Henry Segun Uwabor, Igba Emmanuel, Onuh Matthew Ijiga
The emergence of decentralized finance (DeFi) has transformed global financial ecosystems by enabling transparent, permissionless, and automated investment systems. However, the inherent volatility, regulatory uncertainty, and data complexity within DeFi ecosystems pose significant challenges for risk modeling and compliance assurance. This review explores the integration of AI-powered predictive frameworks to enhance risk assessment, fraud detection, and regulatory compliance in decentralized finance investment systems. By leveraging machine learning (ML), deep learning (DL), and natural language processing (NLP) models, the study examines how predictive analytics can proactively identify anomalous transactions, assess smart contract vulnerabilities, and optimize portfolio risk exposure. The paper also evaluates how AI-driven systems can align DeFi operations with emerging regulatory frameworks, including KYC/AML protocols, data protection standards, and algorithmic auditing requirements. Additionally, the review highlights the role of explainable AI (XAI) in promoting transparency, interpretability, and trust among regulators and investors. Through a synthesis of existing literature and real-world applications, this paper presents a comprehensive framework illustrating how predictive AI technologies can bridge the gap between financial innovation and regulatory governance in DeFi. The findings underscore the potential of intelligent, adaptive, and compliant DeFi systems capable of ensuring sustainable growth, investor protection, and systemic stability in the evolving digital financial landscape.
As the world grapples with climate change and energy insecurity, renewable energy has emerged as a central pillar of sustainable development. However, the transition to renewables faces persistent technological, economic, policy, and social challenges. This paper explores the dual nature of renewable energy—its immense promise and its complex barriers—through global trends and India-focused case studies. By analyzing large-scale and decentralized renewable projects, including Bhadla, Pavagada, Rewa, and Kurnool solar parks, as well as microgrid initiatives in Dharnai and Indira Nagar, this study identifies strategic pathways for inclusive and resilient energy futures. The analysis reveals that integrated policies, innovative financing, community participation, and technological innovation are key to maximizing renewable energy’s transformative potential. Key words: climate change, energy, renewable.
Abstract Blockchain technology has become a transformative solution for secure and transparent digital ecosystems. This paper explores how decentralization, cryptographic hashing, distributed consensus, and immutable ledger architecture contribute to advanced data protection in the IT industry. The study integrates findings from existing literature, evaluates blockchain’s practical applications in sectors including finance, healthcare, supply chain, and governance, and examines a proposed multi-layer blockchain framework. The research highlights blockchain’s advantages in enhancing confidentiality, integrity, availability, and auditability, while identifying its limitations such as scalability, regulatory constraints, and environmental impact. Future scope emphasizes integration with AI, IoT, Web 3.0, quantum-resistant models, and cross-chain interoperability. Overall, the study concludes that blockchain is a critical technology for advancing trust-driven IT infrastructures. Keywords Blockchain, Data Security, Transparency, Decentralization, Smart Contracts, IT Industry
<p>Existing financial systems are bloated with inefficiencies in their operation, lack of transparency and are characterized by and fallible and fragile accumulation points, whereas emerging decentralized finance (DeFi) platforms lack intelligent risk management, self-adaptive governance and provable security assurances. This paper proposes the Intelligent, Verifiable Financial Ledger (IVFL), a novel framework that harmoniously converts both Artificial Intelligence (AI) and blockchain to counteract their core drawbacks. AI-based smart contracts of a formally verifiable character that allows the intelligent, secure and auditable automated execution of complex financial transactions an agile and informed governance system, which is represented by the use of AI enhancements to the Decentralized Autonomous Organization (DAO). Simulation analysis shows that the IVFL framework enables substantial enhancements compared to baseline models, such as detecting anomalies with over 95% accuracy, decreasing operational overhead by 40 percent and becoming less vulnerable to coordinated network attacks. Coming back to provable security and adaptive intelligence, the IVFL framework represents a credible way of creating financial systems.</p>
