Decentralised Finance (DeFi) applications involve a large volume of funds and exhibit diverse user behaviours, including malicious activities such as smart contract exploits and financial scams. Existing approaches struggle to capture complex behaviours. To address this gap, we propose a general Blockchain User Behaviour Analysis (BUBA) pipeline for DeFi security. The pipeline presents an automated action formation process that takes blockchain transactions as inputs and outputs user actions. In addition, BUBA introduces a dual Graph Neural Network (GNN) model that jointly captures user action features, contract and token interactions, and heterogeneous graph structure information to produce rich behavioural embeddings, enabling effective clustering of semantically meaningful user behaviours. We evaluate the proposed pipeline on Uniswap V3, where it outperforms baseline methods in identifying and differentiating suspicious behaviours. A further case study on Sushiswap V2 demonstrates the generalisability of the pipeline across DeFi applications.
The article carries out a comprehensive theoretical study of the evolutionary transformation of the world financial architecture (SFA) in the context of changes in global technological patterns. The authors analyze the historical retrospective of financial globalization, starting from the moment of laying the foundation of the Bretton Woods system, which determined the hierarchical, dollar-centric structure of international settlements for decades to come. mediated by banking institutions and supranational regulators, at the present stage, is facing a crisis of institutional efficiency caused by the accumulation of global imbalances and the digital divide. Particular attention is paid to conceptually rethinking the transition from the Jamaican monetary system to a new era of âalgorithmic orderâ based on Web3 technologies. It has been established that the key feature of modern transformation is the decentralization of financial relations, where the function of trust is transferred from the institutional level (state and bank guarantees) to the protocol level (distributed ledgers, smart contracts). The authors argue that Web3 does not just modernize payment instruments, but forms a fundamentally new logic of international economic interaction â an ecosystem where capital acquires programmable properties, and cross-border transactions are carried out in real time without the involvement of traditional correspondent networks. The paper details the impact of decentralized finance (DeFi) on the changing role of national currencies and central banks. The thesis that the algorithmization of the financial space requires the development of new approaches to international regulation, since traditional methods of capital control lose their effectiveness in the conditions of anonymous decentralized networks, is substantiated. A forecast is made for the formation of a hybrid architecture of the future, where âfiatâ and âalgorithmicâ orders will coexist through interoperability mechanisms. The article aims to lay a theoretical basis for further study of the mechanisms of adaptation of national economies, in particular Ukraine, to the challenges of global digitalization of finance.
Abdul-Wahab Tahiru, Silas Uwumborge Takal, Samuel Jerry Cobbina, Wilhemina Asare ¡ 5 authors
Northern Ghana faces acute climate vulnerabilities, yet adaptation pathways remain fragmented and poorly synthesized. This study systematically reviewed 15 peer-reviewed literature covering the Savannah, Upper East, Upper West, North East, and Northern regions to assess climate impacts on agro-pastoral communities, evaluate existing adaptation strategies, and explore policy implications. Findings reveal that erratic rainfall, prolonged droughts, rising temperatures, and land degradation have undermined food security and livestock systems, while increasing pest outbreaks and intensifying farmerâpastoralist conflicts. Communities have responded through water harvesting, livelihood diversification (including agroforestry, shea processing, small livestock rearing, and seasonal migration), and reliance on indigenous knowledge systems. However, these strategies remain constrained by inadequate finance, weak infrastructure such as faulty hand pumps, gender inequalities, and limited integration with formal climate services. The review underscores the need for coherent policies that expand decentralized water infrastructure, scale climate-smart financing, institutionalize conflict-resolution platforms, and embed gender-responsive and indigenous approaches into national adaptation planning. Strengthening the interface between local innovation and formal governance is critical for building inclusive and scalable resilience across Northern Ghanaâs agro-pastoral systems.
