Digital loans offer rapid, simple, and usually paperless transactions, and have radically changed the lending industry. The whole loan process is easy to access and effective; there are a number of threats associated with the availability of data online. As a crucial kind of digital loan, flash loans put additional pressure on banks to maintain security because they return to the same block of the Blockchain, making it more likely that they will be tampered with. It is important to address the possibility of predatory lending practices that target weaker payees. As a result, this chapter has created a security protocol based on the idea of digital wallets with self-sovereign identity (SSI) and decentralized finance (DeFi) for cryptocurrencies, which are secured by the Xsalsa20 algorithm. It has satisfied specs and is further improved by the application of the Crow search algorithm (CSA), which ensures quick and effective search results. We were able to verify several security features, including data authenticity and complete forward secrecy, by analyzing the suggested system.
Abstract Crypto-asset services without governance mechanisms maximize transparency and censorship resistance through automation but may sacrifice adaptability to changing market conditions, depending on their institutional design. This study examines the consequences of user adoption for a fully automated stablecoin bank that offers zero-interest loans: Liquity Protocol. Using 1586 daily observations from April 2021 to August 2025, this paper investigates whether user decline stems from portfolio allocation rationale or internal design constraints, under heightened competitive pressure and a tight monetary policy environment. We employ probit specifications to analyze the relationship between stablecoin (LUSD) peg deviations and three behavioral outcomes: collateralization adjustments, loan position closures, and capital withdrawals. Results provide strong evidence that negative peg deviations predict defensive position management, with marginal effects that are 4–6 times larger during post-competitive shock periods. The closure of loan positions exhibits the greatest sensitivity, with 8.7 percentage points across the pre-shock period versus 51.8 percentage points post-shock. In comparison, collateralization ratios increased significantly by 6.0 percentage points, versus 38.8 percentage points in the same periods, indicating a systematic deterioration in capital efficiency. By contrast, the directional probability of capital flight during the post-shock period remains comparatively insignificant. An extension analysis incorporating yield differentials from major competing services is implemented using both probit and OLS specifications. The OLS results show that yield differentials predict larger capital outflows in the pre-shock period ( $$p = 0.023$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.023</mml:mn> </mml:mrow> </mml:math> ), while full-sample and post-shock specifications are not significant. Concurrently, the probit results reveal significant links with the direction of capital withdrawal in the pre-shock period ( $$p = 0.007$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.007</mml:mn> </mml:mrow> </mml:math> ), with no further significant associations in the post-shock period. However, yield differentials show no significant predictive power for the magnitude or direction of position management or collateralization behavior in any specification. The evidence points to a coexistence of mechanisms throughout different temporal periods: yield competition acts as a magnitude amplifier for capital flows prior to 2024, when competitive pressure had not reached its peak. In contrast, as competition reaches a high point for stablecoin saving instruments by early 2024, the systematic day-to-day behavioral dynamics of position management (loan positions and collateral) becomes more consistent with protocol-internal design frictions. Regime-based robustness checks examining Federal Reserve tightening and major crypto market shock periods reveal distinct temporal patterns, with macro stress periods leading to capital flight, whereas active position management in the subsequent period of increasing competitive stress does not. These findings provide insight into the critical design trade-offs between deterministic automation and adaptive governance in the decentralized finance industry, particularly for decentralized banks, with implications for protocol developers and researchers studying the viability of governance-free design subject to alternating external market conditions.
Modern banking systems simultaneously maintain monetary values across computation (8-10 decimal places), ledger posting ($\mathbf{4}-\mathbf{8}$decimal places), and customer presentation (2 decimal places) scales. In distributed microservice and event-driven architectures, unmanaged transitions between these scales introduce rounding drift, ledger divergence, reconciliation breaks, and non-deterministic replay risks that threaten audit compliance and regulatory reporting accuracy. This paper investigates how distributed banking systems can manage multi-scale monetary precision while preserving deterministic balances and auditability. The proposed Multi-Scale Monetary Precision Model (MSMP) defines three scale classes, three explicit precision boundaries, and four correctness invariants governing deterministic posting, ledger conservation, presentation consistency, and fractional carry forward. A taxonomy of three canonical failure modes, namely early rounding, boundary truncation, and nondeterministic aggregation, is presented together with four architectural patterns designed to ensure precision-safe monetary propagation. Evaluation on a synthetic bankingrealistic workload (106accounts over 365 days) demonstrates that MSMP reduces cumulative monetary drift by up to 99.9 %, eliminates reconciliation breaks entirely, and achieves 100 % replay determinism, with computational overhead of approximately 18 %. These results establish monetary precision governance as a first-class architectural control for audit-ready distributed financial systems.
