Blockchain Papers

R. Li, Srisht Fateh Singh, Andreas Park, Andreas Veneris

This paper presents a securities tokenization solution that brings the accessibility, transparency, efficiency, and innovation of blockchain and decentralized finance to real-world securities. Tokenization in principle seems straightforward—an intermediary holds assets and issues 1:1 tokens—but decentralized finance applications (DeFi) introduce significant complications. Even basic DeFi mechanisms, such as liquidity pools, pose challenges for tokenizing stocks and bonds because when assets are pooled in smart contracts, ownership becomes unclear, hindering asset owners to access their entitlements, such as dividends, coupons, or voting rights. Existing solutions often fail to address these challenges and are typically limited to specific security types. Our solution, by contrast, generalizes to any security and any holding rights through fungible tokens and using separate smart contracts for shareholders to redeem their entitlements. To address the decentralized ownership issue, our solution employs off-chain accounting with additional logic for liquidity pools. We implement this on Ethereum, demonstrating that it is 27% cheaper in gas costs than current alternatives. We also analyze the liquidity logic of over 90% of Ethereum's liquidity pools, confirming compatibility with our solution. Finally, we demonstrate its use for dividend-paying stocks, common stock, mergers, and coupon-paying bonds.

Christian Zeiß, Lisa Straub, Maximilian Greiner, Marcel Neis · 7 authors

Purpose To promote acceptance of blockchain-based investment options and enhance confidence for new investors, the market must become more comprehensible and accessible to the broad masses. This requires transparency to build trust in web-based intermediaries, particularly given the multitude of websites that often advertise unrealistic returns in the crypto sector. Consequently, intermediaries within the decentralized finance ecosystem need to be clearly identified and categorized to facilitate mass-market adoption. Design/methodology/approach We employ a six-iteration taxonomy approach, establishing a data foundation through literature reviews, expert interviews and document analysis of 50 intermediaries. Archetypes are derived using a hierarchical clustering algorithm. Finally, a survey is conducted to evaluate the taxonomy and the archetypes. Findings The taxonomy encompasses three meta-characteristics (functionality, architecture, security) and 63 characteristics. Furthermore, the research findings reveal six archetypes of blockchain-based investment intermediaries, demonstrating significant discrepancies between them, particularly in terms of financial features and governance structures. Given the complexity of crypto intermediary platforms for novice users, the findings underscore the need to implement technology-based and institutional-based trust mechanisms, improve risk assessment and enable informed decision-making. Originality/value By increasing market transparency and fostering trust, this study contributes to the acceptance and adoption of blockchain-based financial intermediaries, drawing on the diffusion of innovation theory. The proposed taxonomy, particularly its dimensions, specifically addresses the requirements of both technology-based and institution-based trust, which are critical for crypto investments. Moreover, the findings emphasize the importance of educational resources and communicated trust features in strengthening user confidence and facilitating broader market participation.

Krzysztof Lorenz, Piotr Gutowski, Ewelina Gutowska, Anna Drab-Kurowska

Digital transformation is reshaping innovation processes and capital allocation models, fostering the emergence of alternative financing mechanisms such as crowdfunding platforms. This study investigates the spatial determinants of digital innovation development using Kickstarter campaigns in the United States as a case study. Empirical data were preprocessed and classified into digital and traditional categories. Advanced AI methods, including Deep Autoencoders and Self-Organizing Maps (SOM), revealed spatial clusters of digital innovation in crowdfunding. Cluster visualizations exposed geographic concentration patterns and links to local infrastructure. AI uncovered latent ties between campaign structure and regional context, underscoring the role of AI and crowdfunding in decentralized, localized digital transformation.