The accelerating digitization of financial services has transformed global economic ecosystems while simultaneously amplifying the scale, speed, and structural complexity of financial fraud. Real-time payments, open banking infrastructures, fintech platforms, and decentralized finance environments have expanded transactional connectivity, creating highly dynamic and interconnected risk landscapes. Conventional rule-based and standalone machine learning systems demonstrate limited effectiveness against adaptive adversaries, coordinated fraud rings, synthetic identity schemes, and cross-platform laundering networks. Advanced detection strategies require intelligent architectures capable of modeling temporal behavior, relational dependencies, and large-scale streaming data within production-grade environments. This chapter presents a comprehensive framework for next-generation financial fraud detection integrating Artificial Intelligence, Deep Learning, and Graph Analytics. The discussion synthesizes supervised, unsupervised, and semi-supervised learning approaches with sequential deep learning architectures, transformer-based models, and graph neural networks for network-aware inference. Emphasis is placed on hierarchical multi-stage detection systems, cloud-native deployment strategies, adversarial robustness, privacy-preserving computation, and real-world validation methodologies. Critical challenges such as extreme class imbalance, concept drift, scalability of graph processing, explainability under regulatory constraints, and cross-institution collaboration are systematically examined. A unified hybrid AIâgraph intelligence architecture is articulated to address both transactional anomalies and coordinated fraud ecosystems. The chapter contributes a structured taxonomy of modern financial fraud, an integrated modeling perspective combining temporal and structural intelligence, and a deployment-oriented evaluation framework aligned with real-world financial operations. By bridging theoretical advancements with production-grade implementation considerations, this work establishes a rigorous foundation for scalable, interpretable, and resilient fraud detection systems within evolving digital financial infrastructures.
The rapid expansion of digital banking, mobile payments, decentralized finance, and cross-border electronic transactions has fundamentally transformed global financial ecosystems while intensifying exposure to sophisticated cyber threats, fraud networks, synthetic identity schemes, and money laundering operations. Conventional rule-based security infrastructures lack the adaptability required to counter dynamic and large-scale financial crimes. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative enablers of intelligent financial security, supporting real-time fraud detection, behavioral authentication, transaction risk scoring, and regulatory compliance automation. This chapter presents a comprehensive examination of advanced machine learning techniquesâincluding deep learning, graph neural networks, anomaly detection models, and reinforcement learningâfor securing digital transactions and identifying coordinated fraud rings within complex financial networks. Integration of AI with blockchain consensus mechanisms, cryptographic infrastructures, and Regulatory Technology (RegTech) platforms is analyzed to demonstrate how adaptive intelligence enhances network resilience, transparency, and operational efficiency. Emphasis is placed on explainable and fairness-aware AI frameworks to ensure ethical accountability, regulatory alignment, and bias mitigation in automated financial decision systems. Privacy-preserving approaches such as federated learning and secure multi-party computation are also explored to address data governance constraints in cross-institutional collaboration. The chapter consolidates emerging research directions, identifies persistent technical and ethical challenges, and proposes an integrated AI-driven security architecture for scalable and trustworthy digital financial ecosystems.
The rapid digitalization of financial ecosystems has transformed online payments, transaction processing, and investment management into highly interconnected, data-intensive infrastructures. This transformation has simultaneously expanded exposure to cyber fraud, money laundering, identity theft, and market volatility, necessitating intelligent and adaptive security mechanisms. Advanced artificial intelligence techniques, including machine learning, deep learning, reinforcement learning, and graph-based analytics, have emerged as critical enablers of secure payment processing, real-time fraud detection, and predictive financial forecasting. Intelligent architectures embedded within online payment systems facilitate dynamic risk scoring, anomaly detection, behavioral profiling, and automated decision-making under strict latency constraints. This chapter presents a comprehensive examination of intelligent system frameworks for digital finance, integrating scalable cloud-based deployment, blockchain-enabled transaction integrity, explainable AI for regulatory compliance, and synthetic data generation for fraud simulation. Reinforcement learning approaches for portfolio optimization and risk-aware forecasting are analyzed to highlight adaptive investment strategies in volatile markets. Emphasis is placed on addressing class imbalance, adversarial threats, model interpretability, privacy preservation, and governance challenges within automated financial infrastructures. Emerging research directions such as federated learning, decentralized finance intelligence, and AI-driven anti-money laundering systems are also discussed to outline future technological trajectories. The presented synthesis establishes a structured foundation for developing secure, transparent, and scalable intelligent financial ecosystems aligned with regulatory and operational requirements of modern digital economies.