Decentralized finance (DeFi) protocols now intermediate over USD 100 billion in value, including regulated stablecoins and tokenized assets deployed as collateral, yet no widely adopted framework operationalizes risk assessment at the rigor institutional adoption demands. Existing approaches emphasize protocol-specific parameter optimization or conceptual taxonomies without providing explainable, composability-aware, and structurally independent assessment methodologies. We propose a nine-dimension DeFi risk assessment framework extending the six-dimension taxonomy introduced by Moody's Analytics and Gauntlet with three novel dimensions: composability risk, comprehension debt, and temporal risk dynamics. We additionally introduce a transparency confidence modifier separating assessment reliability from risk severity. The framework is grounded in structural analysis of protocol dependencies conducted through an ontology-based protocol intelligence infrastructure covering more than 8,000 DeFi protocols. We retrospectively analyze 12 major DeFi-related incidents from 2024-2026 representing approximately USD 2.5 billion in direct losses. Five of the 12 incidents require at least one novel dimension for complete root-cause characterization, including the two highest-systemic-impact events in the dataset.
Permissionless Proof-of-Stake (PoS) economic security is predicated on the high cost of violating consensus safety or liveness. We show that liquid staking introduces additional risks that are not captured by standard PoS economic security arguments. Through an empirical study of Ethereum data, we find that the operational performance of liquid staking pools is positively associated with subsequent normalized liquid staking token (LST) returns. Motivated by this, we present a cross-layer attack: a low-stake adversary can manipulate the consensus protocol to degrade a target pool's performance and take application-layer positions that profit if the market reprices the corresponding \gls{LST} in-line with the historically observed association. To make the consensus layer manipulation concrete, we develop a deep reinforcement learning (DRL) framework to automatically discover attack strategies. Our evaluation shows that the learned strategies can recover near-optimal theoretical attacks and uncover new manipulation behaviors that significantly degrade target pool performance. We further characterize feasible application-layer monetization channels and analyze leveraged shorting in detail using Monte Carlo simulations, showing that such attacks can be profitable with over one-half probability for LSTs of major staking pools. Our findings reveal a previously overlooked attack surface in PoS systems with liquid staking and expose a gap between consensus and economic security.
Rapid urbanization and the exponential growth of vehicles have led to severe traffic congestion, increased travel time, fuel consumption, and environmental pollution in metropolitan cities.Traditional traffic control systems, which rely on fixed-time signals and manual monitoring, are inadequate to handle dynamic and unpredictable traffic conditions.This project proposes a Smart Traffic Management System designed to optimize traffic flow and reduce congestion using advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and real-time data analytics.The system integrates smart sensors, cameras, and GPS-enabled devices to continuously monitor traffic density, vehicle movement, and road conditions.Data collected from these sources is processed using machine learning algorithms to predict traffic patterns and dynamically adjust traffic signal timings.Additionally, the system provides real-time route guidance to drivers through mobile applications and digital signboards, helping to distribute traffic evenly across the road network.Emergency vehicle prioritization and incident detection mechanisms are also incorporated to enhance response efficiency and safety.
This paper introduces a heterogeneous macroeconomic model of a Proof-of-Stake (PoS) network to analyze the long-term centralizing effects of external traditional finance (TradFi) yields. We model a continuum of rational actors divided into two distinct classes: investors, who optimize portfolios between staking and external variance-dominated investments, and consumers, who balance staking yields against the transactional utility of holding liquid assets. By employing a quasi-linear utility function to model consumer behavior, we derive a cubic polynomial that strictly defines the unique macroeconomic equilibrium of the coupled network. The model demonstrates that, at scale, external macroeconomic factors force the complete institutional capture of the PoS consensus layer. Because investors have access to external risk premiums, their wealth compounds exponentially, leading to massive capital inflows that crush the protocol's internal staking yield to effectively zero. We show that as the yield is crushed, consumer wealth becomes strictly upper-bounded. Ultimately, consumers are forced to cease staking entirely and hold all remaining wealth in liquid form to satisfy their transactional constraints.