Boudreau, Keven

This paper introduces a novel gradient-based framework for maintaining protocol invariants in decentralized finance (DeFi) systems, representing a fundamental departure from traditional reactive monitoring approaches. Rather than detecting violations after they occur and adjusting system perceptions, this framework implements a proactive closed-loop control system that influences external market dynamics through calibrated protocol actions. The core innovation lies in tracking confidence gradients—including velocity, acceleration, and momentum—to predict potential invariant violations before they materialize. The system employs a nine-parameter calibration vector that dynamically adjusts based on confidence trends, enabling the protocol to respond appropriately to increasing (healthy), decreasing (crisis), or stable market conditions. During crisis scenarios, aggressive parameter adjustments trigger protocol actions such as enhanced lender incentives and borrower penalties, directly influencing market participant behavior to maintain critical constraints like maximum lending rates. The framework establishes mathematical foundations for confidence gradient metrics, parameter constraint spaces, and crisis level assessment. It demonstrates how calibrated algorithmic behavior generates measurable influence on external actors, effectively changing market reality rather than merely observing it. Theoretical analysis proves bounded confidence oscillations and probabilistic invariant maintenance guarantees under the proposed calibration schemes. Practical implementation considerations include smart contract architecture, gas optimization strategies through batched updates and fixed-point arithmetic, and parameter discretization for on-chain deployment. A numerical crisis response example illustrates the system's ability to prevent rate violations through coordinated supply increases and demand reductions. The paper positions this approach as foundational for the Kera Protocol ecosystem, with applications extending beyond DeFi lending to automated market makers, stablecoin protocols, governance systems, and cross-chain bridges. This paradigm shift from observation to calibrated influence represents a significant advancement in blockchain protocol stability mechanisms.

Boudreau, Keven

This paper presents the revolutionary Kera Protocol, a mathematically proven blockchain architecture that fundamentally solves the dual crises of accessibility and sustainability plaguing contemporary decentralized finance systems. The work addresses the stark reality that 99.95% of humanity remains excluded from blockchain validation due to prohibitive capital requirements. The paper's core contribution establishes the first provably sustainable economic model in blockchain history through a dynamic APY allocation algorithm that maintains the fundamental invariant OBLIGATIONS = REVENUE at every 12-second block interval. This mathematical constraint creates theoretical impossibility of protocol insolvency, directly addressing the $108 billion in losses from failed DeFi protocols like Terra/LUNA, Celsius, and BlockFi that promised unsustainable fixed returns. The research introduces an innovative vault-to-pool economic architecture leveraging 20x capital efficiency to deliver mathematically certain 102% APY returns—derived from real interest accrual rather than speculative mechanisms. Rigorous validation through the MALIV (Multi-Agent Long-term Investment Validator) model simulates 14,600 days across 40 years, incorporating realistic market cycles, black swan events (0.5% probability), and extreme stress scenarios including 99% revenue drops. Across 3,000+ simulation runs, the protocol demonstrated 100% sustainability with perfect equality maintenance. The paper details seven diversified revenue streams projected to scale from $87 million in Year 1 to $35.75 billion by Year 5, eliminating reliance on inflationary tokenomics. Technical innovations include autonomous validator bot systems that eliminate slashing risks, browser-based validation infrastructure, and deflationary token buyback mechanisms. The work represents PhD-level contributions to solving the DeFi Sustainability Trilemma, with planned submissions to leading academic journals in financial economics and computational economics.

K. Hu

This review explores the intersection of probability theory and data visualization in the domain of financial risk prediction. It examines how probabilistic models—such as Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), Monte Carlo simulation, stochastic processes, and Bayesian inference—serve as the backbone of uncertainty modeling in finance by reviewing previous studies. Simultaneously, it highlights the role of visualization in transforming abstract probability distributions into interpretable insights through dashboards, heatmaps, clustering, and interactive visual frameworks. Drawing on over 20 open-access sources, the review synthesizes applications across corporate profitability, systemic risk, portfolio optimization, credit default, exchange rate forecasting, ESG sustainability, start-up financing, and decentralized finance (DeFi). It concludes by identifying limitations—including data quality issues, computational complexity, interpretability challenges, and ethical/regulatory concerns—and proposes future research directions in robust probabilistic modeling, scalable explainable AI, standardized visualization practices, and fairness-aware risk systems. Together, probability and visualization provide complementary tools that are indispensable for navigating financial uncertainty in the 21st century.