This study examines the determinants of transparency and accountability in village financial management, focusing on the roles of village facilitator competence, village government commitment, and oversight by the Village Consultative Body (BPD). Grounded in good governance and principalâagent theory, the study addresses persistent governance challenges at the village level, where substantial public funds are managed amid limited administrative capacity and uneven institutional oversight. Using a quantitative design, primary data were collected through a structured survey administered to 510 respondents from 85 villages in Donggala and Sigi Regencies, Indonesia. The data were analyzed using Structural Equation ModelingâPartial Least Squares (SEM-PLS), enabling simultaneous testing of direct and mediating relationships among governance variables. The results show that village facilitator competence, village government commitment, and BPD oversight each have significant positive effects on financial transparency and accountability. Importantly, transparency plays a central mediating role, indicating that the effects of competence, commitment, and oversight on accountability are largely transmitted through improved information disclosure. These findings confirm that accountability in village financial management is difficult to achieve without adequate transparency mechanisms that reduce information asymmetry between village governments and the community. Theoretically, this study extends the application of good governance and principalâagent theory to the village governance context by empirically validating transparency as a key governance mechanism linking institutional factors to accountability outcomes. Practically, the findings highlight the importance of strengthening facilitator capacity, fostering integrity-driven leadership commitment, and enhancing the effectiveness of BPD supervision as integrated strategies to improve village financial governance. By providing evidence from a large-sample empirical study, this research offers insights for policymakers and practitioners seeking to promote transparent and accountable village finance management in decentralized governance systems.
Moses Arthur Baidoo, Wang ZhiCheng, Liu Qi, Zhou ShuMin ¡ 6 authors
Access to reliable electricity remains a pressing challenge in Sub-Saharan Africa, particularly in rural areas where over 600 million people live without power. This paper explores the potential of decentralized solar energy systems; such as solar home systems, mini-grids, and solar-powered appliances in addressing energy access challenges across rural Sub-Saharan Africa. While these systems offer clean, reliable, and scalable alternatives to conventional grid expansion, their adoption is constrained by regulatory uncertainty, limited financing options, and local capacity gaps. Drawing on case studies from five countries, the paper examines how recent innovations -including mobile-based Pay-as-you-go (PAYG) financing, hybrid renewable systems, and improved energy storage technologies are reshaping energy access models. It also outlines policy recommendations aimed at strengthening regulatory coherence, promoting regional cooperation, and enhancing sustainability. Ultimately, the study highlights how decentralized solar solutions can contribute to long-term environmental, financial, and social resilience, with direct implications for poverty alleviation and inclusive rural development in the region.
Severin Bonnet, Jan-Gero Alexander Hannemann, Frank Teuteberg
Abstract In this paper, we report on the initial stage of a design science research (âDSRâ) project aimed at establishing design principles for DeFAI (the intersection of DeFi and AI) generative AI-based chatbot assistants tailored to decentralized finance (âDeFiâ). Addressing challenges such as user trust, data privacy and security, and regulatory compliance, we conducted a targeted literature review, expert interviews, as well as iterative prototype ideation and evaluation to derive three design principles: (1) Human-Centered Design, (2) Resilience and Interoperability, and (3) DeFi-Native User Experience Together, these principles operationalize general chatbot design guidance for DeFi contexts characterized by self-custody, irreversible transactions, and adversarial risk environments. We demonstrate these principles through DeFAIGuide, a mockup that illustrates how technical barriers in DeFi can be abstracted to enhance accessibility for novice users while also offering advanced features for expert users. Our study contributes actionable design knowledge to support the future development of DeFAI solutions that advance inclusivity, security, privacy, and self-sovereignty, paving the way for a more transparent and participatory financial future.
