Integrating artificial intelligence (AI) like the large language model (LLM) for smart contract auto-generation standardises performance and security, reduces human error, and offers accessibility for non-developers.In decentralised autonomous systems (DASs) like decentralised finance (DeFi), the ability to AI-generate smart contracts strengthens the decentralisation and automation characteristics of the applications.In order to increase the effectiveness of a smart contract's fully decentralised and autonomous development, this study benchmarks gas-saving patterns in AI-generated DeFi smart contracts.Three DeFI smart contract development scenarios: token generation (ERC-20), tokenised vault (ERC-4626), and flash loan (ERC-3156), and the state-of-the-art LLMs (Code Llama and Code Llama -Python) are explored to study the gas-saving patterns of AI-generated smart contracts.These results help optimise DeFi smart contracts created by AI regarding gas fees for the same operations.
This paper introduces Credance-Based Collateral Exchange (CBCE) as a formal category of financial instrument distinct from conventional repo and from existing distributed ledger collateral protocols. Building on the concept of credance-the collective anterior belief that makes a financial transaction possible before any track record exists (Laverriere, 2026)-we argue that a significant class of collateral exchange activity operates on the basis of credance rather than documentation. We formalise credance as a time-varying bilateral function C(Pâ, Pâ, t), define the credance threshold θ as a composite score of transaction history, temporal depth, and costly honouring, and propose a three-mode typology of CBCE instruments. We analyse the conditions under which each mode is optimal, examine the implications for Islamic repo market development and for distributed ledger technology design, and argue that blockchain-based collateral protocols have systematically failed to incorporate credance as a design variable. A credance-aware DLT architectureincluding a credance oracle, a zero-knowledge credance proof mechanism, and a variable collateralisation ratio governed by C(Pâ, Pâ, t)-would represent a genuinely novel class of financial instrument. The paper concludes with a research agenda including a call for collaboration between financial economists and DLT researchers to prototype the proposed architecture.
This paper provides a comprehensive examination of stablecoins, a class of cryptocurrency designed to mitigate the price volatility inherent in major digital assets like Bitcoin and Ethereum. By pegging their value to stable assets such as fiat currencies, commodities, or through algorithmic manipulation, stablecoins aim to serve as a reliable medium of exchange, unit of account, and store of value within the digital economy. Through systematic literature review methodology, this research traces the evolution of stablecoins, dissects their underlying mechanisms, and categorizes them into four primary types: fiat collateralized, commodity-collateralized, crypto-collateralized, and algorithmic. The paper analyzes their expanding use cases, from powering decentralized finance (DeFi) and revolutionizing cross-border payments to enhancing corporate treasury functions, while scrutinizing the significant risks they present, including de-pegging events, regulatory uncertainty, and systemic financial risks. The catastrophic collapse of the Terra/LUNA ecosystem serves as a critical case study, offering profound lessons on the vulnerabilities of algorithmic models. The research navigates the complex global regulatory landscape, comparing approaches from major jurisdictions including the United States GENIUS Act, European Union MiCA regulation, and UK FCA frameworks. By synthesizing market data, growth projections, and doctrinal analysis, this paper concludes with a forward-looking perspective on stablecoins' enduring role in the ongoing digitalization of finance and provides normative recommendations for balanced regulatory approaches that foster innovation while ensuring financial stability.
Private credit has grown into a multi-trillion-dollar asset class embedded within modern financial networks, yet the pathways through which stress in that sector can propagate into digital asset markets remain poorly understood. This paper examines two distinct contagion channels linking macroeconomic shocks to decentralized finance (DeFi): a macro deleveraging channel in which broad risk-off behavior spreads from traditional markets into cryptocurrencies, and a direct tokenization channel in which blockchain-based tokens representing private credit portfolios serve as collateral within automated DeFi lending protocols. Drawing on recent empirical developments, including the redemption restrictions imposed by a large BlackRock private credit fund in early 2026, the bankruptcy of First Brands Group in September 2025, and the associated stress in tokenized credit instruments on the Morpho lending protocol, the analysis constructs a conceptual contagion framework. The paper also situates these dynamics within the shadow banking theory of Gennaioli, Shleifer, and Vishny (2013), arguing that tokenized private credit instruments exhibit a liquidity paradox: while blockchain infrastructure enables continuous trading, the underlying credit exposures remain illiquid. The paper concludes that as tokenization expands the integration between traditional and digital finance, regulatory frameworks must evolve to monitor cross-market contagion channels, enforce transparency in tokenized asset structures, and account for the systemic implications of automated liquidation mechanisms.