Regional banks emerged around the 1960s with the mission of contributing to the development and integration of Latin America, primarily through the financing of infrastructure projects, essential to the region's industrialization and trade flows.In 2000, the South American Regional Integration Initiative (IIRSA) was created, under whose Secretariat the Inter-American Development Bank (IDB), the Development Bank of Latin America (CAF), and FONPLATA -Development Bank -began working together to promote territorial planning and find financing solutions.Even with the dissolution of IIRSA, resulting from the paralysis of the Union of South American Nations (UNASUR) starting in 2017, the coordinated action of the three banks continued, through initiatives such as the Alliance for the Integration and Development of Latin America and the Caribbean (ILAT), the Sucre Declaration, and the "South American Integration Routes," demonstrating the resilience of infrastructure integration in the face of political change.Therefore, the overall objective of this thesis was to analyze the contribution of the development banks IDB, CAF, and FONPLATA to building regional infrastructure integration in South America.The methodology involved identifying integration models and operational concepts under which these banks operate; identifying the specific problems of Latin American regional infrastructure and its financing; and analyzing the performance of IDB, CAF, and FONPLATA both individually, focusing on documents, projects, and institutional structures focused on integration, and collectively, from the emergence of IIRSA to the Integration Routes.It was found that, despite the current general crisis in Latin American regionalism, both intellectual and institutional, the three banks are at the center of building a governance system for financing regional infrastructure in South America.However, they have shifted from a model centralized in IIRSA to one, after the end of this Initiative, focused on decentralized cooperation.
Transitioning to renewable energy is thus a very important component of global efforts toward combating climate change, especially in emerging economies where energy demand is fast outpacing supply. Carbon markets have emerged as a vital financial mechanism for supporting renewable energy projects by enabling the trade of carbon credits. The following abstract discusses how carbon markets affect multi-dimensionally the financial flows of renewable energy in developing nations: attracting investment, reducing capital costs, driving technology innovation, and delivering decentralized energy. Through case studies from Kenya, India, and Brazil, the article illustrates how carbon markets have indeed served to mobilize such large-scale renewable projects as wind farms and solar installations that improve the lot of rural and underserved communities. Despite the promise of carbon markets, it still faces regulatory gaps, market volatility, high transaction costs, and limited participation from local stakeholders. This may spell out actionable solutions, such as the development of regional carbon trading systems, enhancement of voluntary carbon markets, blended finance models, and the integration of emerging economies into global carbon market initiatives within frameworks like those under the Paris Agreement. Carbon markets could have a real catalyzing role in the transition toward renewable energy, with accelerated rates of greenhouse gas emission reduction and sustainable development in emerging economies, if they are able to successfully address these tacked barriers.
This study interrogates climate governance in the Southern Africa’s socio-ecological peripheries, concentrating on how decentralized adaptation policies shape rural livelihoods confronted with deepening climate hazards. The region’s ecosystems are worsening under climate stress, with smallholder farmers and forest-dependent communities already positioned at the social periphery bearing the brunt of more erratic precipitation and rising temperatures. The study utilized secondary materials, including peer-reviewed articles, official policy documents, and theoretical discussions on governance and adaptive responses. Data analysis was conducted through an interpretive and integrative approach, critically juxtaposing insights from distinct disciplinary repositories and constructing thematically coherent groupings. The study found that while decentralisation can enhance adaptive governance, its overall effectiveness hinges on bolstering cross-scale finance, capacity, and integration. The study further established that marginalized populations particularly women and youth continue to be underrepresented in decisional arenas, which undermines the equity of adaptation initiatives. The study concludes that decentralized climate governance can achieve transformation only when it is inclusive, sufficiently financed, and intricately linked to overarching rural development plans.
