Decentralized Finance (DeFi) enables financial services to operate without centralized intermediaries, using smart contracts and blockchain consensus to ensure transparency and trust minimization. While DeFi protocols like Aave and MakerDAO use overcollateralization to mitigate credit risk, this approach creates capital inefficiencies and limits access to borrowers lacking on-chain assets. This paper introduces Inverum, a novel DeFi lending protocol designed to support undercollateralized loans for Web3 businesses and Decentralized Autonomous Organizations (DAOs). Inverum integrates on-chain credit scoring via soulbound tokens, decentralized liquidity pools, and governance-driven incentives to enable trustless, reputation-based lending. The protocol offers a fully composable framework for exploring undercollateralized lending without relying on traditional identity or off-chain reputation systems, contributing a research-ready model for future experimentation and protocol design.
Luca Pennella, Pietro Saggese, Fabio Pinelli, Letterio Galletta
Decentralized Finance (DeFi) applications introduce novel financial instruments replicating and extending traditional ones through blockchain-based smart contracts. Among these applications, DeFi derivatives protocols enable the creation and trading of decentralized derivative instruments whose value depends on underlying cryptoassets, indices, or other reference variables. Despite their growing significance, however, they remain relatively understudied compared to other DeFi protocols, such as lending protocols and decentralized exchanges. This paper systematically analyzes DeFi derivatives protocols, categorized into perpetuals, options, and synthetics, with the aim of comparing their instrument structures, protocol mechanisms, operational dynamics, and economic agents. We provide a formal characterization of the main classes of decentralized derivative instruments and develop a protocol-agnostic framework that connects instrument-level specifications, market-state variables, and protocol-level mechanisms. We complement the analytical framework with numerical simulations that evaluate how derivative positions evolve under varying economic conditions, including changes in underlying asset prices, volatility, protocol-specific fees, and leverage. Overall, this study provides a structured analytical framework for understanding and comparing the design and functioning of decentralized finance derivatives protocols.
ABSTRACT This study investigates how overconfident cryptocurrency traders influence the connection between returns and risk premia, proxied by option‐adjusted credit spreads. Using daily data from January 2021 to February 2025, we uncover asymmetry and state dependence: returns decline when spreads widen, particularly during crashes, yet they do not recover when spreads narrow. Equity indices exhibit more balanced co‐movements. The asymmetry strengthens in high‐volatility periods and persists after we control for broad market returns and after we substitute a composite crypto index for individual cryptocurrencies. These findings indicate a distinctive pricing mechanism in cryptocurrency markets shaped by overconfident behaviour and credit‐spread dynamics.
Pietro Saggese, Michael Fröwis, Stefan Kitzler, Bernhard Haslhofer · 5 authors
Total Value Locked (TVL) aims to measure the aggregate value of cryptoassets deposited in Decentralized Finance (DeFi) protocols. Although blockchain data is public, the way TVL is computed is not well understood. In practice, its calculation on major TVL aggregators relies on self-reports from community members and lacks standardization, making it difficult to verify published figures independently. We thus conduct a systematic study on 939 DeFi projects deployed in Ethereum. We study the methodologies used to compute TVL, examine factors hindering verifiability, and ultimately propose standardization attempts in the field. We find that 10.5% of the protocols rely on external servers; 68 methods alternative to standard balance queries exist, although their use decreased over time; and 240 equal balance queries are repeated on multiple protocols. These findings indicate limits to verifiability and transparency. We thus introduce ``verifiable Total Value Locked'' (vTVL), a metric measuring the TVL that can be verified relying solely on on-chain data and standard balance queries. A case study on 400 protocols shows that our estimations align with published figures for 46.5% of protocols. Informed by these findings, we discuss design guidelines that could facilitate a more verifiable, standardized, and explainable TVL computation.
