scalability, the proposed model demonstrates the feasibility of Web3-driven identity management for trustless, privacy-preserving, and compliant financial ecosystems.
Purpose: The purpose of this research is to explore how new technologies, such as DLT (distributed ledger technology), ML (machine learning) and AI (artificial intelligence), can support green economic growth and sustainable finance. Need for the study: Awareness of environmental challenges highlights the importance of using technology to support and promote sustainable financial practices. Therefore, this study, among other things, aims to explore this importance by analysing how AI, ML and DLT can contribute to continuously improving innovation and efficiency in various sustainable finance projects. Methodology: This study uses a literature review technique to examine technology use, sustainable finance, and the transition to a sustainable economy. To achieve this goal, qualitative interviews were conducted with professionals in the fields of sustainability, technology and finance to explore different practices and identify different strategies for developing the future of the finance industry. Findings: Based on the findings of previous studies, AI, ML and DLT play an important role in improving risk management, increasing transparency and simplifying procedures that serve to make decisions in the sustainable finance sector. The transformation to a green and sustainable economy can be more straightforward if it relies on the ability of these important technologies to incorporate some ESG (environmental, social and governance) considerations into organisations’ plans for potential investments. Practical implications: The study's findings will help software developers, financial institutions and policymakers promote and strengthen sustainable development. Furthermore, the use of technology, especially advances in AI, ML and DLT, offers various valuable perspectives for those engaged in the transition to more environmentally friendly financial practices, contributing to creating a more sustainable global economy.
The rapid growth of the AI-driven digital economy has intensified the demand for scalable optimization techniques and secure computational frameworks across industries such as logistics, finance, healthcare, and autonomous systems. Classical computational frameworks are becoming inadequate for addressing the inherent combinatorial complexity of AI processes and for maintaining robust cryptographic protections suitable for decentralized and sensitive data environments. This paper explores how emerging quantum computing paradigms, particularly the quantum approximate optimization algorithm and quantum-state-based cryptographic protocols, can provide effective solutions to these dual challenges. As fully fault-tolerant quantum systems remain on the horizon, hybrid quantum-classical architectures provide a practical, near-term solution. These systems embed quantum modules into classical AI pipelines, enabling enhanced optimization capabilities and quantum-resilient communication within the constraints of current noisy intermediate-scale quantum devices. We discuss a potential hybrid architecture intended to support complex decision-making and secure data exchange in practical settings. The study includes comparative analyses of classical, post-quantum, and quantum-state-based techniques and evaluates their applicability across key sectors. Rather than replacing existing systems, quantum methods are positioned as complementary technologies, offering domain-specific advantages in computational efficiency, data security, and decentralized functionality, which are essential capabilities for the future AI-enabled digital infrastructure.
Abstract: The transition from centralized digital ecosystems to decentralized, trust - driven architectures represents a defining paradigm shift in Customer Experience (CX). This paper presents a strategic blueprint for leveraging block chain technologies to build secure, transparent, and interoperable customer - centric environments between 2025 and 2030. Through a comprehensive review of market forecasts, enterprise case studies, and emerging regulatory frameworks, the study demonstrates how decentralized identity (DID), verifiable credentials, and tokenized loyalty systems fundamentally reshape customer engagement, ownership of personal data, and trust models. Findings indicate that block chain adoption empowers customers with self - sovereign identity control, enhances privacy compliance, and delivers measurable efficiency gains in verification, loyalty management, and supply - chain transparency. Case evidence from leading enterprises — including JPMorgan, AXA, Santander, and Accenture — highlights significant improvements in transaction speed, operational costs, and customer engagement. Despite challenges such as legacy system integration and GDPR - related constraints, hybrid architectures, Layer - Two scalability, and permissioned block chain environments provide viable adoption pathways. This paper concludes that block chain is not a supplementary technology for CX, but a foundational enabler of decentralized trust, competitive differentiation, and customer - driven digital ecosystems. Keywords: Block chain; Customer Experience (CX), Decentralized Identity (DID), Verifiable Credentials, Tokenized Loyalty Programs, Digital Trust, Self - Sovereign Identity, Smart Contracts, Hybrid Data Architecture, GDPR Compliance, Enterprise Digital Transformation, Web3 Customer Strategy
As the convergence of blockchain, cryptocurrency, and artificial intelligence technologies continue to evolve, they are merging to revolutionize decision-making processes, financial transactions, and strategic business operations across various industries. This chapter aims to explore the dynamics of this convergence, examining its potential to drive significant shifts in how businesses operate, manage risks, and create value in a rapidly digitizing global economy. The mission of this chapter is twofold- To provide an in-depth analysis of how the integration of AI and cryptocurrency is reshaping traditional business models and decision-making strategies and To identify practical business applications and strategic advantages that companies can harness by adopting these technologies. By delving into the synergies between AI and cryptocurrency, the chapter seeks to offer readers a comprehensive understanding of their transformative impact on businesses and guide organizations in leveraging these innovations for long-term competitive advantage.