Open access
Climate change impacts on agriculture
Sustainability and Climate Change Governance
Conservation, Biodiversity, and Resource Management
Hsi‐Peng Lu, Ya-Yuan Ku, Kuo‐Lun Hsiao, Wadee Alhalabi
With the rise of blockchain and decentralized technologies, doubts about traditional financial institutions' efficiency have increased. Meanwhile, Web3 offers transparency, security, and autonomy. However, the existing literature overlooks role the role of doubt as a push factor while focusing on the positive effects of trust. Moreover, the role of crypto wallets as a mooring factor remains underexplored. This study applies push-pull-mooring theory to examine Web3 literacy, trust in machines, doubt in institutions, and switching costs. Data were collected from 165 survey respondents. The results indicate that Web3 literacy increases doubt in traditional institutions but does not significantly affect trust in Web3. Additionally, switching costs moderate the relationship between Web3 literacy and doubt. When switching costs are low, doubt rises significantly. This study provides a new perspective on Web3 adoption, showing doubt's push effect and the role of push-pull mooring in migration, thus addressing gaps in the literature. Furthermore, the findings highlight how decentralized finance's trust mechanism is evolving, offering insights for Web3 adoption.
Abstract This chapter discusses the Albanian model for the equalization of financial disparities between urban and rural municipalities, especially after the 2014 Territorial and Administrative Reform (TAR). This reform reduced the number of local governments, merging 373 rural and urban entities into 61 larger municipalities. It aimed to streamline and harmonize service provision across regions and municipalities, but challenges persist due to the limited financial resources of local governments. The chapter explores the country’s intergovernmental financial framework, including recent reforms, which enhanced municipal responsibilities and financing. Despite reforms Albanian municipalities are heavily reliant on intergovernmental transfers, with unconditional grants playing a crucial role in equalizing financial resources. While the stability and allocation of the unconditional grants has improved since 2017, rural municipalities still struggle due to higher service costs and lower fiscal capacity compared to urban centers. There are also major differences between larger urban areas and the capital, Tirana. The chapter concludes that while Albania has made strides in decentralization, further reforms are necessary to address the ongoing fiscal inequalities between urban and rural local governments, underscoring the need for more robust equalization and financing mechanisms to bridge the gap between urban and rural municipalities.
The global Blockchain Market, valued at USD 24.20 billion in 2024, is projected to reach USD 301.02 billion by 2030, expanding at an impressive CAGR of 60.2% from 2025 to 2030. The increasing digital payment transactions, rising demand for security, and rapid adoption of cryptocurrencies are fueling market growth. However, challenges such as regulatory uncertainty and high implementation costs remain significant barriers. Despite these limitations, the integration of decentralized finance (DeFi), AI, and advanced technological frameworks presents substantial opportunities for innovation. Leading industry players, including IBM, Ethereum, Hyperledger, and Oracle, are actively engaging in partnerships and technological advancements to strengthen their market presence. As blockchain technology continues to evolve, its applications are expanding across sectors including BFSI, healthcare, government, logistics, and retail. This manuscript provides an in-depth analysis of drivers, restraints, opportunities, segmentation, regional insights, and competitive landscape shaping the future of the global blockchain economy.
We examine the qualifying attributes of decentralized finance (DeFi) as a financial asset class. To achieve this objective, we perform analysis on the relationship (using both level and percentage-change data) between DeFi valuation and selected influencing variables, namely total value locked (TVL), Bitcoin (BTC) value, and market variables. A suite of long-panel data econometric methods is employed on a multi-frequency (daily, weekly, and monthly) panel dataset comprising 16 major DeFi protocols from January 2022 to December 2023. Our empirical design aims to be a comprehensive assessment and triangulation. There are several key findings. First, while there is evidence of cointegration suggesting a possible long-run relationship, this relationship is found to be inconsistent across different variables and time frequencies. However, the impulse response analysis suggests that shocks from the influencing variables do not have a permanent impact. Second, Bitcoin value is found to be the most important influencing factor (positive and highly significant), reflecting strong cryptocurrency market sentiment and aligning with previous research on spillover effects from major cryptocurrencies (Șoiman et al., 2022; Yousaf et al., 2022).
