Ahmad Musamih, Ibrar Yaqoob, Khaled Salah, Raja Jayaraman · 5 authors
Large Language Models (LLMs) are increasingly embedded in intelligent systems across domains such as healthcare, finance, and smart infrastructure. However, their reliance on centralized data pipelines raises unresolved challenges concerning provenance, accountability, and verifiable trust. As the demand for transparent and regulation-aligned AI grows, these challenges have become central to the responsible deployment of intelligent systems. This review examines how blockchain technology can address them by introducing decentralized integrity, immutable audit trails, and cryptographic verification into the LLM lifecycle. Through a structured synthesis of current research, we identify conceptual and architectural gaps that limit trustworthy data management, inference authentication, and explainability. To bridge these gaps, a methodological framework is proposed that integrates blockchain mechanisms across the LLM pipeline using smart contracts, Merkle-based commitments, and decentralized storage. The framework’s feasibility is demonstrated through an illustrative prototype, confirming its practical applicability for building verifiable and transparent AI infrastructures. We further outline application domains such as healthcare, smart cities, Industry 4.0, and supply-chain management, where blockchain-anchored LLMs can enhance auditability and regulatory compliance. The review concludes by highlighting key insights and challenges for future research, emphasizing the need for decentralized attestation models, scalable verification protocols, and governance mechanisms that advance accountable and privacy-preserving intelligent systems.
Open access
Artificial Intelligence in Healthcare and Education
Kassem Danach, Hassan Rkein, Ahmad Farroukh, Ziad E. L. Balaa · 5 authors
The static and hard-coded logic of smart contracts in Decentralized Finance (DeFi) platforms significantly limits their adaptability in dynamic and volatile market environments. To address this challenge, we propose a novel hyper-heuristic driven framework that enables real-time rule optimization within smart contracts, thereby enhancing responsiveness, gas efficiency, and operational robustness. The framework features a two-layer architecture: a reinforcement learning-based high-level controller selects appropriate low-level rule heuristics from a domain-specific library based on evolving transaction contexts and on-chain data. Implemented and evaluated on Uniswap v2 and Aave v3 protocols, the system dynamically optimizes parameters such as slippage tolerance, gas usage thresholds, and loan-to-value ratios. Experimental results on real-world datasets show significant performance improvements, including a 45.6% increase in transaction success rate, 28.3% reduction in average gas consumption, and 38.4% drop in liquidation events under market stress scenarios. This research demonstrates the feasibility and advantages of embedding intelligent, adaptive decision-making mechanisms within DeFi smart contracts, opening new pathways toward autonomous, resilient, and regulation-aligned blockchain systems.
The convergence of artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics is transforming the governance, sustainability, and resilience of modern banking ecosystems. This study provides a multivariate bibliometric analysis using Principal Component Analysis (PCA) of research indexed in Scopus and Web of Science to explore how decentralized digital infrastructures and AI-driven analytical capabilities contribute to sustainable financial development, transparent governance, and climate-resilient digital societies. Findings indicate a rapid increase in interdisciplinary work integrating Distributed Ledger Technology (DLT) with large-scale data processing, federated learning, privacy-preserving computation, and intelligent automation—tools that can enhance financial inclusion, regulatory integrity, and environmental risk management. Keyword network analyses reveal blockchain’s growing role in improving data provenance, security, and trust—key governance dimensions for sustainable and resilient financial systems—while AI/ML and big data analytics dominate research on predictive intelligence, ESG-related risk modeling, customer well-being analytics, and real-time decision support for sustainable finance. Comparative analyses show distinct emphases: Web of Science highlights decentralized architectures, consensus mechanisms, and smart contracts relevant to transparent financial governance, whereas Scopus emphasizes customer-centered analytics, natural language processing, and high-throughput data environments supporting inclusive and equitable financial services. Patterns of global collaboration demonstrate strong internationalization, with Europe, China, and the United States emerging as key hubs in shaping sustainable and digitally resilient banking infrastructures. By mapping intellectual, technological, and collaborative structures, this study clarifies how decentralized intelligence—enabled by the fusion of AI/ML, blockchain, and big data—supports secure, scalable, and sustainability-driven financial ecosystems. The results identify critical research pathways for strengthening financial governance, enhancing climate and social resilience, and advancing digital transformation, which contributes to more inclusive, equitable, and sustainable societies.