Kera Protocol

This paper presents a novel gradient-based approach to probabilistic invariant monitoring in decentralized finance (DeFi) protocols.Unlike traditional reactive systems that adjust perceptions after violations occur, our framework implements a proactive closed-loop control system that influences external market dynamics through calibrated protocol actions.We introduce mathematical formulations for confidence gradient tracking, adaptive parameter calibration, and real-time feedback mechanisms that maintain protocol invariants through measurable influence on lender and borrower behavior.

Krekel, William Peter

This dissertation examines the evolving market microstructure of digital assets, focusing on transaction costs, liquidity provision returns, and the development of innovative exchange mechanisms. In three essays, the research provides empirical evidence on digital asset trading in both traditional and emerging decentralized market architectures. Each essay addresses previously unresolved questions, offering valuable insights for researchers, practitioners, and regulators to better understand and manage the benefits, costs, and risks of trading in digital asset markets.The first essay examines the cost of trading across digital assets in traditional centralized limit-order-book exchanges and a nascent, decentralized market architecture: the Automated Market Maker. By employing a novel methodology the study extends prior research that relies on less detailed, low-frequency information. The findings reveal transaction cost advantages for Automated Market Makers with remarkable stability across varying levels of market volatility, trading volume, and market capitalization. These results offer practical insights into execution venue selection and market design considerations.The second essay explores the evolution of Automated Market Makers, using the introduction of a new generation of these exchange architectures as a case study. In addition to documenting their technical advancements, the research shows that asset pairs migrate to the new Automated-Market-Maker models based on asset-specific fundamentals. The study makes key contributions through two experimental setups, demonstrating that reductions in inventory costs and the introduction of flexible fee tiers deliver welfare benefits for both liquidity demanders and providers. These findings enrich the broader discussion on market design and highlight the potential for innovative mechanisms to enhance efficiency in both decentralized and traditional financial systems.The third essay sheds light on liquidity provision in Automated Market Makers. Leveraging granular profitability data, the study finds that a small subset of liquidity providers dominate liquidity provision. These sophisticated agents achieve significantly higher absolute and relative profits compared to retail participants, while demonstrating a high level of skill. The emergence of these de-facto intermediaries challenges the decentralized finance ethos of disintermediation, highlighting that liquidity provision, even in decentralized markets, remains dominated by specialists. Understanding the composition of participants in these nascent markets is not only crucial for practitioners but also regulators, enabling them to develop targeted and effective policies that promote fair and competitive market environments.

Zhuo Chen, Gaoqiang Ji, He Yun, Lei Wu · 5 authors

Decentralized finance (DeFi) is experiencing rapid expansion. However, prevalent code reuse and limited open-source contributions have introduced significant challenges to the blockchain ecosystem, including plagiarism and the propagation of vulnerable code. Consequently, an effective and accurate similarity detection method for EVM bytecode is urgently needed to identify similar contracts. Traditional binary similarity detection methods are typically based on instruction stream or control flow graph (CFG), which have limitations on EVM bytecode due to specific features like low-level EVM bytecode and heavily-reused basic blocks. Moreover, the highly-diverse Solidity Compiler (Solc) versions further complicate accurate similarity detection. Motivated by these challenges, we propose a novel EVM bytecode representation called Stable-Semantic Graph (SSG), which captures relationships between 'stable instructions' (special instructions identified by our study). Moreover, we implement a prototype, Esim, which embeds SSG into matrices for similarity detection using a heterogeneous graph neural network. Esim demonstrates high accuracy in SSG construction, achieving F1-scores of 100% for control flow and 95.16% for data flow, and its similarity detection performance reaches 96.3% AUC, surpassing traditional approaches. Our large-scale study, analyzing 2,675,573 smart contracts on six EVM-compatible chains over a one-year period, also demonstrates that Esim outperforms the SOTA tool Etherscan in vulnerability search.