Entrepreneurs typically seek financing in decentralized markets, where they approach investors sequentially. We develop a model of sequential capital markets with privately informed investors. The sequential market creates a dynamic adverse selection externality that leads to overinvestment and excessive rents to intermediaries, even as the number of competing investors becomes arbitrary large. The resulting rents lead to excessive entry of investors and insufficient entry of entrepreneurs. Moving to a centralized market structure or reducing transparency restores competitiveness but may harm efficiency. The model also explains how even a small skill advantage for an investor can lead to preferential deal flow and outsized returns.
Aguirre Ortiz, Sofía, Parrado Carreño, Gary Yeffet, Zamora, Jairo
Accelerated global digitalization is threatened by a profound crisis of digital trust, marked by systemic data breaches and eroding confidence in centralized intermediaries. This article investigates Distributed Ledger Technology (DLT) as a foundational solution, examining its capacity to replace institutional trust with cryptographic assurance through decentralized verification. Through a rigorous comparative case study methodology analyzing cross-border finance and supply chain traceability, the research assesses how DLT mitigates counterparty risk while generating new forms of economic value. The core analysis focuses on critical implementation tensions between operational scalability requirements and ideological decentralization goals, alongside the challenge of reconciling immutable systems with evolving global regulatory frameworks. Empirical findings confirm DLT's tangible economic value through significant reductions in financial verification costs and settlement timeframes, while simultaneously generating measurable consumer trust premiums in supply chain applications through verifiable provenance. However, evidence reveals a fundamental trade-off: practical enterprise adoption consistently favors high-throughput permissioned ledgers, compromising decentralization ideals for operational scalability and governance control. Significant regulatory friction further necessitates hybrid data architectures, positioning DLT as a crucial assurance layer within broader compliance ecosystems rather than a standalone solution. This underscores the need for future research developing integrated trust frameworks that balance technological potential with implementation pragmatism across diverse sectoral contexts.
The regulation of cryptocurrency presents a major challenge to financial governance in emerging economies such as Tanzania. The rapid growth of virtual assets, their decentralized nature, and potential for anonymity have raised significant concerns regarding money laundering, terrorist financing, and consumer protection. Internationally, the Financial Action Task Force (FATF) has established standards that require member states to regulate Virtual Asset Service Providers (VASPs) through licensing, supervision, and compliance with Anti–Money Laundering and Counter–Terrorist Financing (AML/CFT) measures. This article critically analyses the Tanzanian legal and institutional framework governing cryptocurrency in light of these international standards. It argues that although Tanzania has made preliminary steps such as recognizing digital assets under the Finance Act, 2024 and issuing public notices through the Bank of Tanzania there remains a significant regulatory gap in achieving full FATF compliance. The study concludes that comprehensive legislation is required to address the legal status of virtual assets, enhance regulatory oversight, and foster a balance between innovation and financial integrity.