This case study examines the transformative impact of Decentralized Finance (DeFi) on India’s traditional banking sector through a multi-stakeholder perspective. Drawing on both quantitative performance indicators and qualitative stakeholder insights, the study explores how DeFi influences operational efficiency, financial inclusion, and regulatory compliance. The findings indicate that while DeFi enhances transaction efficiency and expands access to credit, its integration into India’s financial ecosystem is constrained by regulatory ambiguity, cybersecurity concerns, and infrastructural disparities. The case highlights the need for a hybrid financial architecture supported by collaborative governance and adaptive regulatory frameworks.
Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to volatile market conditions. Existing risk management tools utilize static health-factor thresholds, which are reactive and fail to distinguish between administrative "dust" cleanup and genuine insolvency. In this work, we propose an autonomous agent that leverages time-to-event (survival) analysis and moves beyond prediction to execution. Unlike passive risk signals, this agent perceives risk, simulates counterfactual futures, and executes protocol-faithful interventions to proactively prevent liquidations. We introduce a return period metric derived from a numerically stable XGBoost Cox proportional hazards model to normalize risk across transaction types, coupled with a volatility-adjusted trend score to filter transient market noise. To select optimal interventions, we implement a counterfactual optimization loop that simulates potential user actions to find the minimum capital required to mitigate risk. We validate our approach using a high-fidelity, protocol-faithful Aave v3 simulator on a cohort of 4,882 high-risk user profiles. The results demonstrate the agent's ability to prevent liquidations in imminent-risk scenarios where static rules fail, effectively "saving the unsavable" while maintaining a zero worsening rate, providing a critical safety guarantee often missing in autonomous financial agents. Furthermore, the system successfully differentiates between actionable financial risks and negligible dust events, optimizing capital efficiency where static rules fail.
Angelo Ranaldo, Ganesh Viswanath-Natraj, Junxuan Wang
Abstract We conduct the first comprehensive study of blockchain currencies—stablecoins pegged to fiat currencies and traded on decentralized exchanges (DEXs). Using transaction-level data linked to wallet characteristics, we show that prices in these markets are generally efficient, though constrained by blockchain frictions such as gas fees and Ether volatility. DEX rates closely track traditional currency markets through arbitrage and informed trading. Traders with substantial market share and access to primary markets exert greater price impact, reflecting informational advantages. While blockchain markets may improve access for customers excluded from traditional venues, their scalability depends on addressing frictions inherent to decentralized trading.
Abstract This study proposes a methodological strategy composed of econometric techniques and time series modelling to analyse the dynamic asynchrony between Bitcoin and a basket of traditional sustainable financial assets and emerging markets over a 10-year period marked by major economic and financial changes. The centrepiece of this proposal is the Relation Index that combines vector autoregression and detrended cross-correlation analysis to capture linear and nonlinear dependencies, causality, and time-scale sensitive correlations. Thus, this research fills existing gaps in understanding cross-market interdependencies by integrating cryptocurrencies, sustainability indices, and emerging economies into a rigorous multivariate time series framework. Sustainability indices, emerging markets and Bitcoin have shown a growing correlation since 2020, with both interest rates and Bitcoin having strong autoregressive components. The findings indicate that emerging market equities have undergone a structural shift towards synchronisation with global risk assets, with a correlation index that frequently exceeds 0.6 in periods of systemic stress. This evolution highlights the decline in the advantages offered by diversification in developed and developing economies in a complex and interrelated financial environment.