This paper explores how social influence and peer networks shape the adoption of cryptocurrencies and decentralized finance (DeFi) platforms. Drawing from qualitative interviews and social theory, the study examines how interpersonal communication, social media influence, and online communities impact user behavior. Findings reveal that peer endorsement and communal learning are strong drivers of trust and experimentation in the crypto space, especially in regions with limited institutional trust. Peer networks act as informal but powerful educational structures, providing newcomers with advice, emotional support, and real-time market insights. In many cases, peer encouragement is what propels hesitant individuals to take the first step toward using crypto wallets or engaging in DeFi protocols. However, the influence of peers can also perpetuate hype-driven narratives, misinformation, and herd behavior, leading to poor financial decisions or susceptibility to scams. The paper concludes with recommendations for leveraging peer networks in designing effective crypto awareness and onboarding strategies. These include integrating community leaders into education campaigns, offering platform incentives for verified peer mentorship, and collaborating with trusted influencers to communicate risks and best practices. Understanding the dynamics of social influence can help policymakers, educators, and platforms foster more ethical, inclusive, and informed crypto adoption pathways globally.
The Debreu Koopmans theorem restricts separable aggregation to at most one nonconvex component. We solve this by proving that a separable, additive or multiplicative, function is star quasiconvex, those with star shaped sublevel sets about minimizers, if and only if each component is star quasiconvex. This immediately yields star quasiconvexity of separable sums of quasiconvex functions, formally bridging diversification theory with the S shaped value functions of Prospect Theory. Furthermore, we develop a complete calculus, monotonic composition, pointwise minima, quasi arithmetic means, and we apply it to Cobb-Douglas functions, multifactor risk models, and constant function market makers in decentralized finance. Star quasiconvexity thus provides a unified framework for applications in optimization and economic modeling beyond the classical Debreu Koopmans constraint. The introduction discuss economic motivations.
Artificial intelligence (AI)-powered technology integration in social fintech has transformative potential to advance social responsibility and support sustainable development. This research examines a Blockchain-based lending mechanism that integrates centralized exchanges (CEX) and decentralized exchanges (DEX) to facilitate seamless financial transactions and equitable resource allocation. AI-driven tools are utilized to enhance transparency, accuracy, and security, while smart contracts facilitate the efficient management and verification of loan distribution. The proposed system focuses on helping underserved communities, poor regions, and green businesses, promoting fair and sustainable finance in line with the Sustainable Development Goals (SDGs). The hybrid ecosystem combines the liquidity and regulatory compliance of centralized exchanges with the autonomy and reduced intermediary involvement of decentralized exchanges. AI enhances loan processing, reducing biases and inefficiencies. This framework with smart contracts is to provide scalable, auditable lending aligned with sustainable goals. Machine Learning (ML) algorithms verified loan eligibility with the borrower dataset. The performance of Random Forest algorithms is good due to their robustness and ensemble learning features. Then, Optuna enhanced model tuning, and SHapley Additive exPlanations (SHAP) identified key parameters. Finally, Smart contracts ensured secure, autonomous execution of green loans based on ML verification and sustainability criteria.
Background: Access to prosthetic, orthotic and related assistive services remains uneven globally; this manuscript examines the systemic causes and rehabilitation consequences within the context of India. We frame service gaps as health-systems failures with measurable workforce, supply-chain, financing and data components. Methods: A narrative policy review was undertaken using targeted searches of peer-reviewed literature, government reports, professional body publications, and NGO datasets. Key themes were synthesized across governance, workforce, supply chain, financing, and monitoring domains to derive pragmatic policy interventions. (Authors should replace or update search dates and data sources as required prior to submission.) Findings/Observations: Four structural deficits drive undercoverage: (1) insufficient trained P&O workforce and uneven geographic distribution; (2) fragmented manufacturing and procurement with limited quality control; (3) inadequate public financing and poor insurance/benefits coverage for device services; and (4) absence of routine service and outcome surveillance. These deficits produce preventable functional dependency, increased caregiver burden, and inequitable accessâmost pronounced among rural, low-income, and disabled populations. Conclusions: Closing P&O service gaps requires integrated health-systems actions: workforce scale-up and credentialing, pooled procurement and quality standards, explicit public financing pathways, and routine service/outcome monitoring. Policy recommendations (summary): Five priority actions are proposed: national workforce strategy, accreditation and CE frameworks; standardized device procurement and quality assurance; finance and benefit design for assistive services; decentralized service hubs with tele-rehabilitation links; and a national monitoring dashboard tied to performance indicators.