Saswati Ghosh, Shivnath Ghosh

Blockchain technology has emerged as a revolutionary paradigm for secure, transparent, and tamper-resistant data management. It offers a decentralized ledger where transactions are validated and recorded across a distributed network of nodes, eliminating the need for centralized authorities. Despite its widespread adoption across diverse domains—such as finance, supply chain, healthcare, and digital identity—blockchain still faces significant challenges in ensuring complete security and privacy. This paper addresses these challenges by proposing a novel security and privacy algorithm designed specifically to enhance blockchain resilience against evolving threats. The proposed approach integrates hybrid cryptography, pseudonymous identifiers, and an optimized consensus mechanism to achieve a balanced trade-off between security, privacy, and computational efficiency. The hybrid cryptographic model combines symmetric and asymmetric encryption techniques to safeguard transaction data at multiple layers. Symmetric encryption ensures fast and secure data exchange, while asymmetric keys are used for identity verification and secure key distribution. To further strengthen user anonymity, the algorithm incorporates pseudonymous identity management, which replaces permanent public keys with dynamically generated pseudonyms. These pseudonyms are refreshed periodically to prevent link ability between consecutive transactions, ensuring that individual identities remain hidden even if certain nodes or data patterns are compromised. Additionally, the optimized consensus protocol enhances transaction validation efficiency by reducing redundant computations and improving synchronization among nodes. This approach minimizes latency and energy consumption while maintaining strong resistance against consensus-based attacks such as 51% or Sybil attacks. Extensive simulations and experimental evaluations were conducted to measure the algorithm’s performance under various network conditions and adversarial scenarios. The results demonstrate that the proposed model significantly improves transaction validation speed and reduces cryptographic overhead compared to traditional Proof-of-Work and Proof-of-Stake systems.

Deni Teminyan

Decentralized finance (DeFi) uses smart contracts to automate payments, lending, and asset management, but current blockchains often suffer from slow, expensive, and energy-hungry execution. In this project, I explore a quantum-enhanced optimization framework for smart contract–based financial services. The main idea is to treat gas use, transaction ordering, and resource allocation as optimization problems that can be tackled by hybrid quantum–classical algorithms. Using a conceptual model, I map smart contract execution to cost functions suitable for the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE). I then compare, at a qualitative level, how these quantum-inspired approaches differ from classical heuristics in terms of expected throughput, latency, and cost. A focused literature review on quantum computing, blockchain scalability, and quantum-safe cryptography provides context for these ideas. The results suggest that quantum-enhanced optimization could reduce gas fees, improve transaction scheduling, and support more efficient consensus under heavy load. The project also discusses the need for post-quantum security so that future quantum computers do not undermine blockchain trust. Overall, the work outlines how quantum computing might contribute to faster, safer, and more sustainable automated financial systems.

Zewei Lin, Ting Chen, Jingwen Zhang, Zexu Wang · 7 authors

Decentralized Finance (DeFi) staking is one of the most prominent applications within the DeFi ecosystem, where DeFi projects enable users to stake tokens on the platform and reward participants with additional tokens. However, logical defects in DeFi staking could enable attackers to claim unwarranted rewards by manipulating reward amounts, repeatedly claiming rewards, or engaging in other malicious actions. To mitigate these threats, we conducted the first study focused on defining and detecting logical defects in DeFi staking. Through the analysis of 64 security incidents and 144 audit reports, we identified six distinct types of logical defects, each accompanied by detailed descriptions and code examples. Building on this empirical research, we developed SSR (Safeguarding Staking Reward), a static analysis tool designed to detect logical defects in DeFi staking contracts. SSR utilizes a large language model (LLM) to extract fundamental information about staking logic and constructs a DeFi staking model. It then identifies logical defects by analyzing the model and the associated semantic features. We constructed a ground truth dataset based on known security incidents and audit reports to evaluate the effectiveness of SSR. The results indicate that SSR achieves an overall precision of 92.31%, a recall of 87.92%, and an F1-score of 88.85%. Additionally, to assess the prevalence of logical defects in real-world smart contracts, we compiled a large-scale dataset of 15,992 DeFi staking contracts. SSR detected that 3,557 (22.24%) of these contracts contained at least one logical defect.