The emergence of decentralized finance (DeFi) has prompted a new, highly interwoven financial system in which the stability of the financial system is fundamentally dependent upon the existence of digital assets, in particular stablecoins, that serve as both a method of conducting transactions, collateral, and a source of liquidity. Although DeFi is said to be efficient, programmable, and disintermediated, the structural complexity and composability of the DeFi system also create new systemic- risk channels that are similar to the impact of fragilities in conventional finance (Auer et al., 2024; Xu et al., 2024). The role of stablecoins in this architecture is to facilitate trading, leverage, and settlement of protocols, though the design and collateralization process puts them at risk of derailing the stablecoin and liquidity shocks and runs (Catalini et al., 2022; Hoang and Baur, 2024). These dynamics are similar to traditional bank run and liquidity crisis theories, in which the lack of coordination and redemption could cause damaging withdrawal effects (Diamond and Dybvig, 1983; Bernardo and Welch, 2004). In the case of the elements of DeFi, the volatility can spread very quickly between lending pools, automated market makers, and cross-chain bridges, facilitating the transfer of stress and volatility across platforms and asset classes (Zieba et al., 2019; Pagnottoni, 2023). The lack of centralized backstops, along with the algorithmic governance and large leverage, also serves to further enhance the risk of local perturbations developing into system-wide contagion. Such vulnerabilities have increased the arguments for risk-sensitive system design, greater transparency, and regulatory coordination to reduce spillovers to the financial system more generally (FSB, 2018; Manaa et al., 2021; Fantacci and Gobbi, 2024). Altogether, the discussion shows that the concept of stablecoins is an important crossroads in the stability environment of DeFi: not only do they allow markets to operate, but also they are a primary medium through which runs and shocks are propagated. The knowledge of these mechanisms is paramount in the formation of the resilient protocol design, supervisory systems, and eventual research on systemic risk of programmable financial systems.
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 AI’s 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 AI’s 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
Crypto currency has emerged as one of the most disruptive innovations in modern financial history. Beginning with the introduction of Bitcoin in 2009, decentralized digital currencies have challenged traditional financial systems by enabling peer-to-peer transactions without centralized intermediaries. This paper examines the impact of cryptocurrency on global financial systems, including banking, monetary policy, financial inclusion, cross-border payments, and regulatory structures. It explores both opportunities—such as decentralization, efficiency, and innovation—and risks, including volatility, regulatory uncertainty, financial crime, and systemic threats. The study also analyses the rise of decentralized finance (DeFi) and Central Bank Digital Currencies (CBDCs) as responses to the growing influence of blockchain-based financial models. The research concludes that while cryptocurrencies present transformative potential, their long-term integration into financial systems will depend on regulatory clarity, technological scalability, and macroeconomic stability.
The persistence of global financial instability, sovereign debt fragility, inflationary volatility, and asymmetric currency dependence has intensified scholarly debate regarding the structural limitations of centralized fiat-monetary regimes. This study advances a theoretically grounded and institutionally operational Monetary Plurality Framework designed to enhance systemic resilience through diversified currency architecture, asset-anchored valuation, and hybrid governance integration. Drawing upon interdisciplinary monetary theory, comparative institutional analysis, and resilience economics, the research develops a multi-tier monetary ecosystem combining centralized macro-stability with decentralized micro-adaptability enabled by distributed ledger technologies. The findings suggest that monetary diversification reduces crisis transmission, strengthens domestic productive linkage, and improves long-term financial sovereignty. The study contributes to the literature by synthesizing complementary currency theory, asset-backed monetary design, and digital governance economics into a unified resilience-oriented model suitable for volatile global conditions.
As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bank–year observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m.