Decentralized finance (DeFi) vaults are smart-contract-based asset management systems that pool deposits, execute programmable strategies, and mint tokenized shares representing claims on underlying assets and strategy performance. As vault designs have evolved from early yield aggregators to modular, actively managed systems, a new control layer, curation, has emerged to select strategies, configure risk parameters, and coordinate operational execution, introducing principal-agent dynamics and new failure modes. This paper systematizes DeFi vault architectures and curator-mediated control planes through (i) a unified system model and formal definitions for share accounting, roles, and operational dependencies, and (ii) three complementary taxonomies covering vault exposures and objectives, curator governance and accountability mechanisms, and strategy execution patterns together with their failure modes. We further map a representative set of production protocols to the proposed dimensions. The frameworks in this work aim to support rigorous analysis and safer design of blockchain-based financial applications.
This paper proposes a novel decentralized financial instrument-the Algorithmic Yield-Multiplier Note-to mathematically resolve the classical agency friction between debt and equity constituencies. Under legacy market microstructures, the strict fungibility constraints and asynchronous settlement latencies of traditional clearinghouses preclude the issuance of dynamic, cross-asset covenants, thereby exacerbating asset substitution and debt overhang during macroeconomic distress. We circumvent these architectural bottlenecks by migrating corporate liability to a programmable, tokenized infrastructure. We introduce a continuous, state-dependent yield function that algorithmically scales a bondholder's coupon rate relative to their cryptographically verifiable equity holdings, structurally coercing fixed-income investors into an Endogenous Capital Loop. To defend this mechanism against high-frequency decentralized finance (DeFi) exploits, such as flash-loan and snapshot arbitrage, we engineer a continuous, path-dependent knockout barrier, đ đ (đĄ), that permanently collapses the yield premium upon any instantaneous breach of the requisite equity threshold. Furthermore, we resolve the resultant fungibility crisis by constructing a hybrid Decentralized Exchange (hDEX) utilizing atomic swaps to govern secondary market velocity. By algorithmically enforcing a liquidity tax (đ), a yield-surrender covenant (đž), and strict cryptographic vesting lock-ups (đ penalty), the mechanism fundamentally traps institutional capital. Ultimately, we demonstrate that this programmable constraint system monetizes investor duration risk and artificially suppresses the realized covariance of the firm's stock. By fusing the fixed-income and equity constituencies, the corporate treasury engineers a Pareto-improving capital structure that dramatically lowers the effective Weighted Average Cost of Capital (WACC) and insulates the enterprise value from systemic market contagion.
Digital assets have become a significant and indispensable part of the modern financial system and have brought innovations in the areas of payments, investments, and financial intermediation. However, their expansion brings numerous regulatory challenges, particularly with regard to preventing money laundering, user identification, the legal treatment of decentralized finance, and privacy protection. Approaches to the regulation of digital assets vary significantly among jurisdictions - while some countries introduce comprehensive legislation, others apply restrictive or fragmented policies. Serbia has positioned itself as one of the first countries in the region to adopt a specific Law on Digital Assets (2020), thereby establishing a regulatory framework for this market. This paper analyses the legal challenges of digital assets, exploring national and international regulatory approaches, including the European Union's MiCA Regulation. It also examines the need to strike a balance between fostering innovation and ensuring the stability of the financial system. The key finding is that continuous international cooperation and a flexible regulatory framework are necessary to enable the sustainable development of digital assets and the technologies that support them.
DEX like Uniswap v3 has gained significant attention in the blockchain industry, and understanding the driving factors behind liquidity provision is crucial for the platformâs success and adoption in the decentralized finance space. This study investigates liquidityproviders (LPs) behaviour in Uniswap v3 and their response to key events and developments,such as the EIP-1559 and the FTX collapse, to provide a comprehensive picture ofthe dynamics in the DeFi ecosystem. We explore LPs behaviour by assembling a datasetof 746,438 pool-day records associated with 2,228 Uniswap smart contracts. Two hypotheses,the âFee reward hypothesisâ and the âImpermanent loss avoidance hypothesisâare examined to understand LPsâ motivations and strategies. We further study the impactof EIP-1559 on liquidity provision, revealing a convergence in capital efficiency betweenlow and high-efficiency swap pools following its implementation. Last, we assess howthe FTX collapse affected trading volume, uncovering a more notable decline in tradingactivity among pools comprising exclusively of unstable coins as opposed to those withstablecoins only.