Maninder Singh, William Bjorndahl, Gagangeet Singh Aujla, Joseph Camp
In the era of continuously increasing demand for bandwidth and revolutionary wireless technologies, efficient spectrum management is essential. This paper proposes a novel multi-tier tokenization approach for dynamic spectrum management. Leveraging the concept of heterogeneous tokenization of spectrum bands, we develop a decentralized framework based on blockchain technology that enables the sharing of spectrum among users. The spectrum space is represented by multi-planes, the first plane consists of unique spectrum bands converted into NFTs for long-term allocations, while the second plane involves subdividing these NFT spectrum bands for short-term usage by retail users through fungible tokens. The fungible tokens are dynamically traded and mapped using particle swarm optimization (PSO) to manage demand and supply. The paper presents formal models of the involved entities and algorithms for creating multi-tier tokens, dynamic token trading and demand-supply mapping using PSO. To enhance privacy, a zero-knowledge proof (ZKP) based approach is employed for user authentication. The proposed framework offers a secure, transparent, and scalable solution for spectrum management, addressing the limitations of traditional centralized approaches. Simulation results demonstrate the effectiveness of the framework in dynamic spectrum access, while providing privacy-aware and scalable solutions suitable for future wireless networks, including 6G.
The rapid evolution of Decentralized Finance (DeFi) has introduced innovative financial services, offering accessibility, efficiency, and transparency. However, the integration of DeFi into global capital markets presents systemic risks, including liquidity shocks, smart contract vulnerabilities, and regulatory arbitrage. This review explores the intersection of DeFi protocols with systemic risk frameworks to enhance capital market stability and regulatory oversight. By analyzing risk assessment methodologies, stress-testing mechanisms, and governance models, the study highlights strategies for mitigating financial contagion and ensuring market resilience. Furthermore, it examines regulatory approaches, such as real-time compliance monitoring and cross-border coordination, to bridge the gap between decentralized ecosystems and traditional financial regulations. Through case studies and empirical data, this paper underscores the importance of integrating robust risk frameworks with DeFi innovations to foster sustainable financial markets. The findings contribute to ongoing discussions on balancing financial innovation with risk management, providing insights for policymakers, regulators, and industry stakeholders navigating the evolving landscape of digital finance.
Effective risk management solutions become absolutely crucial when financial markets embrace distributed technology and decentralized financing (DeFi). This study offers a thorough survey and comparative analysis of the integration of artificial intelligence (AI) in risk management for distributed arbitrage systems. We examine several modern caching techniques namely in memory caching, distributed caching, and proxy caching and their functions in enhancing performance in decentralized settings. Through literature review we examine the utilization of AI techniques for alleviating risks related to market volatility, liquidity challenges, operational failures, regulatory compliance, and security threats. This comparison research evaluates various case studies from prominent DeFi technologies, emphasizing critical performance metrics like latency reduction, load balancing, and system resilience. Additionally, we examine the problems and trade offs associated with these technologies, emphasizing their effects on consistency, scalability, and fault tolerance. By meticulously analyzing real world applications, specifically centering on the Aave platform as our principal case study, we illustrate how the purposeful amalgamation of AI with contemporary caching methodologies has revolutionized risk management in distributed arbitrage systems.
This study presents a comprehensive systematic review of Artificial Intelligence (AI) applications in DecentralizedFinance (DeFi), emphasizing AI’s pivotal role in mitigating the vulnerabilities and operational complexities inherentin permissionless financial systems. By systematically analyzing 39 peer-reviewed studies from major scholarlydatabases, the review identifies five dominant application domains: fraud detection, smart contract security, marketprediction, credit risk assessment, and decentralized governance. It examines the diverse range of AI methodsspanning machine learning, deep learning, graph neural networks, and reinforcement learning—and evaluates theircomparative performance and limitations. The findings reveal that AI not only enhances DeFi’s transparency, trust,and efficiency but also underpins emerging capabilities such as autonomous governance and adaptive marketmechanisms. Persistent challenges including data scarcity, cross-chain generalization, interpretability, andscalability—underscore the need for robust, explainable, and ethical AI solutions. The review concludes that AIconstitutes a foundational enabler for secure, transparent, and resilient decentralized financial ecosystems, andoutlines critical future research directions for integrating trustworthy intelligence into the evolving DeFi landscape. (PDF) A Systematic Review on the Application of Artificial Intelligence in Decentralized Finance. Available from: https://www.researchgate.net/publication/397514996_A_Systematic_Review_on_the_Application_of_Artificial_Intelligence_in_Decentralized_Finance [accessed Nov 11 2025].