The rise of Decentralized Finance (DeFi), enabled by blockchain technology, has introduced open and transparent financial ecosystems that contrast sharply with traditional financial systems. While DeFi expands the financial landscape and democratizes participation in global markets, it also introduces new complexities. Classic valuation models used in traditional finance often fall short in this context. However, DeFis transparency---where all transactions are publicly recorded---offers a unique opportunity to model and understand market behavior using modern analytical tools. Motivated by the challenges and opportunities of DeFi, the main goal of this thesis is to propose novel methods to understand market dynamics through the lens of network science and machine learning. To this end, we focus on four specific objectives: (i) assess whether structural information from blockchain transaction networks provides predictive signals beyond traditional indicators; (ii) develop robust trust-based valuation metrics for DeFi protocols; (iii) develop a framework for forecasting financial time series through uncertainty-aware machine learning architectures; (iv) construct diversified portfolios using network-based representations of asset relationships. First, using Ethereum as a case study, we analyze the influence of the transaction network on market trends by comparing the performance of two machine learning models: one that uses technical analysis and social media indicators commonly found in the literature and another that incorporates structural properties of the transaction network. We found that by including transaction network information, we can anticipate 46% more uptrends and 19% more downtrends, highlighting the predictive power of the transaction network. Second, we introduce the TVL/MCAP bands as a tool to identify periods of overconfidence and underconfidence in the DeFi market. We show that extreme values of this indicator can signal price movements: values above the 95th percentile are associated with a 15\% higher return in the following month, while values below the 5th percentile anticipate declines, highlighting investor confidence as a key market driver. Third, we address the need for forecasts that not only anticipate market trends but also quantify the uncertainty surrounding them. To this end, we integrate Reservoir Computing (RC) with conformal prediction methods to provide statistically rigorous forecasts along with prediction intervals. We found that RC outperform traditional econometric models, particularly in anticipating the trend of financial time series. Furthermore, we show that conformal methods, especially quantile-conformal variants, significantly improve forecast reliability while adapting to market volatility. Finally, we address the challenge of portfolio optimization using network-based methods. Specifically, we model the network of relationships between cryptocurrencies to obtain a market representation that enables selecting a more diversified portfolio. We find that peripheral assets enhance portfolio stability and returns, while links bridging network communities carry higher risk. Thereby, these results highlight the importance of structural diversification in volatile markets. In addition, we contribute to refining pairs trading strategies by proposing the Hurst exponent to identify rapid mean-reversion opportunities. We show that anti-persistent values of H lead to faster reversion and consistent returns---minimizing trading costs and enabling broader portfolio construction. In conclusion, this thesis provides an interdisciplinary analytical framework that advances our understanding of DeFi markets. By introducing network-based indicators, trust metrics, uncertainty-aware forecasts, and diversification strategies grounded in market structure, we provide new tools for investors and researchers to navigate the complexity and volatility inherent in decentralized financial systems. RESUMEN El auge de las Finanzas Descentralizadas (DeFi), impulsado por la tecnología blockchain, ha dado lugar a ecosistemas financieros más accesibles y transparentes que contrastan con los sistemas financieros tradicionales. DeFi amplía el panorama financiero actual e introduce nuevos retos, como la necesidad de un nuevo modelo de valoración de los activos. No obstante, el hecho de que todas las transacciones son públicas, ofrece una oportunidad única para modelar y comprender la dinámica del mercado mediante nuevas herramientas analíticas. Esta tesis tiene como objetivo principal proponer nuevos métodos para comprender la dinámica del mercado desde la perspectiva de los sistemas complejos y el aprendizaje automático. Para ello, nos centramos en cuatro objetivos específicos: (i) evaluar si la información estructural de las redes de transacciones aporta señales predictivas más allá de los indicadores tradicionales; (ii) desarrollar métricas de valoración de los protocolos DeFi basadas en la confianza de los inversores; (iii) construir un marco metodológico para predecir