This paper proposes Global Adaptive Equity Pricing (GAEP), a novel AI-driven framework for moderating economic inequality through real-time, consumption-event-based price personalization. At each domestic purchase, biometric verification links to encrypted networth data to compute a progressive adjusted price using the Wealth Elasticity Pricing Equation (WEPE). Excess payments from higher-net-worth individuals fund a transparent Gini Moderation Fund (GMF) for AI-optimized redistribution targeting a blended Gini coefficient of â0.30. Tunable parameters enable governments to control moderation velocity, balancing equity gains against capital retention risks in wealth-attracting jurisdictions. Calibrated to Singapore's 2025-2026 data (income Gini after transfers and taxes: 0.379; market income Gini before transfers: 0.452; wealth Gini: 0.55; top 1% hold ~14%, top 5% ~33% of household wealth), agent-based simulations project 15-41% Gini reductions over 20 quarterly cycles. Ethical safeguards include zero-knowledge proofs, blockchain-audited aggregates (no personal data exposure), fairness audits, and positive incentives. GAEP extends Gini theory and computational economics by integrating biometric technology with redistributive algorithms, distinct from usage-tiered tariffs or surveillance pricing. It offers policymakers a pathway for dynamic, consumption-led equity in AI-augmented economies while preserving innovation incentives.
On April 1, 2025 Circle Internet Group, Inc. (hereinafter referred to as "Circle," "the Company," or "issuer", filed a registration statement on Form S-1 with the U.S. Securities and Exchange Commission (SEC) contemplating the "offering [of]⌠shares of Class A common stock." After the additional filing of prospectus amendments, the final offering prospectus is dated August 12, 2025. The offering of 34,000,000 shares was priced before market opening on June 4, 2025 at $31 per share. Circle's disclosure documents provide an excellent description of the many new blockchain-enabled Decentralized Finance [DeFi] technological and operational challenges facing participants and investors. These valuable disclosures benefit all who seek to understand these important developments impacting the future stability of global financial and currency markets. It is the actual disclosure language of Circle Internet Group, Inc. in their prospectus that is the focus of the article.
Flash loans enable uncollateralized borrowing within a single transaction, providing capital efficiency and arbitrage opportunities in decentralized finance (DeFi). However, when combined with composable protocols and reactive state changes, flash loans can induce feedback loops that amplify liquidity, manipulate pricing signals, and bypass economic safeguards. This paper defines Flash Loan Feedback Loops as recursive transaction patterns in which temporary liquidity repeatedly influences protocol state, enabling extraction of value without proportional risk exposure. We analyze structural conditions that permit such loops, demonstrate why existing mitigations are insufficient, and propose a logic-layer enforcement framework that constrains state-dependent recursion. The approach restores causal integrity between capital commitment and protocol outcomes, addressing a core systemic vulnerability in DeFi architectures.
High-yield decentralized finance (DeFi) lending protocols attract capital by offering returns that exceed organically sustainable market demand. This paper defines <b><i>Anchor Protocol Overexposure</i></b><b> </b>as a systemic risk condition in which outsized, subsidy-driven yields concentrate liquidity into a single mechanism, creating hidden leverage, correlated withdrawal behavior, and reflexive collapse dynamics. Using Anchor Protocol as a representative archetype, the paper analyzes how yield subsidies, composability, and perception-driven stability interact to generate unsustainable exposure across interconnected DeFi ecosystems. We further demonstrate why transparency, disclosure, and governance-based controls fail to mitigate this class of risk. Finally, the paper outlines a logic-layer enforcement model capable of constraining yield-induced systemic fragility prior to the onset of collapse dynamics.
The tokenization of real-world assets (RWAs) has emerged as one of the most consequential developments at the intersection of traditional finance and decentralized finance (DeFi). By representing ownership or economic rights in instruments such as government bonds, private credit, and real estate through blockchain-based tokens, tokenization has enabled traditional assets to be integrated into decentralized lending platforms. As these markets have matured, participants have developed increasingly sophisticated yield-enhancement strategies, among the most notable of which is leveraged looping. This recursive yield-amplification technique involves depositing tokenized RWAs as collateral, borrowing stablecoins against that collateral, and reinvesting the borrowed funds to purchase additional RWA tokens. Through successive iterations, investors create and expand leveraged exposure to the underlying asset's yield while maintaining the same initial capital. This paper examines the mechanics and economic rationale of leveraged looping strategies, analyzes the structural risks arising from the integration of traditional financial assets into decentralized financial infrastructure, and evaluates the regulatory and financial stability implications of these strategies. Particular attention is directed toward the mismatch between the continuous operation of DeFi platforms and the slower settlement, valuation, and liquidity characteristics of the underlying assets. The analysis draws on recent market data, protocol-level case studies, and relevant academic literature. The paper concludes that leveraged looping represents a technologically innovative adaptation of traditional leverage and carry-trade strategies but may introduce new forms of systemic risk if liquidity constraints and valuation opacity are not adequately addressed by market participants and regulators.