Decentralized finance (DeFi) has revolutionized the financial landscape, with protocols like Uniswap offering innovative automated market-making mechanisms. This article explores the development of a backtesting framework specifically tailored for concentrated liquidity market makers (CLMM). The focus is on leveraging the liquidity distribution approximated using a parametric model, to estimate the rewards within liquidity pools. The article details the design, implementation, and insights derived from this novel approach to backtesting within the context of Uniswap V3. The developed backtester was successfully utilized to assess reward levels across several pools using historical data from 2023 (pools Uniswap v3 for pairs of altcoins, stablecoins and USDC/ETH with different fee levels). Moreover, the error in modeling the level of rewards for the period under review for each pool was less than 1%. This demonstrated the effectiveness of the backtester in quantifying liquidity pool rewards and its potential in estimating LP's revenues as part of the pool rewards, as focus of our next research. The backtester serves as a tool to simulate trading strategies and liquidity provision scenarios, providing a quantitative assessment of potential returns for liquidity providers (LP). By incorporating statistical tools to mirror CLMM pool liquidity dynamics, this framework can be further leveraged for strategy enhancement and risk evaluation for LPs operating within decentralized exchanges. • Develop a methodology for backtesting liquidity provision in a CFMM. • Enhance CFMM backtesting by leveraging GPU acceleration for faster computation. • Showcase the practicality of CFMM backtesting using actual Uniswap pool data.
Rik Ghosh, Samrat Gupta, Arka Datta, Abhimanyu Nag · 5 authors
Compound Finance is a decentralized lending protocol that enables the secure and efficient borrowing and lending of cryptocurrencies, utilizing smart contracts and dynamic interest rates based on supply and demand to facilitate transactions. The protocol enables users to supply different crypto assets and accrue interest, while borrowers can avail themselves of loans secured by collateralized assets. Our collaboration with Compound Finance focuses on harnessing the power of the Chainrisk simulation engine to optimize risk parameters of the Compound V3 (Comet) protocol. This report delineates a comprehensive methodology aimed at calculating key risk metrics of the protocol. This optimization framework is pivotal for mitigating systemic risks and enhancing the overall stability of the protocol. By leveraging Chainrisk's Cloud Platform, we conduct millions of simulations to evaluate the protocol's Value at Risk (VaR) and Liquidations at Risk (LaR), ultimately providing recommendations for parameter adjustments.
In decentralized finance, any individual can pool their assets into an automated market maker (AMM) -- herein we focus on the constant product market maker (CPMM) -- in exchange for a claim on a fraction of future pool assets and fees earned from the market making operations. This position is represented by a liquidity token, whose prevailing on-chain price is effectively the initial deposited assets. Though this price is well-defined, we treat the liquidity token as a derivative position in the prices of the underlying assets for the CPMM in order to deduce risk-neutral pricing and hedging formulas, not dissimilar to the Black-Scholes result. Adopting this perspective, in a frictionless environment, hedging the CPMM liquidity token under fair valuation should produce a riskless process, which therefore grows at the risk-free rate, something that is not seen in empirical case studies under the prevailing price. With our novel pricing formula, we construct a method to calibrate a volatility to data which provides an updated (non-market) valuation which is consistent with the (near-continuous) replication strategy out-of-sample. We conclude with a discussion of novel AMM design considerations motivated by this derivative-pricing perspective.