Open access
2 source records
Prosthetics and Rehabilitation Robotics
Assistive Technology in Communication and Mobility
Blockchain technology has been widely heralded as a transformative tool capable of establishing trust in digitized supply chains through cryptographic finance, immutability, and decentralized ledgers. However, empirical evidence and recent analyses suggest that these technological mechanisms alone are insufficient to generate holistic trust among supply chain stakeholders. This study critically examined why blockchain adoption often failed to produce sustained trust, despite enhancing transparency, traceability, and data integrity. A qualitative, theory-driven methodology was employed, analyzing peer-reviewed literature across supply chain management, financial technology, and digital governance domains. The findings revealed that trust remained deeply rooted in social, relational, and institutional dimensions, which blockchain technologies could not replace. Off-chain data dependencies, governance gaps, regulatory ambiguities, and power asymmetries emerged as key factors undermining trust formation. Furthermore, blockchain often displaced trust from human and institutional actors to opaque technical systems, reducing accountability and stakeholder confidence. The study concluded that blockchain should be conceptualized as a supportive infrastructure for trust rather than a substitute for relational and institutional mechanisms. Recommendations included integrating blockchain with hybrid governance models, legal frameworks, and inclusive participation strategies to enhance trust resilience. The study also identified future research directions focusing on cross-industry comparisons, socio-technical interactions, and emerging blockchain alternatives. These insights contribute to a more nuanced understanding of the socio-technical limits of blockchain in supply chain digitization and highlight the critical role of governance and institutional alignment in sustaining trust.
The rapid evolution of digital finance has transformed the way payments are processed globally, leading to a growing comparison between cryptocurrency-based payment systems and traditional payment systems. Traditional payment systems, such as banks, credit cards, and online payment gateways, have long been the backbone of financial transactions, offering reliability and regulatory oversight. However, these systems often face challenges related to transaction speed, high processing costs, and centralized security risks. In contrast, cryptocurrency payment systems leverage blockchain technology to enable decentralized, peer-to-peer transactions that promise faster settlement, reduced transaction fees, and enhanced transparency. This study compares crypto-based payment systems and traditional payment systems across three critical dimensions: speed, cost, and security.
Open access
Blockchain Technology Applications and Security
Innovations and Analysis in Business and Education
Blockchain technology has evolved from its initial application in cryptocurrencies such as Bitcoin to a versatile decentralized infrastructure supporting decentralized finance (DeFi), digital identity systems, smart contracts, and Web3 ecosystems. Despite its transformative potential, the rapid expansion of blockchain platforms has significantly increased the security attack surface, exposing networks to threats such as double-spending, Sybil attacks, smart contract vulnerabilities, transaction laundering, and large-scale financial fraud. At the same time, the emergence of quantum computing introduces a fundamental challenge to classical cryptographic mechanisms particularly Elliptic Curve Digital Signature Algorithm (ECDSA) and RSA that form the backbone of blockchain authentication and transaction verification. This paper presents a comprehensive study of Machine Learning (ML) techniques and Post-Quantum Cryptographic (PQC) frameworks for strengthening blockchain security and threat detection. The study reviews supervised, unsupervised, and deep learning models used for fraud detection, anomaly identification, smart contract vulnerability analysis, and blockchain transaction monitoring. In parallel, it examines quantum-resistant cryptographic algorithms emerging from the NIST post-quantum standardization process, including lattice-based, hash-based, and code-based schemes, and evaluates their suitability for blockchain environments. Furthermore, the paper analyzes the limitations of ML-based security mechanisms and the practical challenges of integrating PQC into decentralized infrastructures, including scalability, key size overhead, and performance trade-offs. A comparative analysis highlights that ML enhances adaptive behavioral threat detection, while PQC ensures long-term cryptographic resilience against quantum attacks. Therefore, the study emphasizes the importance of a hybrid MLâPQC security model that combines intelligent anomaly detection with quantum-resistant cryptographic protection. Finally, the paper identifies key research challenges and outlines future directions toward building scalable, adaptive, and quantum-secure blockchain ecosystems capable of supporting next-generation decentralized applications.