Blockchain technology, in simple words, is an innovative force that democratizes the methodologies of financial transactions by creating safe, traceable, and unalterable digital data. This research investigates how blockchain increases financial transparency in banking, government, and supply chain management for different sectors. It identifies block chainâs core features: decentralized ledgers, real-time auditing, and transparent data sharing, in total reducing information asymmetry and thus fraud, increasing public trust. This study will focus on the role of block chain in financial reporting, as immutable transaction records ensure audit-free error-free error checks and compliance with regulatory standards. The primary use cases for this are anticorruption government procurement systems, banking networks improving fraud detection, and supply chain platforms ensuring product traceability. Smart contracts integrated into financial processes help reduce intermediaries and promote accountability. The paper concludes with an overview of emerging trends in zero-knowledge proofs, decentralized finance, and blockchain-based governance systems that may transform the standards of transparency. Some policy recommendations for leaders are investment in blockchain research, the development of regulatory frameworks, and fostering cross-industry collaboration. Blockchain technology is expected to redefine financial transparency through accountability, fraud reduction, and increased public trust in digital economies.
The AI-Powered Financial Insights Platform is designed to address the increasing complexity of decentralized applications and digital asset management systems. As blockchain ecosystems expand, users often struggle to interpret detailed transaction data, understand staking mechanisms, or navigate complex on-chain information. This platform leverages advancements in Artificial Intelligence, real-time blockchain indexing, and decentralized protocols to convert unintuitive data into easily interpretable financial insights while maintaining security and trust. By utilizing the Cardano network as its foundation, the platform provides a research-driven, layered architecture that ensures scalability and sustainability as separate principles. This platform represents a paradigm shift in wealth management and fiscal oversight by transitioning from reactive reporting to predictive intelligence. At its core, the system utilizes a sophisticated multi-agent AI architecture designed to ingest, normalize, and analyze massive volumes of heterogeneous financial data. By synthesizing information from global market indices, real-time news sentiment, and individual spending patterns, the platform constructs a 360-degree financial profile. It employs advanced Long Short-Term Memory (LSTM) networks and Transformer-based models to forecast cash flow trajectories and identify potential liquidity risks before they manifest. This proactive approach allows users-whether institutional investors or private individuals-to navigate volatile markets with a data-backed roadmap rather than relying on lagging indicators. Beyond mere data aggregation, the platform emphasizes contextual relevance. The "Insight Engine" utilizes Natural Language Generation (NLG) to translate complex algorithmic outputs into high-level executive summaries, effectively democratizing access to professional-grade financial analysis. Security is woven into the fabric of the application through a hybrid backend-combining the raw computational speed of C++ for high-frequency data processing with the flexibility of Python for AI model deployment. This ensures that the system remains scalable and responsive under heavy loads.
The rapid expansion of private credit markets over the past decade has reshaped the structure of corporate lending and created a large parallel credit system operating outside traditional banking institutions. At the same time, developments in blockchain technology have enabled decentralized finance platforms to tokenize real-world credit assets, allowing investors to gain exposure to lending pools through digital tokens. These developments raise important questions about whether tokenization meaningfully changes the economic risks associated with private lending or simply redistributes traditional credit exposures through new technological infrastructure. This paper examines emerging signs of stress in the private credit market during 2025 and 2026 and evaluates whether these developments may transmit to decentralized finance lending platforms that provide exposure to tokenized credit assets. Drawing on literature related to shadow banking, liquidity risk, financial contagion, and decentralized finance, the analysis explores structural similarities between traditional private credit funds and blockchain-based lending pools. The findings suggest that although tokenization can improve transparency and settlement efficiency, it does not eliminate the fundamental credit risks associated with illiquid lending markets. Borrower default risk, leverage, sector concentration, and macroeconomic cycles continue to shape outcomes in both traditional and tokenized credit markets.
Blockchain technology has progressed from a novel notion in bitcoin to a widely used system in nations throughout the world, encompassing a wider range of industries, including agriculture. The decentralized nature of blockchain allows for increased transparency, cheaper transaction costs, and greater financial inclusion. This study reveals a comparative analysis of the applications of tokenized stocks, blockchain adoption, and fintech ecosystem between two countries, an emerging one, Turkiye, and a developed country, Germany, based on studies conducted between 2019 and 2025, to see how they differed in their approaches to implementation as Germany taking the lead in government adoption and Turkiye being the lead in fast adoption of innovations, as well as how each of the countries can learn from the other by presenting similarities and differences that will guide this research paper.