Related party transactions (RPTs) can serve as channels for the spread of credit risk events among blockchain firms. However, current credit risk-assessment models typically only consider a firm’s individual characteristics, overlooking the impact of related parties in the blockchain. We suggest incorporating RPT network analysis to improve credit risk evaluation. Our approach begins by representing an RPT network using a weighted adjacency matrix. We then apply DANE, a deep network embedding algorithm, to generate condensed vector representations of the firms within the network. These representations are subsequently used as inputs for credit risk-evaluation models to predict the default distance. Following this, we employ SHAP (Shapley Additive Explanations) to analyze how the network information contributes to the prediction. Lastly, this study demonstrates the enhancing effect of using DANE-based integrated features in credit risk assessment.
The emergence of decentralized finance (DeFi) has transformed traditional financial systems by leveraging blockchain technology to offer decentralized solutions for trading and liquidity provision. Within the CORE Chain ecosystem, the COREDAO VIP token plays a pivotal role in facilitating liquidity pooling and automated trading across various COREDAO-based tokens. This research investigates the impact of COREDAO VIP token within decentralized exchanges (DEX) such as ICECREAMSWAP, LFGSWAP, SHADOWSWAP, and ARCHERSWAP. By analyzing its integration into these platforms, the study explores how COREDAOVIP enhances liquidity management, reduces slippage, and supports automated trading strategies. Key aspects examined include the token's utility, governance implications, and its influence on trading dynamics within the COREDAO ecosystem. Through comprehensive analysis and empirical insights, this research aims to provide a nuanced understanding of COREDAOVIP token's role in advancing decentralized finance practices and its implications for future blockchain-based financial ecosystems.
Togzhan Barakbayeva, Soroush Farokhnia, Amir Kafshdar Goharshady, Markus Gufler · 5 authors
Cardano is a blockchain protocol based on proof-of-stake and an extended UTXO model which also supportsarbitrary smart contracts. Its primary currency, Ada, is cur-rently one of the global top ten cryptocurrencies with amarket cap of more than 16 billion USD. In Cardano, newblocks are produced by stake pools. Any holder of Ada candelegate their stake to a pool. The underlying proof-of-stakeconsensus protocol is Ouroboros Praos, which divides time intoa number of epochs and each epoch into a number of slots, eachcorresponding to one second. In each slot, leaders are randomlyselected to produce and add new blocks to the blockchain, withtheir selection probability being proportional to their stake.Each block can contain a sequence of transactions and blockproduction is rewarded in two ways: (i) transaction fees and(ii) monetary expansion. The producers have no control over(ii), but can optimize (i) by choosing which transactions toinclude in their blocks. Thus, they are incentivized to maximizethe total transaction fees.In this work, we consider the natural optimization problemof forming a block with maximum transaction fees givena set of unmined Cardano transactions. We show that byexploiting the sparsity of interrelations between transactions,i.e. the small treedepth of dependency-conflict graphs, it ispossible to obtain a polynomial-time algorithm that outputsoptimal blocks. We implemented our algorithm in a freeand open-source tool called Pixiu. Using Pixiu, we provideextensive experimental results over real-world transaction dataon the Cardano blockchain demonstrating that our approachincreases the block producers’ revenue by almost 1,357.82USD/day = 495,604.3 USD/year.
Decentralized Exchanges are becoming even more predominant in today's finance. Driven by the need to study this phenomenon from an academic perspective, the SIAG/FME Code Quest 2023 was announced. Specifically, participating teams were asked to implement, in Python, the basic functions of an Automated Market Maker and a liquidity provision strategy in an Automated Market Maker to minimize the Conditional Value at Risk, a critical measure of investment risk. As the competition's winning team, we highlight our approach in this work. In particular, as the dependence of the final return on the initial wealth distribution is highly non-linear, we cannot use standard ad-hoc approaches. Additionally, classical minimization techniques would require a significant computational load due to the cost of the target function. For these reasons, we propose a three-step approach. In the first step, the target function is approximated by a Kernel Ridge Regression. Then, the approximating function is minimized. In the final step, the previously discovered minimum is utilized as the starting point for directly optimizing the desired target function. By using this procedure, we can both reduce the computational complexity and increase the accuracy of the solution. Finally, the overall computational load is further reduced thanks to an algorithmic trick concerning the returns simulation and the usage of Cython.