series temporales financieras mediante arquitecturas de aprendizaje automático que incorporen incertidumbre; (iv) construir portfolios diversificados utilizando representaciones de la red de relaciones entre criptomonedas. En primer lugar, utilizando Ethereum como caso de estudio, analizamos la influencia de la red de transacciones sobre la tendencia del mercado comparando dos modelos de aprendizaje automático: uno que emplea indicadores de análisis técnico y de redes sociales comunes en la literatura, y otro incluyendo propiedades estructurales de la red de transacciones. Los resultados muestran que incluyendo información de la red podemos anticipar un 46% más de tendencias alcistas y un 19% más de tendencias bajistas, lo que subraya el poder predictivo de la red de transacciones. En segundo lugar, introducimos las bandas TVL/MCAP para identificar períodos de sobreconfianza y desconfianza en el mercado DeFi. Demostramos que valores extremos de este indicador anticipan movimientos en el precio: valores por encima del percentil 95 se asocian con un rendimiento 15% superior en el mes siguiente, mientras que valores por debajo del percentil 5 anticipan caídas. En tercer lugar, abordamos la necesidad de predicciones que no solo anticipen tendencias del mercado, sino que también cuantifiquen la incertidumbre. Para ello, integramos Reservoir Computing (RC) con métodos de predicción conforme para generar predicciones estadísticamente rigurosas junto con intervalos de confianza. Mostramos que RC supera a los modelos econométricos tradicionales, especialmente anticipando la tendencia del precio. Además, los métodos conformes ---en particular las variantes de cuantiles--- mejoran significativamente la fiabilidad de las predicciones al adaptarse a la volatilidad del mercado. Por último, abordamos el problema de optimización de portfolios mediante métodos basados en redes. Específicamente, modelamos la red de relaciones entre criptomonedas para seleccionar un portfolio más diversificado. Observamos que evitar pares que conectan distintas comunidades en la red y priorizar activos periféricos aumenta el rendimiento y disminuye el riesgo, demostrando así la importancia de una diversificación estructural. Además, proponemos el uso del exponente de Hurst para identificar oportunidades que revierten antes a la media en estrategias de pairs trading. En conclusión, esta tesis propone un marco analítico interdisciplinar que contribuye al entendimiento de los mercados DeFi. Al introducir indicadores basados en redes, métricas de confianza, predicciones con estimación de incertidumbre y estrategias de diversificación basadas en la estructura del mercado, ofrecemos nuevas herramientas para que inversores e investigadores naveguen la complejidad y volatilidad propias de los sistemas financieros descentralizados.
Blockchain technology has emerged as the backbone of cryptocurrencies and decentralized finance, yet its long-term resilience is increasingly threatened by advances in quantum computing. Quantum algorithms, such as Shor’s algorithm, can undermine public-key cryptography, while Grover’s algorithm accelerates brute-force search, weakening proof-of-work schemes. In this paper, we propose a Quantum Blockchain Framework that integrates quantum communication protocols, quantum consensus mechanisms, and quantum-resistant cryptography. We construct a theoretical model of quantum-secured distributed ledgers, where qubits, entanglement, and quantum key distribution (QKD) enhance security and efficiency. Applications to cryptocurrency are explored, highlighting how quantum blockchain can mitigate security risks, improve consensus speed, and enable quantum-native digital assets.
Once a playground for tech enthusiasts, the crypto space has shifted to a financial field that is increasingly on policymakers’ radar due to the increasing adoption of crypto-assets, and also some significant crypto-related collapses. In this context, it is crucial to propose monitoring frameworks to assess the potential integration of the crypto sphere into traditional financial systems. We propose the use of the TVP-VAR approach as a strategic instrument for policymakers to analyze the connectedness between major financial markets and relevant crypto systems, such as the emerging centralized finance sector and the increasingly relevant decentralized finance ecosystem. Our findings indicate that the financial integration between the crypto space and traditional financial markets remains weak. Nonetheless, we report a very slight increase in connectedness since 2020, suggesting that while the crypto space is still far from being fully integrated, it has begun to establish modest but persistent links with conventional financial markets. • We examine dynamic connectedness between crypto and global equity markets. • TVP-VAR shows crypto–TradFi integration remains weak but rising since 2020. • DeFi and broad crypto indices transmit more spillovers than Bitcoin or CeFi. • Results highlight regulatory priority on DeFi and full-market monitoring.
