This study aims to provide foundational data for developing sports non-fungible token (NFT) marketing strategies and enhancing fan experiences by analyzing public sentiment and semantic structures of NBA NFTs. Social big data were collected between 2021 and 2025 from six global platforms (Google, YouTube, Twitter, Reddit, Yahoo, Quora) using the TextoM platform. The analyses conducted included text mining, sentiment analysis, semantic network analysis, and CONCOR analysis. Central keywords included NFT, NBA, TopShot, player, team, marketplace, and crypto. Sentiment analysis indicated 67.5% positive and 32.5% negative sentiment. Semantic network analysis revealed a structure centered around NFT, community, news, game, and blockchain. CONCOR analysis identified five clusters: NFT Infrastructure, NBA Branding, Market Economy, Community Engagement, and Temporal Context. Overall, NBA NFTs are perceived as technical assets and as emotionally driven, identity- and community-centered content. The findings of this study offer significant practical implications for practitioners and managers in the sports industry by guiding the development of marketing strategies that integrate emotional engagement and multidimensional consumer value. This study contributes to the emerging literature on sports NFTs by providing exploratory discourse-level insights into how NBA NFTs are discussed across online platforms and by identifying themes that may inform future theory-driven research at the consumer level.
Accurately quantifying enterprise digital capability is fundamental to evaluating industrial modernization, yet conventional empirical inquiries predominantly rely on single-dimensional proxy variables or unweighted keyword counts, failing to capture the multidimensional integration of digital assets. Moving beyond causal regression paradigms and reductionist metrics, this inquiry develops a comprehensive, objective Digital Readiness Index (DRI) for physical manufacturing enterprises using an information-theoretic Entropy Weight Method (EWM). Grounded in multi-source text-mining disclosures and corporate balance sheets across 41,756 firm-year observations of Chinese A-share listed manufacturing enterprises spanning 2000 to 2025, the evaluation framework integrates eight discrete operational indicators across three dimensions: Technical Depth (AI, Big Data, Cloud Computing, Blockchain, and Digital Applications), Intangible Capital Endowments, and Governance Oversight. Objective entropy weighting demonstrates that specialized frontier technologies, particularly Blockchain (w=37.81%), Artificial Intelligence (w=15.95%), Big Data Analytics (w=15.93%), and Cloud Computing (w=15.66%)—constitute the primary sources of informational divergence across manufacturing firms. Longitudinal trajectory evaluation reveals a sustained upward trajectory in mean digital readiness, accelerating markedly after the 2015 macroeconomic policy inflection point. Non-parametric Gaussian Kernel Density Estimation uncovers a distinct dynamic polarization pattern, characterized by a shifting rightward distribution and an elongating upper tail. Cross-sectional decomposition establishes substantial structural disparities: high-tech sectors such as Computers and Electronics exhibit the highest mean digital readiness (DRI=16.28), whereas chemical and pharmaceutical sectors display persistent digital inertia (DRI≈4.06). Furthermore, non-state-owned enterprises (Non-SOEs) systematically outperform state-owned enterprises (SOEs) across all asset scale tiers. These findings provide an objective measurement tool and benchmark for corporate technology auditing and industrial policy calibration.
The study involved a total of 217 respondents. consisting of key stakeholders in higher education, including undergraduate and postgraduate students, academic staff, and administrative personnel. The respondents were selected using a purposive sampling technique to ensure they had relevant experience and understanding of academic data management systems. Among the participants, the majority were students, representing approximately 65%, followed by academic staff at 20%, and administrative personnel at 15%. In terms of gender distribution, 54% were female and 46% were male. Most respondents were aged between 18 and 30 years, reflecting a digitally active population familiar with emerging technologies. Additionally, a significant proportion of respondents reported prior exposure to digital academic systems, while a smaller percentage demonstrated awareness of blockchain technology applications in education. This distribution ensures that the collected data reflects diverse perspectives within the academic ecosystem and supports the reliability of the analysis conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS).