The urban administration in Pakistan has transformed as a result of political and economic shifts. The urban government in Pakistan has been influenced by external financing, which is a reflection of institutional reforms, fiscal decentralization, and the priorities of global development. Over the course of the last three decades, Pakistan's urban management has transitioned from a centralized bureaucratic authority to fragmented local governance systems that are shaped by donor-driven projects and conditional cash inflows. An in-depth analysis of how multilateral development banks and bilateral aid influence urban policy, infrastructure, and service delivery is presented in this specific piece of writing. The evidence demonstrates that the use of external financing has hastened the process of urban modernization while simultaneously exacerbating governance problems such as policy incoherence, accountability deficiencies, and socio-spatial inequities. In this study, political economics research and urban planning perspectives are combined in order to investigate how external funding mechanisms influence the capacities of local governments and the transformation of urban infrastructure in Pakistan's fast-growing cities. The findings highlight the necessity of having governance structures that are adaptable and, in a position, to strike a balance between local interests and global urban finance strategy.
This study systematically examines the transformative role of Artificial Intelligence (AI) in addressing the persistent challenges of blockchain technology across protocols, smart contracts, and distributed ledger management. Although blockchain offers decentralization, immutability, and transparency, its broader adoption remains constrained by scalability limitations, security vulnerabilities, inefficient consensus mechanisms, and the complexity of contract design and auditing. The findings of this review demonstrate that AI provides promising solutions to these barriers. Reinforcement learning (RL) applied to Proof-of-Stake reduced consensus latency by 30-50%, while NLP-based smart contracts lowered vulnerabilities by up to 40%, though both approaches introduced new concerns related to energy overheads and auditability. In addition, intelligent algorithms enhance ledger efficiency and data analytics, supporting more scalable and secure transaction processing. Drawing on 28 peer-reviewed studies published between 2018 and 2024, and guided by the PRISMA 2020 framework, this paper synthesizes state-of-the-art research, maps sector-specific applications in finance, healthcare, and supply chain management, and highlights unresolved gaps in ethics, reproducibility, and regulatory compliance. Notably, only 12% of the reviewed studies validated their approaches on live networks underscoring the gap between simulation-driven research and real-world deployment. The discussion culminates in the AI–Blockchain Interaction Model (AIBIM), a conceptual framework that systematizes synergies across consensus, contract, and application layers. By integrating empirical insights with critical evaluation, this work emphasizes the interdisciplinary nature of AI–blockchain research and provides actionable directions for advancing decentralized, scalable, and ethically aligned systems. This synthesis provides actionable insights for developers, regulators, and researchers in deploying AI-blockchain systems across finance, healthcare, and supply chains.