Decentralised finance (DeFi) has profoundly reshaped global capital markets, enabling automatic transactions, eliminating the need for intermediaries, and accelerating transaction settlement times. Despite these significant advancements, institutional involvement in DeFi remains very low. The lack of institutional participation can be attributed to the lack of an enforceable compliance mechanism at the protocol level; that is, once a transaction is confirmed as having been completed on the blockchain, it cannot be undone or disputed in any meaningful way. The existing compliance mechanisms are primarily retrospective, meaning that they generate alerts after a transaction has occurred instead of preventing illicit transfers in advance. Regulated financial institutions that transact in cryptocurrency bear the ultimate financial risk and regulatory burden. The UK FCA has made it very clear through CP25/41 that there are now specific regulatory expectations regarding the existence of adequate pre-settlement controls [2]. We introduce AMTTP Version 4.0, which has been designed to have a four-layer architecture explicitly intended to support deterministic compliance enforcement in DeFi institutions. Layer I provides SDKs, REST APIs, and web applications intended for programmatic and human interaction with AMTTP; Layer II provides a compliance orchestration layer that combines (i) machine learning risk scoring (ii) graph analysis (iii) sanctions screening, and (iv) policy adjudication into a single deterministic decision-making matrix; Layer III consists of an offline training pipeline with a Composite Teacher that uses an AutoencoderEnhanced XGBoost (w = 0.4), seven FATF AML Mode Patterns (w = 0.3), and graph structural properties (w = 0.3) in order to produce pseudo-labels (SLPs) for the Student pipeline across 2,640,000 transactions; and finally, Layer IV supports the physical infrastructure for AMTTP deployment, which consists of 18 smart contracts on Ethereum Sepolia, 17 containerised microservices, and a Database Persistence Tier (MongoDB, Redis, Memgraph, IPFS). The Infrastructure Security features multioracle threshold signatures, replay protection & zkNAF a zeroknowledge proof framework that allows for privacy preserving verification of KYC credentials, risk ranges & non-membership from sanctions. In addition, TLS Encryption, Rate Limiting, Cloudflare Tunnel integration & the UI Integrity Service provide an additional layer of protection at the infrastructure level. This paper aims to demonstrate that deterministic compliance can be integrated into decentralised finance at an architectural level. In order to support this assertion, the client SDKs (TypeScript and Python) are released as open source.1
Decentralized Finance (DeFi) has successfully rebuilt the plumbing of Wall Street (Trading, Lending, Derivatives) but has failed to replicate its engine: Credit. Currently, all DeFi lending is Over-Collateralized. To borrow $1.00, a user must deposit $1.50 in assets. This is not "Credit"; it is merely "Liquidity Swapping." It restricts DeFi to wealthy speculators and excludes 99% of global borrowers who need capital precisely because they do not have assets to pledge. The Klyrox Sovereign Credit Protocol introduces the first scalable framework for Under-Collateralized Lending on-chain. By transforming the Klyrox Identity Token (Epistemic Capital) into a programmable "Credit Score," we allow users to pledge their History instead of their Assets. This paper outlines the mathematical risk models that allow lenders to safely issue loans with 50% or even 0% collateral, unlocking a trillion-dollar market for on-chain personal finance.
Private agents do not internalize the impact of their investment decisions on the sovereign’s bond prices and default risk. Therefore, a standard externality argument implies that investment is insufficient and that a subsidy can improve welfare, if financed by non-distortionary means. We contrast this logic with a countervailing force. When the sovereign is impatient relative to households, plausibly due to political economy factors, it finds laissez-faire capital accumulation excessive and might prefer instead to tax it. We embed both mechanisms in a sovereign default model with decentralized capital investment, long-term public debt, and stochastic trend growth, calibrated to salient features of the Spanish economy. We find that the impatience channel dominates quantitatively, to such an extent that laissez-faire is preferable to the government’s ideal fiscal policy, based on households’ welfare.
Liquidation of collateral are the primary safeguard for solvency of lending protocols in decentralized finance. However, the mechanics of liquidations expose these protocols to predatory price manipulations and other forms of Maximal Extractable Value (MEV). In this paper, we characterize the optimal liquidation strategy, via a dynamic program, from the perspective of a profit-maximizing liquidator when the spot oracle is given by a Constant Product Market Maker (CPMM). We explicitly model Oracle Extractable Value (OEV) where liquidators manipulate the CPMM with sandwich attacks to trigger profitable liquidation events. We derive closed-form liquidation bounds and prove that CPMM transaction fees act as a critical security parameter. Crucially, we demonstrate that fees do not merely reduce attacker profits, but can make such manipulations unprofitable for an attacker. Our findings suggest that CPMM transaction fees serve a dual purpose: compensating liquidity providers and endogenously hardening CPMM oracles against manipulation without the latency of time-weighted averages or medianization.