In this article, we compare financial knowledge levels and identify the determinants of financial attitudes among 16-20-year-old students in Italy and the Autonomous Community of Galicia (Spain). We combine cross-country comparative evidence with data-driven variable selection based on machine learning techniques and theory-driven modelling of financial attitudes. Our study offers an original contribution to the literature on youth financial literacy and behaviour in emerging digital financial domains, namely instalment-based credit solutions and cryptocurrency investments. Our findings reveal that Galician students display higher average financial knowledge than Italian ones and have a higher propensity to use instalment payments and to invest in cryptocurrencies. Financial knowledge plays a central role in shaping both credit and investment attitudes, alongside experience, income, and behavioural traits, with significant cross-country differences. More specific knowledge in each domain is associated with more cautious attitudes, suggesting that deeper understanding relates with more prudent behaviour. Among Italian educational pathways, technical institutes appear to be the only track able to substantially reduce the literacy gap. These insights highlight the need for a reform of financial education pathways, with greater emphasis on experiential learning and student-involving teaching strategies.
Purpose â This paper investigates whether perpetual futures can satisfy Islamic prohibitions on riba (interest), gharar (uncertainty), and maysir (speculation) â a question unaddressed by prior literature. Design/methodology/approach â The study develops a four-category taxonomy of perpetual futures by funding-formula structure, collects 39,406 unique funding-rate intervals across four platforms over 365 days (February 2025 â February 2026), and analyses a cross-platform comparison â with a placebo design â between interest-bearing and interest-free DEX protocols. Shariah analysis applies El-Gamal's riba conditions, Kamali's standardisation criterion, and Salamon's maysir test. Findings â The interest parameter in CEX funding formulas is mathematically unnecessary. dYdX v4 operates with I = 0 and produces a funding distribution distinct from CEX platforms (Cohen's d = 0.782 at native cadence, 0.703 cadence-matched; Kolmogorov-Smirnov D = 0.506). Hyperliquid â a DEX using the CEX formula â clusters with CEX. Formula structure rather than exchange architecture governs the riba dimension of the Shariah classification. Originality/value â The first empirically validated framework demonstrating that the riba component of perpetual futures funding is structurally removable, correcting an error of extrapolation in Islamic finance scholarship that treated a single CEX implementation as representative of the entire instrument class. Ownership (qabdh) and delivery-intent are scoped as open questions for cash-settled synthetic perpetuals. Research limitations/implications â The empirical sample covers a single 365-day period; extension to additional time windows would strengthen external validity. Practical implications â The taxonomy provides a screening tool for Shariah boards, fund managers, and exchange designers evaluating Shariah-compliant perpetual futures instruments.
This study examines whether the launch of the iShares Bitcoin Trust (IBIT) is associated with a persistent reconfiguration of Bitcoin's second-moment dynamics, extending beyond the short-run announcement effects documented in prior event studies. Using a multi-method empirical framework on daily data from 2020 to 2026 and a sustained twenty-eight-month post-event window, we document a durable transformation of volatility, persistence, and tail risk rather than a transitory announcement response. While Chow, HAC-Wald, Andrews sup-F, and PELT procedures fail to reject mean stability, annualized volatility declines by approximately 19% to 28% across estimators, with close-to-close, Parkinson, and Garman-Klass measures all yielding statistically significant reductions. EGARCH(1,1,1) estimates indicate that the implied half-life of volatility shocks shortens from approximately 45 to 12 trading days, with a complementary GJR-GARCH specification yielding a post-IBIT half-life of approximately 25 days; both specifications point to a substantial acceleration of shock dissipation. Extreme downside exposure, measured by 1% Value-at-Risk and Expected Shortfall, compresses by approximately 44%, with bootstrap inference confirming statistical significance. At the portfolio level, Bitcoin's marginal contribution to tail risk declines by approximately three-quarters at standard allocation weights. Non-parametric ICSS variance-break detection identifies no discrete break within thirty days of the launch, consistent with a gradual regime adjustment rather than an instantaneous structural switch. Robustness tests using weekly returns, exclusion of the April 2024 halving window, and alternative breakpoints confirm the findings. The results contribute to the literature on cryptocurrency risk dynamics by documenting a coincident compression in Bitcoin's second-moment characteristics around the spot ETF era, a pattern distinct from the volatility responses previously documented for derivative-based products. In contrast to short-window event-study evidence, the compression is shown to persist and to extend into the tail of the return distribution and the portfolio risk budget, dimensions outside the scope of an announcement-window design.