The study explores the impact of non-fungible tokens on asset liquidity within the decentralized finance system, based on a systematic review of thirty articles retrieved from major scholarly databases. We analyzed how NFTs contribute to increasing liquidity and facilitate a shift in digital asset ownership. Findings show NFTs improve asset liquidity by permitting fractional ownership and trade of assets that were previously illiquid, like real estate and digital art. NFTs’ unique characteristics and market volatility may make them less liquid. Blockchain technology that underpins NFTs offers transparent and unchangeable ownership records. The ramifications show how developers, investors, and regulators may take advantage of NFTs’ while resolving obstacles, including scalability problems and regulatory uncertainty.
Abstract: Identity theft has emerged as a psychologically consequential form of cybercrime enabled by the proliferation of digital platforms, the expansion of datafication, and the collapse of traditional criminal–victim proximity. As personal identity becomes increasingly externalized through financial accounts, medical records, biometric templates, and algorithmically curated social profiles, offenders exploit cognitive biases, disclosure fatigue, and habituated oversharing to acquire and weaponize personal information. Criminal psychology research demonstrates that social engineering, authority mimicry, and emotional urgency manipulate victims into bypassing rational scrutiny, while cyberpsychology highlights the affective attachment individuals form with their digital representations. Unlike conventional theft, in which tangible objects are removed, identity theft appropriates informational components of the self, enabling prolonged impersonation, reputational distortion, and chronic anxiety that cannot be readily restored. Geographic detachment, encrypted communication channels, and anonymizing technologies reduce offenders’ perceived accountability, encouraged moral disengagement and facilitating mass victimization at minimal personal risk. Victims, confronted with unauthorized transactions or corrupted medical histories, report hypervigilance, loss of digital agency, and destabilization of narrative coherence. Emerging technologies, including Internet of Things devices, deepfake media, decentralized finance, and eventually quantum computing, further expand the attack surface and amplify criminogenic opportunity structures. Meanwhile, jurisdictional fragmentation complicates forensic attribution and legal recourse. Collectively, these developments reveal that traditional, place-based models of personal security are insufficient in networked environments. Safeguarding informational sovereignty requires interdisciplinary approaches that integrate behavioral criminology, cognitive vulnerability assessment, cyberpsychological resilience, and international policy coordination. Understanding identity theft as an ontological, relational, and psychologically persistent violation offers critical insight for prevention, victim support, and regulatory design in the digital epoch. Keywords: Identity Theft; Cyberpsychology; Criminal Psychology; Datafication; Digital Proximity Collapse; Social Engineering; Informational Sovereignty; Biometric Fraud; Cognitive Vulnerability; Cybercrime Scalability
Open access
2 source records
Cybercrime and Law Enforcement Studies
Crime Patterns and Interventions
Psychopathy, Forensic Psychiatry, Sexual Offending
In timing-sensitive blockchain applications, such as decentralized finance (DeFi), achieving first-come-first-served (FCFS) transaction ordering among decentralized nodes is critical to prevent frontrunning attacks. Themis [CCS'23], a state-of-the-art decentralized FCFS ordering system, has become a key reference point for high-throughput fair ordering systems for real-world blockchain applications, such as rollup chains and decentralized sequencing, and has influenced the design of several subsequent proposals. In this paper, we critically analyze its core system property of practical batch-order fairness and evaluate the frontrunning resistance claim of Themis. We present the Ambush attack, a new frontrunning technique that achieves nearly 100% success against the practical batch-order fair system with only a single malicious node and negligible attack costs. This attack causes a subtle temporary information asymmetry among nodes, which is allowed due to the heavily optimized communication model of the system. A fundamental trade-off we identify is a challenge in balancing security and performance in these systems; namely, enforcing timely dissemination of transaction information among nodes (to mitigate frontrunning) can easily lead to non-negligible network overheads (thus, degrading overall throughput performance). We show that it is yet possible to balance these two by delaying transaction dissemination to a certain tolerable level for frontrunning mitigation while maintaining high throughput. Our evaluation demonstrates that the proposed delayed gossiping mechanism can be seamlessly integrated into existing systems with only minimal changes.