Amid the institutionalization wave of Decentralized Finance (DeFi), U.S. institutional Liquidity Providers (LPs) have emerged as the core incremental capital for leading Decentralized Exchanges (DEXs). However, the adaptation gap between Uniswap V4's concentrated liquidity mechanism and institutional risk preferences, as well as regulatory compliance requirements, has hindered their market entry. This study focuses on the integration of "technical characteristics - institutional constraints - precise pricing" and constructs a machine learning pricing model optimized across three dimensions: return, risk, and compliance. By integrating Uniswap V4 on-chain data, institutional risk preference data, and market data, a Stacking ensemble architecture combining LightGBM and CNN-LSTM is designed, incorporating 22 core features to achieve precise pricing. Empirical results show that the model's Mean Absolute Error (MAE) on the test set was reduced by 37% compared to the benchmark, and the Root Mean Square Error (RMSE) is reduced by 42%. The Sharpe ratio reaches 1.87 (an increase of 62% compared to the benchmark), with a volatility of 15.3% and a compliance adaptability score of 91. In the case study, a $150 million liquidity supply achieved a 19.7% annualized return and an 8.3% maximum drawdown, successfully passing SEC compliance review. This research fills the gap in institution-oriented pricing models for V4, improves the institutional extension of Automated Market Maker (AMM) pricing theory, and provides a risk-controllable and compliance-adaptable pricing tool for U.S. institutions participating in DeFi, promoting the transformation of the DeFi ecosystem towards standardization and institutionalization. By aligning the V4 Hook mechanism with U.S. regulatory frameworks, this research provides a scalable technical standard for institutional DeFi adoption, reinforcing the competitive advantage of the U.S. Web3 financial ecosystem.
Aijie Shu, Wenbin Wu, Gbenga Ibikunle, Fengxiang He
Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thus, a shock to one token may result in significant and uncontrolled contagion effects. As the DeFi ecosystem becomes increasingly linked with traditional financial infrastructure through instruments, such as stablecoins, the risk posed by this dynamic demands more powerful quantification tools. We introduce DeXposure-FM, the first time-series, graph foundation model for measuring and forecasting inter-protocol credit exposure on DeFi networks, to the best of our knowledge. Employing a graph-tabular encoder, with pre-trained weight initialization, and multiple task-specific heads, DeXposure-FM is trained on the DeXposure dataset that has 43.7 million data entries, across 4,300+ protocols on 602 blockchains, covering 24,300+ unique tokens. The training is operationalized for credit-exposure forecasting, predicting the joint dynamics of (1) protocol-level flows, and (2) the topology and weights of credit-exposure links. The DeXposure-FM is empirically validated on two machine learning benchmarks; it consistently outperforms the state-of-the-art approaches, including a graph foundation model and temporal graph neural networks. DeXposure-FM further produces financial economics tools that support macroprudential monitoring and scenario-based DeFi stress testing, by enabling protocol-level systemic-importance scores, sector-level spillover and concentration measures via a forecast-then-measure pipeline. Empirical verification fully supports our financial economics tools. The model and code have been publicly available. Model: https://huggingface.co/EVIEHub/DeXposure-FM. Code: https://github.com/EVIEHub/DeXposure-FM.
This dissertation investigates the economic and behavioral foundations of decentralized finance (DeFi) where lending, borrowing, and rate discovery are executed by smart contracts rather than traditional financial institutions. Through three complementary essays, it analyzes the design of decentralized credit protocols, the formation of interest rates in decentralized markets, and the fundamental and behavioral drivers of DeFi token valuation.The first essay examines the Atlendis protocol, which enables non- or partially-collateralized lending by combining off-chain underwriting with on-chain execution. The second develops a theoretical model of decentralized rate discovery based on a multi-unit game framework, identifying the conditions for efficiency and the frictions specific to these markets. The third provides an empirical analysis of DeFi token returns, showing that investor sentiment, liquidity dynamics, and behavioral factors play a significant role in price formation alongside economic fundamentals.By bridging financial engineering, theoretical modeling, and empirical research, this thesis sheds light on how DeFi reshapes intermediation, price formation, and governance in a transparent, programmable financial environment.