The high-frequency issuance and short-cycle speculation of meme tokens in decentralized finance (DeFi) have significantly amplified rug-pull risk. Existing approaches still struggle to provide stable early warning under scarce anomalies, incomplete labels, and limited interpretability. To address this issue, an end-to-end warning framework is proposed for BSC meme tokens, consisting of four stages: dataset construction and labeling, wash-trading pattern feature modeling, risk prediction, and error analysis. Methodologically, 12 token-level behavioral features are constructed based on three wash-trading patterns (Self, Matched, and Circular), unifying transaction-, address-, and flow-level signals into risk vectors. Supervised models are then employed to output warning scores and alert decisions. Under the current setting (7 tokens, 33,242 records), Random Forest outperforms Logistic Regression on core metrics, achieving AUC=0.9098, PR-AUC=0.9185, and F1=0.7429. Ablation results show that trade-level features are the primary performance driver (Delta PR-AUC=-0.1843 when removed), while address-level features provide stable complementary gain (Delta PR-AUC=-0.0573). The model also demonstrates actionable early-warning potential for a subset of samples, with a mean Lead Time (v1) of 3.8133 hours. The error profile (FP=1, FN=8) indicates that the current system is better positioned as a high-precision screener rather than a high-recall automatic alarm engine. The main contributions are threefold: an executable and reproducible rug-pull warning pipeline, empirical validation of multi-granularity wash-trading features under weak supervision, and deployment-oriented evidence through lead-time and error-bound analysis.
ABSTRACT: Universal access to electricity remains one of the major structural challenges to development in sub-Saharan Africa, and particularly in the Democratic Republic of Congo (DRC), where territorial disparities and low rural electrification rates significantly hinder inclusive economic growth. Faced with the technical and financial limitations of traditional centralized grids, mini-grids and other decentralized electrification solutions are emerging as alternatives adapted to the country's geographical, demographic, and socio-economic realities. However, the development of these solutions fundamentally depends on the ability to mobilize appropriate, sustainable, and structured financing mechanisms. High initial infrastructure costs, combined with the limited repayment capacity of rural populations and a still-developing institutional environment, constitute major constraints to investment. The analysis highlights the need for a hybrid financial architecture, combining private equity, concessional debt, subsidies, and innovative financial instruments such as mezzanine debt, crowdfunding, and pay-as-you-go mechanisms. The economic sustainability of projects depends on a delicate balance between the financial viability of operators and affordable pricing for users. Business models must incorporate diversification of energy services, the integration of productive uses, and rigorous risk management (demand, exchange rate fluctuations, regulatory instability). The leverage generated by combining different funding sources strengthens investment capacity and improves project resilience. Institutionally, the regulatory framework plays a crucial role. The clarity of tariff rules, legal stability, transparency in subsidy allocation, and the effectiveness of rural electrification agencies are key factors in the sector's attractiveness to private investors. Tax and customs incentives, as well as risk guarantee mechanisms, are essential levers for reducing the cost of capital and stimulating local financial sector involvement. The study of the Congolese context reveals considerable energy potential, particularly in hydroelectric and solar power, but also persistent challenges related to access to credit, administrative complexity, and the structuring of public-private partnerships. Improving the financing of mini-grids in the DRC therefore requires an integrated approach combining regulatory reforms, institutional capacity building, and financial innovation. Ultimately, financing mini-grids is not merely a technical or budgetary issue, but a strategic challenge for energy governance and structural transformation. Establishing a coherent financial and regulatory ecosystem is essential to ensure the sustainability of projects, accelerate rural electrification, and contribute significantly to achieving the Sustainable Development Goals, particularly SDG 7 on access to reliable, affordable, and sustainable energy.
As Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) expand, self-custody wallets have become the primary interface for user sovereignty. However, existing solutions suffer from critical limitations, including static authentication frameworks that compromise usability, a lack of real-time risk awareness, and inadequate key recovery mechanisms that often lead to permanent asset loss or reliance on centralized custodians. Furthermore, current wallets frequently expose transaction metadata, undermining user privacy. To address these systemic flaws, we present a modular self-custody wallet that incorporates a context-aware risk engine for real-time transaction scoring, risk-based adaptive authentication, and a dual-path decentralized key-recovery layer combining DAO-governed Shamir secret sharing with a zk-SNARK-verified fallback. The architecture further includes programmable policy enforcement and a zero-knowledge swap layer with stealth addressing to decouple front-end activity from on-chain data. The design integrates smart contracts on EVM chains and Solana through provider adapters and executes on-device ML inference to minimize latency. Experimental results demonstrate that the proposed system reduces privacy leakage probability to 5% (compared to 85% in standard architectures) and accelerates key recovery from over 24 h to approximately 8 seconds using zk-SNARKs, all while achieving 93.6% risk classification accuracy. The proposed CAPPR-Wallet advances self-custody by combining context adaptivity, privacy, and recoverability without centralized trust.
Open access
Blockchain Technology Applications and Security
Security and Verification in Computing
Physical Unclonable Functions (PUFs) and Hardware Security
Understanding how Bitcoin mining is distributed across countries is important for evaluating both the sustainability and resilience of the network. In this study, we examine the evolution of total Bitcoin electricity consumption alongside the geographic distribution of Bitcoin mining. Data are provided by the Cambridge Centre for Alternative Finance (Licensed under CC BYâNCâSA 4.0): Annual data from the Cambridge Bitcoin Electricity Consumption Index (2010â2025) and a monthly panel of country-level Bitcoin hashrate shares for 105 countries (September 2019âJanuary 2022). To assess the degree of decentralization in the global mining network, we employ entropy-based measures, inequality indices, and panel convergence tests. The results indicate that total electricity consumption grew exponentially during the early years of Bitcoin, but later transitioned to a more stable and approximately linear path. Country-level permutation entropy reveals highly volatile and dynamic mining trajectories. The Theil index shows that cross-sectional inequality declines over time, while increasing symbolic entropy reflects a progressively more even cross-country distribution of mining activity. Further evidence from Ď-convergence supports a statistically significant reduction in cross-country dispersion of mining shares. Dynamic panel fixed-effects estimates reveal mean-reverting behavior in relative country shares, consistent with stochastic convergence. Finally, PhillipsâSul analysis points to heterogeneous early transition paths but ultimately supports convergence toward a single global club. The gradual geographical decentralization occurs alongside persistent coreâperiphery asymmetries in long-run mining shares. Overall, our findings suggest that Bitcoin mining behaves as a globally integrated industry in which computational capacity reallocates rapidly across countries in response to economic and regulatory conditions.
Artificial Intelligence (AI) has become a critical driver of firm survival in the banking industry, particularly for deposit money banks (DMBs) facing increasing challenges such as economic volatility, regulatory compliance, cybersecurity threats, and rising customer expectations. This study explores the role of AI in enhancing operational efficiency, risk management, fraud detection, customer experience, and financial resilience in the banking sector. AI-powered technologies, including machine learning, predictive analytics, robotic process automation (RPA), and natural language processing (NLP), are transforming how banks analyze financial risks, detect fraudulent transactions, automate operations, and provide personalized banking services. Research findings indicate that AI adoption has led to a 35% reduction in loan defaults, a 40% improvement in operational efficiency, and a 60% decline in financial fraud cases, highlighting its transformative potential in ensuring the survival and competitiveness of DMBs. Despite these advancements, AI adoption in the banking sector is hindered by high implementation costs, cybersecurity vulnerabilities, workforce resistance, and regulatory uncertainties. Many banks, particularly in developing economies like Nigeria, struggle with legacy banking systems, lack of AI governance frameworks, and concerns over algorithmic bias in lending decisions. Additionally, AI-driven financial innovations, such as blockchain integration, decentralized finance (DeFi), and AI-powered ESG compliance solutions, are reshaping the banking industry, yet require strategic policy alignment and investment to maximize their benefits. The study identifies gaps in existing literature, including the need for empirical research on AIâs long-term impact on firm survival, its role in financial inclusion, and the ethical challenges of AI governance in banking. To bridge these gaps, future research should focus on developing AI implementation models suited to the challenges of emerging economies, exploring AIâs potential in expanding financial access to underserved populations, and strengthening AI-driven sustainability and ESG compliance frameworks in banking. As AI continues to evolve, deposit money banks must embrace a balanced approach that integrates AI innovation with regulatory oversight, cybersecurity safeguards, and workforce upskilling to ensure long-term survival and competitiveness in the digital financial landscape