Omar Al-Jamili, Abdulaziz Fahmi Omar Faqera, Mohd Adan Omar, Shehu M. Sarkintudu · 8 authors
Open Government Data (OGD) has become central to digital transformation and data-driven governance, yet scholarly understanding of how OGD initiatives progress from initial adoption to sustained institutionalization remains fragmented. This study aims to synthesize the existing literature and develop an integrative framework that explains the socio-technical mechanisms underpinning the long-term sustainability and value creation of OGD initiatives. The study integrates bibliometric analysis with a systematic literature review of 481 peer-reviewed articles published between 2010 and 31 December 2024. Quantitative science-mapping techniques are combined with qualitative thematic synthesis to capture the intellectual structure, technological evolution, and theoretical foundations of OGD research. The findings reveal rapid growth and thematic diversification in OGD scholarship, with increasing attention to advanced technologies such as artificial intelligence and blockchain. However, the literature remains theoretically fragmented across behavioral, institutional, and public-value perspectives. Two critical gaps are identified: insufficient theorization of institutional legitimacy as a driver of continuity, and limited exploration of user-centric governance mechanisms shaping sustained data reuse. To address these gaps, the study proposes the Socio-Technical Institutionalization Model (STIM), which conceptualizes OGD sustainability as the dynamic alignment of technological infrastructures, institutional arrangements, and user ecosystems. By combining quantitative science mapping with systematic thematic synthesis and proposing the STIM lifecycle framework, this study offers an integrative synthesis that extends prior OGD reviews. The framework bridges fragmented theoretical perspectives and explains how open data initiatives may evolve from adoption to institutionalized value creation within complex digital governance ecosystems.
In modern business and trade, digital transformation (DT) has become a key factor in gaining a competitive edge. Rapid developments in blockchain, big data analytics, cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) are changing company models, value generation workflows, and organizational strategies. By combining organizational, strategic, and technological viewpoints, this study offers a multifaceted examination of digital transformation. Secondary data from peer-reviewed literature, international industry publications, and corporate disclosures of top companies, such as Amazon, Alibaba Group, Microsoft, and Tesla, Inc., were analyzed using a descriptive and analytical research design. The study creates a conceptual framework that connects performance results, transformation processes, and digital drivers. The results indicate that ecosystem integration, organizational agility, digital capability development, and strategic alignment are necessary for a successful digital transformation. The paper contributes to digital transformation literature by combining findings from several sectors and putting forth an integrated strategic model that can be empirically validated in further studies; the paper adds to the body of knowledge on digital transformation.
Abstract Internal audit functions in U.S. manufacturing and retail face a growing disconnect between increasingly sophisticated fraud schemes and legacy detection methods that rely on static rules and manual sampling. The Association of Certified Fraud Examiners estimates that organizations lose approximately five percent of annual revenue to fraud, with manufacturing and retail sectors particularly vulnerable due to complex supply chains, high transaction volumes, and decentralized operations. Modern fraud schemes have evolved well beyond simple expense manipulation; they now involve multi-party collusion, cyber-enabled invoice fraud, supply chain manipulation through fictitious vendors, and coordinated point-of-sale skimming networks. Traditional audit approaches typically cover only three to five percent of transactions through periodic sampling, leaving the vast majority of activities unexamined and creating significant windows of exposure. Learningter demonstrates how applied AI (machine learning anomaly detection, natural language processing, and agentic AI) transforms fraud detection from reactive forensics into proactive, continuous assurance. Drawing on five anonymized case studies from active industry engagements, the presentation illustrates measurable outcomes: false-positive rates reduced by up to 70 percent, detection time compressed from months to minutes, and coverage expanded from sample-based testing to full-population analysis. Each case maps legacy controls against AI-augmented alternatives, providing a clear migration pathway. In particular, Agentic AI enables autonomous and continuous monitoring through self-correcting feedback loops that recalibrate detection models in real time without requiring manual intervention, adapting dynamically to emerging fraud patterns and shifting transaction behaviors. The presentation addresses practical adoption challenges (data quality, algorithmic bias, SOX/ICFR compliance, and change management) and offers a structured readiness framework for consulting engagements or dissertation research. Grounded in Boyer's Scholarship of Application, this work connects data science and auditing to real-world problems, demonstrating how cross-disciplinary collaboration produces actionable improvements in governance and risk management. The research is directly relevant to doctoral candidates seeking applied dissertation topics with measurable industry impact and to faculty developing curricula that bridge theoretical foundations with practitioner-oriented pedagogy.