Jennifer Bala, Sikiru O. SUBAIRU, Noel M. DOGONYARO, Joseph A. OJENIYI · 5 authors

Blockchain technology, particularly Ethereum, has revolutionized decentralized finance by enabling transparent, secure, and programmable smart contracts. However, these same features have created avenues for financial crimes such as Ponzi schemes, where fraudulent actors exploit pseudonymity and the absence of centralized oversight to deceive investors. This study develops an optimized hybrid detection model that combines eXtreme Gradient Boosting (XGBoost) and Gated Recurrent Units (GRU) to identify Ponzi schemes in Ethereum transaction networks. The model integrates XGBoost’s capability for structured feature learning with GRU’s temporal sequence modeling to capture both static and dynamic behavioral patterns of smart contracts. Using a dataset of 3,866 labeled Ethereum contracts obtained from Kaggle, the research employed advanced preprocessing, temporal sequence enrichment, and class balancing through SMOTE-TS to mitigate data imbalance. Bidirectional optimization, incorporating attention-enhanced GRUs and Bayesian hyperparameter tuning for XGBoost, further improved learning performance and generalization. The model was evaluated using precision, recall, F1-score, ROC-AUC, and PR-AUC, achieving higher detection accuracy of 99% (F1-score = 0.945, ROC-AUC = 0.983) than standalone XGBoost or GRU models. Results demonstrate the hybrid model’s superior ability to detect temporal and statistical anomalies, reducing false negatives and improving early detection of fraudulent contracts. The approach contributes a scalable and interpretable framework for real-time Ponzi detection in blockchain ecosystems. This research not only enhances the reliability of Ethereum’s financial ecosystem but also offers regulators and developers a novel tool for proactive fraud prevention. Future work could extend this framework to multi-chain detection systems and real-time forensic monitoring.

Krithika Rao, Shakil Khan, Bruce Singh, Nagulapati Kiran · 5 authors

Regulatory sandboxes—controlled environments where firms test innovations under regulatory supervision—have been adopted globally to manage fintech and crypto experimentation. This paper compares sandbox approaches and policy effectiveness for decentralized finance (DeFi) across the European Union, the United States, and the Asia-Pacific. Using a mixed-methods design (document analysis, stakeholder reports, and an illustrative quantitative model), we assess objectives, design choices, risk controls, and outcomes (market access, investor protection, and innovation diffusion). Findings show the EU’s pan-European coordination aims to harmonize testing and legal clarity; the US displays fragmented, agency-led pilot initiatives with stronger enforcement posture; Asia-Pacific exhibits rapid, varied adoption with jurisdictional leaders (Singapore, Hong Kong, Australia) using sandboxes as precursors to more formal rulebooks. Policy effectiveness depends on clarity of legal scope, cross-agency coordination, and well-designed exit and scaling rules. We conclude with policy recommendations and a research agenda for empirically measuring sandbox effectiveness for DeFi.

Arthur Carvalho, Ewerton Vinícius Pereira da Silva, Gustavo Carvalho Hamade

This scientific article analyzes Law No. 14,478/2022, the “Legal Framework for Cryptocurrencies” in Brazil. Adopting a legal-dogmatic approach, the study maps the regulatory advances, such as the creation of an initial normative framework, the criminalization of certain conducts, and the formalization of consumer protection. Conversely, it explores the law’s limits and gaps, emphasizing the omission of asset segregation and the challenges posed by the decentralized nature of Decentralized Finance (DeFi) and tax uncertainties. A comparative analysis with the European Union’s MiCA Regulation contextualizes Brazil’s choice for a principles-based model. The study concludes that the law’s effectiveness will depend on infra-legal regulation and the legal system’s ability to adapt to the market’s dynamism.