This study develops and evaluates a hybrid ComplianceâAI cybersecurity model for unified protection of traditional banking and decentralized finance (DeFi) systems in Brazil. Using publicly available data from the NIST Cybersecurity Framework, DeFi exploit repositories (REKT and DeFiLlama), Elliptic crypto-transaction graphs, IEEE-CIS fraud data, DARPA Transparent Computing datasets, and Monte Carloâsimulated cross-domain attack scenarios, the research applies hierarchical clustering, supervised learning, Markov chain modeling, and stochastic simulation. Results show that 45% of banking controls are transferable or hybridizable to DeFi, that embedding machine-readable compliance features improves ROCâAUC from 0.842 to 0.914 and reduces false positives by nearly 47%, and that bidirectional orchestration lowers escalation probability by over 54%. Monte Carlo analysis further indicates a 62% reduction in tail financial risk under the hybrid architecture. The study recommends machine-readable regulation, compliance-aware AI deployment, orchestrated enforcement layers, and expanded RegTech and SupTech adoption to strengthen systemic financial cybersecurity. Keywords: ComplianceâAI Integration, Financial Cybersecurity, Decentralized Finance, Machine-Readable Regulation, Systemic Cyber Risk.
BACKGROUND: Noncommunicable diseases (NCDs), including hypertension and diabetes, account for approximately 27% of all deaths in Kenya, with 26% of adults having elevated blood pressure. Despite devolution of health services to county governments in 2013, financing for NCD management at the primary health care (PHC) level remains weak. This study examines financial determinants shaping hypertension and diabetes care in PHC facilities within a devolved county health system in rural Kisumu County, Kenya. METHODS: We conducted a convergent parallel mixed-methods cross-sectional study in seven public PHC facilities in Seme Sub-County, providing new facility-level evidence on how the interaction between devolutionâs financing architecture, facility-level financial autonomy constraints, and resource allocation mechanisms shapes chronic disease care effectiveness in rural Kenya. Quantitative data were collected via structured questionnaires and retrospective document review of financial records (JanuaryâAugust 2024). Qualitative data were gathered through key informant interviews (n = 7) with facility in-charges exploring planning, budgeting, and resource allocation. Descriptive statistics were produced in STATA v16; qualitative data were analyzed thematically in R. RESULTS: All seven facilities prepared annual workplans and budgets, but none achieved comprehensive NCD-specific planning (workplan + budget + dedicated NCD budget line). Funding sources were narrow: 71.4% (n = 5) of the facilities depended on NHIF reimbursements and donor support, while only 28.6% (n = 2) received direct county funding; 57.1% (n = 4) of the facilities relied on only two funding streams. Although all facilities held bank accounts, none had formal financial autonomy and expenditures required county-level approval, typically taking 3â4 weeks (57.1%, n = 4) to over two months (28.6%, n = 2). Combined with unreliable central supplies, this lack of autonomy meant facilities could not procure locally when stockouts occurred; consequently 85.7% (n = 6) of the facilities reported frequent medication stockouts. Facility in-charges attributed these failures to inadequate, unpredictable funding and centralized approval processes that prevented timely local procurement. CONCLUSIONS: Rural PHC facilities operate under structural governance failures in Kenyaâs devolved health financing system that systematically undermine effective NCD care. The centralization of financial authority at county level, absence of ring-fenced NCD budgets, and misalignment between planning processes and resource allocation represent system-level policy contradictions rather than facility-level operational deficiencies. Addressing these governance failures requires not only increased funding but constitutional fiscal decision-space for facilities, mandatory NCD budget protection, and reformed disbursement mechanisms essential for equitable chronic care under Kenyaâs UHC agenda. The sustainability of chronic care depends fundamentally on facility decision space, not only on funding volume. These findings are transferable to other Kenyan counties under the same devolved framework and to decentralized health systems in sub-Saharan Africa facing similar tensions between fiscal accountability and operational autonomy for chronic disease management.