Predictive Churn Modeling and Proactive Service Using Customer Interaction Data Objectives: 1. To provide a comprehensive understanding of cloud-native architectures and middleware technologies used for designing scalable, resilient, and high-performance financial trading systems. 2. To explain the core concepts of microservices, containerization, orchestration, distributed messaging, and data management that power modern financial platforms and digital banking ecosystems. 3. To demonstrate the practical implementation of advanced technologies such as Kubernetes, Apache Kafka, Redis, gRPC, and AI-driven solutions for real-time trading and financial service delivery. 4. To equip software engineers, solution architects, researchers, and FinTech professionals with the knowledge required to build secure, fault-tolerant, low-latency, and highly observable trading infrastructures. 5. To explore emerging trends in financial technology, including serverless computing, WebAssembly, Artificial Intelligence, Machine Learning, and Decentralized Finance (DeFi), preparing readers for the next generation of cloud-native financial systems. Table of Contents CHAPTER 1 The Foundation of Customer Retention: Concepts and Definitions CHAPTER 2 The Business Value of Predicting Churn: Impact on ROI CHAPTER 3 Sources of Customer Interaction Data: CRM, Logs, and Beyond CHAPTER 4 The Architecture of a Churn Prediction System CHAPTER 5 Data Acquisition and Quality Assessment CHAPTER 6 Preprocessing High-Dimensional Interaction Data CHAPTER 7 Feature Engineering: Creating Meaningful Indicators from Raw Data CHAPTER 8 Exploratory Data Analysis for Churn Patterns CHAPTER 9 Traditional Statistical Methods in Churn Modeling CHAPTER 10 Machine Learning Approaches: From Random Forests to XGBoost CHAPTER 11 Deep Learning for Temporal Interaction Sequences CHAPTER 12 Natural Language Processing for Sentiment-Based Churn Analysis CHAPTER 13 Handling Class Imbalance in Churn Datasets CHAPTER 14 Evaluating Model Performance: Beyond Accuracy CHAPTER 15 Interpreting Black-Box Models for Stakeholder Trust CHAPTER 16 Real-Time Churn Scoring and Pipeline Automation CHAPTER 17 Designing Proactive Service Interventions CHAPTER 18 Personalized Marketing and Customer Success Strategies CHAPTER 19 Ethical Considerations and Data Privacy in Churn Modeling CHAPTER 20 Case Studies and Future Trends in Predictive Analytics
Few forces have reshaped organizational life as quickly as digital transformation. The way firms create value, manage risk, and hold their competitive ground now depends on systems that grow more entangled with one another every year. A typical enterprise sits at the center of a constant flow of data drawn from its operations, its cloud platforms, the sensors embedded in its products, its planning systems, and the many places where it meets its customers. Artificial intelligence (AI), machine learning, big data analytics, blockchain, and cybersecurity have each made it easier to turn that flow into useful judgment. Yet most organizations still adopt these tools one at a time, and the habit quietly erodes the strategic payoff that integration could deliver. This paper sets out an Autonomous Decision Intelligence (ADI) framework that gathers AI, cybersecurity, big data analytics, blockchain, and management information systems (MIS) into one coherent architecture built for resilient digital enterprises. The argument rests on a synthesis of recent work in decision intelligence, predictive analytics, business intelligence, federated learning, cloud computing, blockchain governance, cyber threat intelligence, and enterprise risk management. From that body of evidence, we construct a conceptual model for organizational decision-making that is trustworthy and capable of improving itself over time. The framework gives weight to secure data governance, explainable AI, privacy-preserving analytics, blockchain-based trust, cyber-resilience, and intelligent automation. It then asks how such a design might reinforce critical infrastructure protection, supply chain resilience, economic sustainability, IT project governance, and day-to-day agility. The contribution is at once theoretical and practical, because it shows how converging technologies can turn conventional decision-support systems into adaptive ecosystems that learn. Organizations that pair AI-driven analytics with strong security and decentralized trust look best placed to absorb uncertainty, keep operating under stress, and pursue digital transformation that lasts.
Connects to 16 breakthroughs. AUM Inflow Despite Price Slide - TipRanks From GoogleNews (271,272,274,275,276,277,278,279,285,286,287,288,290,291,293,294). Avg score: 0.24 Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Introduction, Sports science data governance is characterized by persistent tensions between data sharing, stakeholder incentives, and regulatory constraints. These challenges are amplified by fragmented data infrastructures and competing interests among stakeholders, limiting the effective use of data in performance optimization and research. Objective, This study aims to develop and theoretically ground a decentralized autonomous organization (DAO)-based governance framework for sports science data ecosystems, focusing on how decentralized mechanisms can enhance coordination, participation, and compliance. Methodology, A multi-method research design is employed, integrating conceptual case analysis, agent-based modeling (ABM), and survey-based empirical analysis. Structural equation modeling (SEM) is used to examine the relationships between governance perceptions, incentives, and data-sharing intentions. Results, The findings indicate that DAO-based mechanisms can support more distributed and transparent data-sharing processes. Simulation results suggest that participation dynamics follow non-linear patterns, with incentive and reputation mechanisms contributing to system stabilization. Empirical results identify technical usability, perceived regulatory compliance, and incentive structures as significant predictors of stakeholder participation. Discussion, The study contributes to platform governance and institutional theory by conceptualizing a hybrid decentralized governance model for data-intensive environments. The findings highlight the importance of aligning technological design with usability and regulatory requirements. However, limitations related to model assumptions, perception-based data, and interoperability challenges remain. Future research should focus on real-world implementation and the development of standardized governance frameworks.