Mohammed Al Ghafari, Badar Al Alawi, Idris Aal Jumaa, Salah Al Awaidy

Background/Objectives: Oman Vision 2040, the national blueprint for socio-economic transformation, aims to elevate the Sultanate to developed nation status, with the “Health” priority committed to building a “Leading Healthcare System with International Standards” via a Health in All Policies (HiAP) approach. This paper critically reviews Oman’s strategic health directions and implementation frameworks under Vision 2040, assessing their alignment with global Sustainable Development Goals (SDGs) and serving as a case model for health system transformation. Methods: This study employs a critical narrative synthesis based on a comprehensive literature search that included academic, official government reports, and international organization sources. The analysis is guided by the World Health Organization’s (WHO) Health Systems Framework, providing a structured interpretation of progress across its six building blocks. Results: Key interventions implemented include integrated governance (e.g., Committee for Managing and Regulating Healthcare), diversified health financing (e.g., public private partnership (PPPs), Health Endowment Foundation), and strategic digital transformation (e.g., Al-Shifa system, AI diagnostics). Performance metrics show progress, with a rise in the Legatum Prosperity Index ranking and an increase in the Community Satisfaction Rate. However, critical challenges persist, including resistance to change during governance restructuring, cybersecurity risks from digital adoption, and system fragmentation that complicates a unified Non-Communicable Disease (NCD) response. Conclusions: Oman’s integrated approach, emphasizing decentralization, quality improvement, and investment in preventive health and human capital, positions it for sustained progress. The transformation offers generalizable insights. Successfully realizing Vision 2040 demands rigorous, evidence-informed policymaking to effectively address equity implications and optimize resource allocation.

Guodong Yi, Zhihui Wu, Xinyu Zhang, Xiaocui Li

Federated learning (FL) offers a distributed approach for the collaborative training of machine learning models across decentralized clients while safeguarding data privacy. This characteristic makes FL well suited for privacy-sensitive fields such as healthcare and finance. However, addressing the heterogeneity caused by nonindependent and identically distributed (non-IID) data remains a significant challenge for traditional FL methods. To address these issues, the enhancing clustered federated learning with adaptive similarity (AS-CFL) algorithm, which dynamically forms client clusters based on model update similarity and uses a forward-incentive mechanism to improve collaborative training efficiency among similar clients, is proposed in this study. Experimental results on the MNIST and EMNIST datasets reveal that compared with baseline methods such as the CFL, IFCA, and FedAvg models, the AS-CFL algorithm achieves faster convergence—reducing the number of communication rounds by approximately 20%—while maintaining competitive accuracy, demonstrating its effectiveness in heterogeneous FL scenarios.

Yi Hu, Jinming Li, Kangning Gao, Zizhao Zhang · 6 authors

Multi-agent systems (MAS) have emerged as a critical paradigm for distributed problem-solving in complex environments. However, their deployment in mission-critical applications faces significant challenges regarding trust, security, and adversarial robustness. This paper presents TrustOrch, a novel dynamic trust-aware orchestration framework designed to enhance the resilience of multi-agent collaboration against adversarial attacks. TrustOrch introduces five key innovations: (1) a dynamic trust assessment mechanism that evaluates agent reliability in real-time using multi-dimensional metrics, (2) an adversary-aware orchestration strategy combining reinforcement learning and game theory to detect and mitigate prompt injection attacks, (3) an adaptive collaboration topology that dynamically adjusts agent communication structures based on task complexity and trust levels, (4) explainable decision tracing for complete audit chains, and (5) a layered security architecture leveraging blockchain technology for decentralized trust verification. Our experimental evaluation demonstrates that TrustOrch reduces collision rates by 62%, achieves 91.7% robustness under adversarial attacks, and reduces communication overhead by 39.8% compared to baseline approaches. The framework achieves robust performance under various adversarial scenarios while maintaining transparency and regulatory compliance, making it particularly suitable for deployment in high-risk domains such as finance, healthcare, and autonomous systems.