As Decentralized Finance (DeFi) develops, understanding user intent behind DeFi transactions is crucial yet challenging due to complex smart contract interactions, multifaceted on-/off-chain factors, and opaque hex logs. Existing methods lack deep semantic insight. To address this, we propose the Transaction Intent Mining (TIM) framework. TIM leverages a DeFi intent taxonomy built on grounded theory and a multi-agent Large Language Model (LLM) system to robustly infer user intents. A Meta-Level Planner dynamically coordinates domain experts to decompose multiple perspective-specific intent analyses into solvable subtasks. Question Solvers handle the tasks with multi-modal on/off-chain data. While a Cognitive Evaluator mitigates LLM hallucinations and ensures verifiability. Experiments show that TIM significantly outperforms machine learning models, single LLMs, and single Agent baselines. We also analyze core challenges in intent inference. This work helps provide a more reliable understanding of user motivations in DeFi, offering context-aware explanations for complex blockchain activity.
The rapid evolution of financial technologies (FinTech) and digital assets—including cryptocurrencies, decentralized finance (DeFi), and tokenized capital markets—has created an unprecedented need for secure, scalable, and computationally efficient systems. This study examines the transformative potential of quantum computing in reshaping financial technology infrastructures and digital asset ecosystems. Traditional computational models, constrained by classical encryption limits and the exponential growth of financial data, face increasing inefficiencies in handling real-time risk assessment, portfolio optimization, and transaction verification. Quantum computing, with its capacity for superposition, entanglement, and parallel state evaluation, provides novel opportunities to redefine data security, financial modeling, and cryptographic mechanisms in the digital economy. The research explores how quantum algorithms—notably Quantum Approximate Optimization Algorithm (QAOA), Quantum Fourier Transform (QFT), and Grover’s search algorithm—can enhance financial operations such as market forecasting, fraud detection, and asset pricing. Additionally, it investigates quantum-resistant cryptography to safeguard digital asset networks against the vulnerabilities introduced by future quantum decryption capabilities. By integrating hybrid quantum–classical frameworks, this approach enables the development of sustainable, adaptive, and transparent financial systems. The findings highlight quantum computing’s potential to advance financial inclusion, increase transaction speed, and improve systemic resilience. As global financial markets transition toward quantum readiness, the convergence of FinTech and quantum innovation is expected to redefine how digital assets are managed, traded, and secured—marking a paradigm shift toward quantum financial intelligence.
The exponential growth of digital finance—encompassing online banking, digital assets, decentralized finance (DeFi), and algorithmic trading—has intensified the need for robust cybersecurity frameworks. However, the rise of quantum computing presents a dual challenge: while it enables revolutionary advances in data analytics and optimization, it simultaneously threatens the cryptographic foundations of contemporary financial systems. This research explores the emerging field of quantum cybersecurity and its implications for safeguarding financial infrastructures in the post-quantum era. Traditional encryption methods such as RSA, ECC, and Diffie–Hellman key exchange are vulnerable to quantum attacks, particularly through Shor’s algorithm and Grover’s search algorithm, which can efficiently break asymmetric and symmetric cryptographic schemes. The study evaluates quantum-resistant cryptographic protocols—including lattice-based, hash-based, and multivariate polynomial encryption—as viable solutions for ensuring financial data integrity, transaction confidentiality, and regulatory compliance in quantum-vulnerable environments. Furthermore, it investigates Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as hardware-assisted techniques for achieving unconditional security in financial communications and transaction authentication. By integrating quantum cryptography, hybrid encryption, and AI-driven threat modeling, this work outlines a roadmap for financial institutions transitioning toward quantum-secure infrastructures. The findings demonstrate that quantum cybersecurity is not merely a defensive measure but a transformative enabler for resilient digital finance, aligning with global efforts to achieve technological sovereignty, financial stability, and sustainable innovation in the era of quantum computing.