Transforming legacy SAP systems into smart cloud-based systems is a major shift in today’s digital strategy. This transformation re-engineers old SAP environments, which are often rigid and not easily scalable, by utilizing current technologies, including artificial intelligence and cloud services like AWS. This paper discusses how AI-driven cloud transformation can help organizations transform their SAP ecosystems to be more agile, scalable, and data-driven in their decision-making processes. It addresses enterprise AI, intelligent automation, hybrid cloud environments, and other emerging technologies such as generative AI and distributed ledger systems. The discussion demonstrates how such innovations can be utilized to help create smart enterprises that can make predictions, adapt to changes, and operate more independently. The paper also takes into account the changing role of business analysis and knowledge ecosystems in facilitating this transformation. By integrating these developments and models, this paper provides a comprehensive view of the process of reinventing SAP landscapes to meet the demands of a constantly evolving digital economy.
Umar Yeni Suyanto, Ratna Rosita Pangestika, Kinanti Puja Prameswari, Heni Setiyaningsih
The integration of Artificial Intelligence (AI) into Small and Medium Enterprises (SMEs) has become a critical lever for achieving resilience, efficiency, and long-term sustainability in the digital era. However, despite AI’s transformative potential, empirical evidence suggests a persistent gap between technological capabilities and actual adoption within the SME sector. This study employs a bibliometric analysis using VOSviewer with the keywords "artificial intelligence" OR "AI" AND "Small and medium enterprises" OR "SMEs" AND "digital", encompassing 150 Scopus indexed articles from 2017 to 2025. The visualizations reveal six prominent thematic clusters, including AI based adaptive strategies, post-pandemic digital transformation, decentralized finance, digital literacy, and emerging concepts such as green cybersecurity. Notably, overlay visualizations indicate that sustainability-oriented digital practices are gaining scholarly momentum, signaling a future research trajectory focused on inclusive, secure, and environmentally conscious AI applications in SMEs. This article proposes a conceptual model SDRAIS (SME Digital Resilience through AI and Sustainability) that integrates three strategic dimensions: Strategic AI Integration, Digital Capabilities, and Sustainability Orientation. The model advances theoretical development by aligning with the Dynamic Capabilities and TOE (Technology Organization Environment) frameworks, while also responding to gaps in Triple Bottom Line (TBL)-driven technology adoption. The findings offer new perspectives for policymakers, SME stakeholders, and researchers by emphasizing the importance of interdisciplinary approaches to foster AI-driven innovation ecosystems that are both competitive and sustainable. This study contributes to the evolving discourse on digital transformation in SMEs and sets a robust foundation for future empirical exploration.
The retail and consumer packaged goods industries are at an inflection point; the autonomous, goal-oriented software agents are substituting the inflexible, analyst-reliant business decision cycles with closed-loop intelligence systems, which can perceive, reason, and act in real-time. The autonomy, proactivity, and constant learning of agentic AI redesign the pricing, trade promotion optimization, and supply chain coordination processes within complicated, multi-account business settings. Based on proven sources of empirical evidence in the literature on machine learning, multi-agent reinforcement learning, and supply chain optimization, the technical architecture of an agentic commercial system is discussed along five related dimensions: autonomous trade performance monitoring through perception-reasoning-action pipelines; cooperative multi-agent system design under the models of centralized training and decentralized execution; scenario simulation engine based on digital twin models; multi-objective trade promotion optimization with Pareto-front metaheuristic algorithms; and practical barriers of data infrastructure, model drift, organizational change management, and algorithmic governance. Bringing these capabilities together into a single agentic decision stack is a paradigm shift in the concept of commercial intelligence in retail and CPG, moving the operational center of gravity off retrospective dashboards and onto adaptive, constantly learning systems that coordinate the decisions on pricing, promotion, and supply.