Stefanos Karakolias, Nikolaos Polyzos

Background: Decentralization in health systems enhances responsiveness and equity but is often accompanied by uneven implementation and resource disparities. Greece' health system has undergone successive phases of decentralization, culminating in a transformation in 2015 when regional health authorities (RHAs) assumed operational responsibility for public primary healthcare (PHC). This study presents the first comprehensive assessment of this transition, examining funding adequacy and resource allocation across RHAs. Methods: Financial and operational analyses were performed to assess disparities among RHAs and between RHAs and hospitals. Data were drawn from publicly available sources, including financial statements, reports from the Ministry of Health, and national statistics. The analysis examined patient visits, staffing levels, infrastructure, funding, labor productivity, and efficiency across health regions. Results: Between 2018 and 2023, patient visits declined at most RHAs. Staffing composition shifted toward nursing personnel, while medical staff numbers declined. Substantial intraregional and interregional disparities were observed in service utilization, staffing, infrastructure, funding, labor productivity, and efficiency. Hospitals continued to absorb a large share of PHC demand and funding, whereas RHA units held markedly fewer assets and received lower financial support. Funding imbalances among RHAs were evident, and the overall negative return on assets indicated systemic underfunding of public PHC. Conclusion: The ongoing decentralization of Greece's health system faces structural challenges, including overlapping territorial jurisdictions and uneven, occasionally insufficient, resource allocation. These challenges hinder progress toward health equity. Policy interventions should prioritize evidence-based resource allocation, standardized financing frameworks, and strengthened PHC integration to promote equitable and sustainable healthcare delivery under decentralized governance.

Next Move Strategy Consulting

The global Decentralized Finance (DeFi) Market is experiencing exponential growth, valued at USD 29.05 billion in 2024 and expected to reach USD 44.79 billion by 2025. By 2030, the market is projected to surge to USD 390.47 billion, expanding at an impressive CAGR of 54.2% from 2025 to 2030. This rapid expansion is driven by increasing financial inclusion needs, rising cryptocurrency adoption, and strong institutional interest. DeFi leverages blockchain technology to eliminate intermediaries and provide open, permissionless financial services such as lending, borrowing, trading, and yield generation. While high-growth potential and innovation define the industry, challenges related to smart contract security and regulatory uncertainties remain. However, the growing penetration of smartphones, expanding internet access, and large unbanked global populations provide significant market opportunities. This manuscript highlights the market dynamics, technological drivers, regional insights, challenges, and future outlook shaping the global decentralized finance landscape.

Zuroqi Mubarok, Himsar Silaban, Pandji Sukmana, T. Herry Rachmatsyah

This study examines the evolution and current challenges of Islamic education policy in Indonesia through qualitative document analysis, a PRISMA guided systematic review, and bibliometric mapping. Legal and regulatory texts, including Law No. 20 of 2003 (SISDIKNAS) and Ministry of Religious Affairs reports, were analyzed using the READ framework to trace thematic shifts. The review targeted 2021-2025 peer-reviewed literature on curriculum, financing, governance, and educator professionalism, with screening per PRISMA. Bibliometrics identified influential works and collaboration patterns. Findings show a shift from centralized control to decentralized, locally adaptive policy. Innovations include integrated curricula, sharia-compliant financing, and strengthened teacher development, yet disparities in administrative capacity and resource allocation persist, yielding uneven quality assurance. The study argues that harmonizing national standards with regional contexts, enhancing inter-ministerial coordination, and embedding maqasid al-Shariah principles are pivotal for equity and accountability. Future research should evaluate digital governance and financing models for scalability and sustained impact. Keywords: Islamic education policy, Qualitative document analysis, PRISMA, Bibliometrics, Curriculum