Hisham Mohamed Hassan Al Hammadi, Muhammad Hafiz bin Badarulzaman, Abdulaziz Fahmi Omar Faqera
The regulatory architecture governing cryptocurrencies and virtual assets in the United Arab Emirates has expanded markedly through Federal Decree-Law No. 20 of 2018, Cabinet Decision No. 10 of 2019, Federal Decree-Law No. 46 of 2021, and Dubai Law No. 4 of 2022, reflecting the state’s ambition to position itself as a leading digital finance hub while addressing money laundering risks. Notwithstanding this legislative progress, significant challenges persist, stemming from the decentralized and pseudonymous nature of cryptocurrencies, fragmented institutional oversight across federal and emirate-level authorities, and constrained supervisory capacity for real-time monitoring. Existing scholarship has largely overlooked the interaction between legal design and institutional enforcement dynamics within the UAE’s cryptocurrency regime, creating a critical gap this study addresses. The study critically evaluates the legal and institutional frameworks governing cryptocurrencies, examines enforcement and compliance vulnerabilities within AML mechanisms, and assesses regulatory risks associated with cryptocurrency market adoption. Employing an exploratory qualitative doctrinal methodology, the analysis systematically examines primary legislation alongside secondary sources drawn from high-impact journals, authoritative monographs, and institutional reports, subjected to rigorous thematic analysis. Guided by Institutional Theory, the findings demonstrate that while the UAE’s framework is normatively comprehensive, enforcement effectiveness is undermined by coordination deficits and technological constraints. The study advances targeted recommendations to enhance regulatory coherence, institutional integration, and risk-based supervision, contributing to legal, financial regulation, international governance, and digital risk studies, while identifying directions for future comparative inquiry.
Meme coins have become extremely popular in the cryptocurrency market, but they also carry a high level of risk. Many of these projects rely on social media hype and community excitement, yet a large number eventually turn out to be scams where developers steal investor funds and abandon the project, commonly known as rug pulls. This paper presents a smart analysis tool designed to help investors identify such risky meme coin projects before financial loss occurs. The proposed system examines both smart contract behavior and market-related factors, including ownership control, liquidity locking, token distribution, and developer wallet activity. The tool was tested on real-world meme coins, including well-known legitimate projects as well as confirmed scam tokens. The results show that the system is able to accurately distinguish between safe and high-risk projects. This approach provides a practical and effective way to improve investor safety in the rapidly evolving decentralized finance ecosystem
Francis Chigozie Emmanuel, Ogaziechi Tobechi Anold, Obidinma Christian Alozie, Ikenna Tonna Adiele
The global freelance economy has experienced rapid growth, yet existing payment and escrow systems remain constrained by structural inefficiencies inherent in both centralized fiat-based and decentralized cryptocurrency-based models. Centralized escrow systems, while widely adopted due to their regulatory compliance and usability, suffer from custodial opacity, information asymmetry, high transaction costs, and limited verifiability. Conversely, purely decentralized blockchain-based escrow systems offer transparency and trust-minimized execution through smart contracts but face barriers including cryptocurrency price volatility, limited fiat integration, steep technical learning curves, and inadequate dispute resolution mechanisms for subjective deliverables. This article, a hybrid escrow system integrates traditional fiat payment infrastructure with decentralized Ethereum-compatible smart contract execution. The system adopts a three-layer architecture comprising a centralized service layer, a middleware synchronization layer, and a decentralized execution layer. A Finite State Machine (FSM) model governs escrow state transitions across both fiat-funded and cryptocurrency-funded transactions, ensuring determinism, auditability, and consistency. The system further incorporates a human-in-the-loop dispute resolution framework anchored to blockchain execution, enabling fair and transparent adjudication of subjective conflicts. Evaluation results demonstrate that the proposed hybrid architecture successfully bridges the gap between traditional finance and decentralized systems. The system achieved 100% correct FSM state enforcement with zero unauthorized fund releases across all test scenarios. Fiat-funded contracts were synchronized to the blockchain with an average latency of 8.4 seconds, while cryptocurrency-funded contracts confirmed on-chain within a median of 3.2 seconds on the Polygon testnet. All three dispute resolution outcomes were correctly enforced on-chain within an average of 5.1 seconds following adjudication, and API response times remained below 420 milliseconds under concurrent user loads. An ablation study further confirmed that all three architectural layers are individually necessary, as removing any single layer degraded transparency, payment flexibility, dispute resolution capability, or user accessibility. This research contributes a scalable and adaptable hybrid escrow blueprint applicable to fintech development, digital labour platforms, and cross-border payment systems.