The US Department of Transportation (USDOT) delivers a wide range of infrastructure projects, backed by a fiscal year 2023 budget exceeding $100 billion. These projects face mounting pressures to meet performance, accountability, and delivery standards, driven by their dependence on public funding and their operational complexity. Transportation infrastructure presents sector-specific challenges—such as time-sensitive user disruptions, multiparty coordination, and asset intersection risks—that demand more robust, automated, and transparent project delivery mechanisms. Blockchain-enabled smart contracts have emerged as a promising solution to address these operational pain points through real-time automation, immutable data records, and decentralized transaction processing. However, the practical realities of the transportation sector—its fragmented systems, regulatory layers, and diverse stakeholder interfaces—create unique integration challenges that remain underexamined. To address this, this study investigates how smart contracts can be effectively integrated into transportation infrastructure by identifying the context-specific needs, requirements, capabilities, and challenges that govern their adoption. A three-phase research design was employed. First, a literature review was conducted to extract generalized integration factors for smart contract use in the broader construction domain. Secondly, these factors were evaluated and ranked by qualified transportation experts to reflect their relevance in sector-specific contexts. Thirdly, structural equation modeling (SEM) was used to analyze expert survey responses and isolate the most influential integration drivers. The results indicate that, unlike general construction projects, the top integration priorities in transportation include (1) compliance checking for quality management (needs); (2) integration with existing cloud repositories or enterprise platforms (requirements); (3) the ability to maintain immutable records (capabilities); and (4) uncertainty regarding usability (challenges). These findings provide a targeted knowledge base for practitioners and policymakers, outlining the critical considerations required for effective and sector-sensitive implementation of smart contracts in transportation infrastructure.
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.
As information technology underpins advances in life and healthcare sciences, there is a growing intersection of healthcare and information engineering that is opening new possibilities for remote health monitoring and the secure exchange of health information between patients and clinicians. To gain the trust of the citizens, healthcare technologies need to ensure that the information they store and process is confidential, has not been tampered with, and, in the case of large-scale processing, is conducted according to probabilistic compliance policies. Currently, the onus on data protection practices of healthcare technology providers is drawn from legislation. This paper outlines data integrity and compliance policies and shows how these can be encoded in a blockchain-based system. The study enhances this blockchain-based system to use the Ethereum blockchain for executing smart contracts, which can execute probabilistic compliance rewarding health-related workflows. These smart contracts increase transparency and data integrity by not only laying out a set of promises for citizens and public health physicians to monitor the state of a blockchain protocol to ensure there are no attempted violations, thus increasing the service's trustworthiness. While the security of the blockchain is used to ensure data privacy and security, all blockchain-located proxy healthcare data can only be accessed through patients appointing a blockchain address and the associated articulated smart contract at their own discretion, making the proposed solution particularly patient-centric.
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.
Purpose Previous research has identified numerous business applications for non-fungible tokens (NFTs), yet there is a dearth of studies exploring this phenomenon in managerial practice. This article fills this gap by employing service-dominant logic and examining multiple NFT projects to gain insights into how the deployment of NFTs can contribute to achieving companies’ strategic objectives. Design/methodology/approach Our research employs a diverse case selection strategy, focusing on 13 well-established brands using NFTs to transform their activities across various countries and sectors. We conducted a secondary data analysis, including press articles and social media content, to identify relevant NFT projects and develop five propositions that offer entry points for future studies in the broad field of crypto-marketing. Findings Five main areas are identified in which NFTs can help advance companies’ strategic objectives: (1) enhancing perceived quality, (2) driving innovation performance, (3) accessing funds with zero interest, (4) enhancing flexibility and (5) promoting sustainability. Originality/value This article is among the first to investigate and systematize current trends and applications of NFTs in relation to companies’ strategic objectives. It provides actionable insights for practitioners and specific propositions for further research by management scholars.
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.
The construction supply chain often faces challenges such as contract disputes, inefficient payments, and difficult claim management due to its complexity and the involvement of multiple parties. Most existing solutions focus on optimizing contract terms or improving local processes, but they lack systemization, automation, and transparency. Therefore, this study proposes a claim and payment process management model based on blockchain and smart contracts, aiming to achieve digital and automated governance of the construction supply chain. The study constructs three types of smart contracts: The supply chain decomposition smart contract automatically divides engineering projects into independent billing cycles. The billing unit smart contract monitors the compliance of construction. The negotiation and settlement smart contract automatically handles disputes and payments. These three types of smart contracts work together to form a decentralized dynamic management framework. Through simulation experiments comparing the traditional process with the smart contract model, the results show that in scenarios with a high probability of claims and a large proportion of construction defects, the capital flow efficiency of the smart contract model is increased by more than 20%, and it shows stronger stability under high claim risks. The contribution of this study lies in combining blockchain technology with the logic of supply chain decomposition, proposing a smart contract system applicable to dynamic engineering projects, thus providing a digital processing method for the integrated management of claims and payments in the construction supply chain. This digital dynamic management method can ultimately systematically solve the island problem in supply chain claim research. Its automated and transparent characteristics help reduce dispute costs and enhance trust among multiple parties, which has important practical significance for improving the overall efficiency of the industry.