The development of digital technologies that increase the security of payments and improve settle-ments is one of the main tasks facing regulators in the financial sector. The main reason for the in-creased attention to distributed ledger technology in the financial sector is the expectation that it will eliminate a number of problems and limitations inherent in the currently used methods of storing, ac-counting and transmitting financial information. The authors consider the advantages of the distribut-ed ledger technology in the implementation of financial transactions
Abstract Although extensive research has examined the credit risk of real estate enterprises, the relationship between the political connection of real estate enterprises and these enterprises’ credit risk has not been formally studied. Using the panel data of 123 real estate listed companies in the Chinese stock market from 2008 to 2021, this paper finds a significant positive correlation between the political connection of private real estate listed companies and their credit risk. This phenomenon is attributed to the excessive debt that benefits from political connections since it may raise the credit risk of any real estate firm. Interestingly, considering that 2013 is the first year of China’s Internet finance era, we find that the popularity of Internet finance and other decentralized lending financing channels may enhance the impact of political connections on real estate credit risk. Our findings provide new micro evidence for the influencing factors and mechanism of credit risk of real estate enterprises during the recent “credit crisis” in the real estate market in China.
The conventional process of credit document verification heavily relies on manual methods, making it tedious and time-consuming. The advent of self-sovereign identity (SSI) revolutionised the landscape of credit document verification. SSI empowers individuals with complete control over their identity, ensuring privacy, trust, and security. This paper presents an in-depth exploration of SSI's application in the credit processing domain. This paper highlights the implementation of SSI using the Trust over IP framework on Hyperledger Aries, empowering borrowers to own and control the sharing of their verifiable credentials. By integrating Hyperledger Aries and SSI, a robust and interoperable blockchain-based identity framework can be built. This allows individuals to store their verifiable credentials on a distributed ledger securely and selectively disclose them to lenders as needed. This model empowers borrowers to present accurate and tamper-proof credentials, enhancing data privacy, transparency, and trust, while promoting a borrower-centric approach to sharing credentials.
This paper introduces a novel framework for rate discovery in de-centralized finance (DeFi), focusing on the unique challenges andopportunities within decentralized lending platforms. We explorethe mechanisms of interest rate formation in a decentralized en-vironment, free from traditional banking institutions’ control. Byleveraging lending pool dynamics, we propose a method that inte-grates borrowers’ risk profiles with market liquidity conditions todetermine fair borrowing rates without third party involvment. Ourmodel aims to offer a transparent and reliable solution for rate dis-covery in DeFi. Through a series of simulations, we demonstratethe potential of our framework to improve lending practices in theDeFi ecosystem, making it a viable and competitive alternative toconventional financial systems. The findings suggest that our ap-proach not only enhances the transparency and fairness of the lend-ing process but also encourages a more informed participation oflenders and borrowers, ultimately contributing to the stability andgrowth of the DeFi market.
To understand the disruption and implications of distributed ledger technologies for financial reporting and auditing, we analyze firm misreporting, auditor monitoring and competition, and regulatory policy in a unified model. A federated blockchain for financial reporting and auditing can improve verification efficiency not only for transactions in private databases but also for cross-chain verifications through privacy-preserving computation protocols. Despite the potential benefit of blockchains, private incentives for firms and first-mover advantages for auditors can create inefficient under-adoption or partial adoption that favors larger auditors. Although a regulator can help coordinate the adoption of technology, endogenous choice of transaction partners by firms can still lead to adoption failure. Our model also provides an initial framework for further studies of the costs and implications of the use of distributed ledgers and secure multiparty computation in financial reporting, including the positive spillover to discretionary auditing and who should bear the cost of adoption. This paper was accepted by David Simchi-Levi, finance. Funding: The authors gratefully acknowledge research support from the FinTech Laboratory at J. Mack Robinson College of Business at Georgia State University, the Center for Research in Security Prices at the University of Chicago, the Ripple University Blockchain Research Initiative, and the Smith AI Initiative for Capital Market Research at the University of Maryland. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.02577 .