This paper presents a comprehensive structural analysis of cryptocurrency derivative markets spanning January 2019 to December 2024, covering Bitcoin (BTC), Ethereum (ETH), and six additional tokens across over 2.83 billion high-frequency transactions on eight major centralized exchanges and three decentralized finance (DeFi) derivative protocols. Using a theoretically grounded multi-method framework—comprising Vector Error Correction Models (VECM), Hasbrouck (1995) and Gonzalo-Granger (1995) information share decompositions, Heston (1993) and rough volatility (Gatheral et al., 2018) stochastic models, DCC-GARCH(1,1) augmented with realized kernel estimators, MIDAS regressions linking high-frequency derivative signals to lowerfrequency on-chain variables, and panel quantile regressions for cross-sectional volatility risk—we deliver six primary empirical contributions. First, perpetual swap markets consistently dominate spot markets in price discovery, contributing 63.4% (BTC) and 58.7% (ETH) of price-efficient information on average, rising to 72.1% and 68.4%, respectively, during the top quartile of volatility days—consistent with informed-agent migration to leveraged venues. Second, the Heston leverage correlation estimate ρ = −0.61 for BTC and ρ = −0.73 for ETH reflects asymmetric tail risk demand rather than balance-sheet leverage, with the implied volatility smirk's left-tail slope strongly cointegrated with funding-rate deviations (r = −0.54, p < 0.001). Third, we estimate a time-varying variance risk premium averaging 14.8 (BTC) and 19.3 (ETH) annualized variance percentage points; panel regressions reveal that on-chain network congestion fees retain significant incremental explanatory power after controlling for VIX, DXY, and credit spreads—a novel identification of a blockchain-specific volatility channel. Fourth, rough volatility models (Hurst exponent H ≈ 0.08 for BTC) significantly outperform classical Heston specifications in fitting near-term implied volatility smiles, with RMSPE reductions of 31.7% for one-week expiry options. Fifth, CME Bitcoin Futures introduction produced a structural break in arbitrage efficiency, reducing basis mean-reversion halflives by 41.2% and lowering adverse-selection costs by 18.6 basis points. Sixth, on-chain DeFi perpetual protocols (GMX v2, dYdX v4) exhibit significantly higher adverse selection costs and lower price discovery shares (mean IS = 0.24) relative to centralized counterparts, but display timevarying convergence during U.S. regulatory uncertainty episodes. Our findings deliver unified implications for derivative pricing theory, risk management, and the architectural design of regulated cryptocurrency derivative markets.
The 2026 cryptocurrency market cycle has witnessed the emergence of a novel asset class that defies traditional financial categorization: the AI-Integrated Meme Asset (AIMA). This report provides an exhaustive analysis of this phenomenon, utilizing the trajectory of Act I: The AI Prophecy ($ACT) as a primary case study. We posit that the convergence of large language models (LLMs) and decentralized community coordination has created a new "meta" for liquidity formation, characterized by the transition from static meme imagery to dynamic, agentic interaction. Central to this analysis are two theoretical frameworks proposed herein: the "Spring Effect," a market mechanics model describing the kinetic release of accumulated volatility following suppression events, and "Cognitive HODLing," a behavioral finance concept drawing on Social Identity Theory and Kahneman’s Prospect Theory to explain the rigidity of social consensus in the face of founder betrayal. Through a synthesis of on-chain data, behavioral analysis, and the philosophical frameworks of Vitalik Buterin and Satoshi Nakamoto, this report argues that $ACT represents the pioneer of a "Decentralized Agentic Economy," where value is derived not from revenue, but from the resilience of the human-AI social fabric.