This study identifies major approaches in token design for founders in the cryptocurrency/web3/blockchain space. The high failure rate of blockchain companies means that successful long-term performance will depend greatly on well-designed tokens. This study will integrate all prior research to highlight the most important aspects of structured tokenomics, including token utility, governance, and security. The study also contributes to the literature by introducing the Business Model Canvas as a conceptual framework that enables the integration of best practices for token design, drawing on both academic and industry literature. The results indicate significant gaps in the literature. This study offers new and practical insights for founders to enhance stakeholders’ engagement, improve regulatory compliance, and ensure project viability in the volatile cryptocurrency market. Furthermore, this research generates new knowledge that bridges the gap between the theory and practice of tokenomics, laying the groundwork for future research to develop and refine token design strategies.
The increasing deployment of agentic artificial intelligence (AI) systems and decentralized digital infrastructures has challenged traditional assumptions about organizational administration, control, and governance. While AI has advanced task-level optimization and decision support, administrative functions such as coordination, compliance, and accountability remain largely centralized and dependent on humans. This paper introduces Autonomous Administrative Intelligence (AAI), a governance-aware AI capability that enables autonomous agents to execute and adapt administrative decisions within strategically defined constraints and decentralized governance mechanisms. Building on the Strategic Decentralized Resilience–AI (SDRT-AI) framework, the study develops a layered architecture and operational flow integrating agentic decision-making, governance-aware learning, and protocol-based validation. The proposed framework explains how strategic intent, organizational capabilities, and decentralized trust jointly enable scalable administrative autonomy while preserving accountability and control. By reframing administration as an AI-mediated governance process, this paper extends research on agentic AI and contributes to administrative science by providing a conceptual foundation for the design and governance of autonomous administrative systems in decentralized organizations.
The Fourth Industrial Revolution, commonly referred to as Industry 4.0, represents a fundamental transformation of manufacturing systems through the integration of advanced digital technologies such as the Industrial Internet of Things (IIoT), artificial intelligence, big data analytics, cloud computing, and autonomous robotics. This paradigm shift enables the development of cyber-physical systems and smart factories characterized by real-time connectivity, decentralized decision-making, and data-driven optimization. The present study examines the conceptual foundations, technological pillars, and operational impacts of Industry 4.0, with particular emphasis on automation, productivity enhancement, and sustainability outcomes. Using a synthesis of recent empirical studies, global market data, and evidence from World Economic Forum “Lighthouse” factories, the paper evaluates how digital transformation influences manufacturing efficiency, energy use, emissions reduction, and workforce dynamics. The findings indicate that Industry 4.0 adoption significantly improves labor productivity, operational flexibility, and resource efficiency, while also presenting challenges related to cybersecurity, legacy system integration, and skills gaps. The study concludes that Industry 4.0 is not merely a technological upgrade but a strategic and organizational transformation essential for achieving competitive advantage and sustainable industrial development in an increasingly volatile global economy.
Mostafa Shabani, Sina Tavakoli, Hossein Ghanbari, Ronald Ravinesh Kumar · 5 authors
The acceleration of financial innovation and pro-crypto regulations in the digital asset space have spurred interest in cryptocurrencies among funds, and institutional and retail investors. Like any risky assets, investment in digital assets offers opportunities in terms of returns and challenges in terms of risk. However, unlike traditional assets, digital assets like cryptocurrencies are highly volatile. Accordingly, applying conventional single-criterion financial metrics for portfolio construction may not be sufficient as the method falls short in capturing the complex, multidimensional risk-return dynamics of innovative financial assets like cryptocurrencies. To address this gap, this study introduces a novel, integrated hybrid Multi-Criteria Decision-Making (MCDM) framework that provides a structured, transparent, and robust approach to cryptocurrency fund selection. The framework seamlessly integrates three well-established operations research methodologies: the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP), and the Vlse Kriterijumsk Optimizacija I Kompromisno Resenje (VIKOR) algorithm. DEMATEL is utilized to map and analyze the intricate causal interdependencies among a comprehensive set of evaluation criteria, categorizing them into foundational “cause” factors and resultant “effect” factors. This causal structure informs the ANP model, which computes precise criterion weights while accounting for complex feedback and dependency relationships. Subsequently, the VIKOR algorithm is invoked to use these weights to rank cryptocurrency fund alternatives, delivering a compromise between optimizing group utility and minimizing individual regret. To illustrate the application and efficacy of the proposed method, a diverse set of 20 cryptocurrency funds is analyzed. From the analysis, it is shown that foundational criteria, such as “Fee (%)” and “Annualized Standard Deviation,” are the primary causal drivers of financial performance outcomes of funds. This proposed framework supports strategic capital allocation in a rapidly evolving domains of digital finance.
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.
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