A. Khan, Shahzeb Muhammad, Muhammad Rizwan

Grounded into Innovation Diffusion Theory and Technology Acceptance Model, the purpose of this study was to evaluate the impact of AI-powered financial services on financial access in the Saudi Arabian fintech sector. To achieve this aim, the research employed SEM analysis on the collected data from 194employees working in the departments related to AI-based services, staff members of fintech firms, and owners of small enterprises who use digital financial solutions in Riyadh, Jeddah, and Dammam. The results reveal that AI-based robo-advisory platforms, fraud detection, and credit scoring servicessignificantly improved financial access demonstrating that AI adoption in financial services can play a transformative role in promoting inclusion and reducing barriers for underserved populations whereas AI-based personalized banking solutions showed insignificant impact suggesting that while personalization may enhance user satisfaction or loyalty, it does not directly translate into increased access to financial services. In practical terms, the findings imply that fintech companies and financial institutions should prioritize AI-enabled services as a means of expanding access to professional financial advice which requiresa multi-stakeholder approach, where fintech firms, regulators, and policymakers collaborate to maximize the benefits of AI-powered financial services while minimizing associated risks. Furtherresearch should be carried out adopting longitudinal design and mixed methodology to study the role of emerging technologies such as blockchain-based identity verification, AI-driven insurance, or decentralized finance platforms on financial access.

Yakhshiboyev, R.E.

The digitalization of global economic relations has redefined the foundations of consumption, investment, and financial intermediation, positioning e-commerce and digital finance as central pillars of the contemporary economic model. The rapid integration of online trade platforms, fintech ecosystems, and algorithmic payment systems has not only transformed consumer behavior but also reshaped the mechanisms of capital formation and resource distribution. Ecommerce functions as an accelerator of market accessibility and competition, while digital finance provides the structural infrastructure necessary for transactional transparency, financial inclusion, and liquidity circulation in data-driven markets. In emerging economies, these instruments collectively stimulate entrepreneurial activity, reduce transaction costs, and expand cross-border investment flows. The research emphasizes that the synergy between digital trade and financial technologies generates a new consumption–investment paradigm characterized by personalization, real-time decision-making, and decentralized trust mechanisms. At the same time, the sustainability of this paradigm depends on the robustness of digital infrastructure, cybersecurity frameworks, and institutional adaptability to technological disruption. The study concludes that e-commerce and digital finance are not isolated innovations but interdependent drivers of structural modernization that integrate consumer dynamics with investment behavior, forming the analytical nucleus of the digital economy.

Rogers Rugeiyamu

Abstract Although decentralization is frequently used to improve service delivery and development, the impact of symmetrical fiscal decentralization on local government authorities ( lga s) and fair development has not been thoroughly investigated. Whilst symmetrical decentralization allows lga s to collect revenue and finance development projects, there are disparities among lga s in their ability to collect sufficient revenue and implement development projects. This study goes beyond prior research by investigating the way differences in the financial capacity of lga s affect development in various regions of Tanzania using a review of recent audit reports and studies. Results indicate that while certain lga s have shown a strong ability to collect revenue, others struggle considerably, due to factors such as historical background, revenue sources, population variations, urban-rural divides, and citizen awareness. Furthermore, the intergovernmental transfers, intended as an equalization strategy, have led to lga s remaining highly dependent on the central government and subject to the central government’s financial capacity. This study concludes that symmetrical fiscal decentralization in Tanzania has not achieved its intended goals. Despite some lga s over-collecting revenue, most remain heavily dependent on intergovernmental transfers, which are inconsistently distributed and often politically influenced, thus intensifying uneven development trends. The study recommends updating the 1998 policy to integrate asymmetrical decentralization, amending the Local Government Acts of 1982, building capacity to lga s, and shifting the central government’s role from controller to supporter.