The introduction of block chain-supported investment tools like cryptocurrencies, DeFi platforms and tokenized assets has brought new decentralized, clear and exciting choices to the world of finance. As the use of impact investing expands all over the world, learning how investors view these projects matters for their continued success. This study investigates the motivations, risk perceptions, and decision-making processes of investors engaging with blockchain-based financial products. Drawing on behavioral finance theories and existing literature, it explores how psychological biases, technological literacy, and external influences such as social media and regulatory shifts shape investor actions. The research identifies key gaps, including the limited focus on non-cryptocurrency products, underdeveloped behavioral models, and insufficient attention to demographic and longitudinal factors. By addressing these gaps, this study aims to provide actionable insights for policymakers, financial institutions, and technology developers, contributing to a deeper understanding of investor dynamics in the blockchain era.
We develop an agent-based model in which inflation emerges from decentralized price-setting and credit-financed production in an endogenous-money economy. Firms operate under working-capital constraints, form market-based price expectations through heterogeneous adaptive learning, and set prices via cost-plus rules with endogenous mark-ups. Bank lending simultaneously creates deposits, while heterogeneous lending rates and credit rationing shape firms' financing costs and, through unit costs, their pricing decisions. The economy features interacting production and credit networks: intermediate-input linkages propagate cost shocks across supply chains, while bank--firm relationships transmit financial conditions across firms. The interaction of network-based pass-through, state-dependent pricing incentives, and evolving credit conditions generates inflationary regimes, including episodes driven by pricing cascades and feedback loops.
Federated Learning (FL) has emerged as a transformative paradigm for distributed machine learning, enabling model training across decentralized edge devices while preserving data privacy. This methodology is critical for sectors handling sensitive information, such as finance, healthcare, and the Internet of Things (IoT). Despite its benefits, the coordination and communication overhead between distributed nodes remain significant challenges. This paper evaluates the efficacy of REST and GraphQL API architectures in facilitating FL workflows. While REST APIs are favored for their statelessness and simplicity, GraphQL offers enhanced flexibility and efficiency by enabling precise data fetching—a vital feature for bandwidth-constrained decentralized systems. We provide a comparative analysis of these paradigms across performance, security, and scalability metrics, specifically regarding data synchronization and model aggregation. Finally, we propose design best practices for developing APIs that support robust, compliant, and efficient federated prediction systems.
The proliferation of agentic artificial intelligence systems—characterized by autonomous goal-seeking, tool use, and multi-agent coordination—presents unprecedented challenges to existing legal and financial regulatory frameworks. While traditional AI governance has focused on model-level alignment through training-time interventions such as Reinforcement Learning from Human Feedback (RLHF), the deployment of large language models (LLMs) as persistent agents embedded within socio-technical systems necessitates a paradigm shift toward institutional governance structures. This paper examines the intersection of agentic AI, Retrieval-Augmented Generation (RAG), and their implications for legal accountability and financial market integrity. Through a comprehensive analysis of the Institutional AI framework proposed by Pierucci et al. [1], we argue that alignment must be reconceptualized as a mechanism design problem involving runtime governance graphs, sanction functions, and observable behavioral constraints rather than internalized constitutional values. We address the critical deficit identified by LeCun regarding the absence of world models in current agents, demonstrating how RAG architectures function as externalized epistemic infrastructure that grounds agentic cognition in verifiable data repositories. The paper subsequently interrogates the legal implications of these systems under the European Union's Artificial Intelligence Act (EU AI Act) and the regulatory thresholds established by the Financial Conduct Authority (FCA) and European Central Bank (ECB), proposing justified compliance boundaries for high-risk financial applications. Furthermore, we acknowledge significant governance gaps within Decentralized Finance (DeFi) protocols where institutional oversight mechanisms face structural limitations. By synthesizing technical insights from multi-agent systems, constitutional AI limitations, and offensive security frameworks, this work advances a jurisprudential foundation for agentic AI that prioritizes defensible audit trails, incentive-compatible compliance, and systemic stability over opaque internal alignment guarantees. The analysis concludes that the future of AI governance lies not in perfecting isolated model behavior, but in architecting institutional environments where compliant behavior emerges as the dominant strategy through carefully calibrated payoff landscapes.